SIIT Commemorative Publication (10 Years of International ...

!" Years of International CooperationFederation of Thai IndustriesNippon KeidanrenThammasat University!!!!!!!!!SIIT Commemorative Publicationหนังสือที่ระลึกความรวมมือระหวางสภาอุตสาหกรรมแหงประเทศไทย สหพันธเศรษฐกิจแหงประเทศญี่ปุนและมหาวิทยาลัยธรรมศาสตร!!!!!!!!!!สถาบันเทคโนโลยีนานาชาติสิรินธรมหาวิทยาลัยธรรมศาสตรSirindhorn International Institute of TechnologyThammasat Universityสถาบันเทคโนโลยีนานาชาติสิรินธรมหาวิทยาลัยธรรมศาสตร#$%&'()*+),%#""#%วันที่ ๒๗ พฤศจิกายน ๒๕๔๕

First Honorary Doctoral Degree of Science in Information Technologyfrom SIIT, Thammasat UniversityOn 31 July, 1996, His Majesty King Bhumibol Adulyadej of Thailand andHer Royal Highness Princess Maha Chakri Sirindhorn graciously presided over theThammasat University Graduation Ceremony, in which the Thammasat UniversityCouncil humbly appealed to present His Majesty the King the first HonoraryDoctoral Degree of Science in Information Technology (IT) fromthe Sirindhorn International Institute of Technology (SIIT).

1993: Her Royal HighnessPrincess Maha ChakriSirindhorn graciously laidthe cornerstone of theInstitute’s main building.1997: Her Royal Highness Princess Maha Chakri Sirindhorn graciously presided overthe inauguration of the Institute’s new name“Sirindhorn International Institute of Technology”.

March 9, 2001January 8, 2002Her Royal Highness Princess Maha Chakri Sirindhorn graciously granted an audience to the Chairman ofthe SIIT Board of Trustees and the Rector of Thammasat University, Associate Professor Dr. NarisChaiyasoot, who led the team of SIIT trustees, SIIT Director and senior executives, to report on thedevelopment of SIIT with the supportive cooperation of Thammasat University, the Federation of ThaiIndustries (FTI), and Nippon Keidanren (The Japanese Business Federation). On these occasions, thePrincess was presented with donations for Her Royal Highness’s foundations.

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!""#$%&'())*(+,$,-#.#$/(01(!2,%#.&2(340-4,.5!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49:++4#0);;%;)'&-.?/.@&-.),5#A""ASIIT Director!!!!!!!"!#!$!!!!"!#!$!6#,%(;( ( C( 9$F&40$.#$/,D(7#2=$0D0-H(340-4,.()9(( ( C( )$%E5/4&,D(9$-&$##4&$-(340-4,.():( ( C( )$5/4E.#$/,/&0$(,$%(:0$/40D(8H5/#.5(340-4,.()7(( ( C( )$104.,/&0$(7#2=$0D0-H(340-4,.(+9((( C( +#2=,$&2,D(9$-&$##4&$-(340-4,.(+7((( C( +,$,-#.#$/(7#2=$0D0-H(340-4,.(7:( ( C( 7#D#20..E$&2,/&0$5(340-4,.(97?( C( 9$-D&5=(74,&$&$-(?$&/(@8?(( C( @#$#4,D(8/E%&#5(?$&/!"#$$$!6)

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

! !"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A!!""#$%&'()*+(,))-(.#/(0#123145$6#()$%&657318(!65%#4&6(9#518( :;;??:( @5$A# B=( K:( QS( (34N:#O#4%@C,.@&/#:-&**#B%;?%'(# 6J# 6J# Q!LM# #E-H,(T(!%4&$&87157&J#(,7522(U#4V#18( >?OQ( >?O;( >SOQ( =G:=(( ( ( ( (,F""317&$A(E56&D&7&#8+(( ( ( ( (R))S(#T#U)>',&/(#:1%,8.,H#O#34N:5#R&C-# 6J# A5AAV# K5PVV# M""E!5"""#-375D(W&V151/(E56&D&7/(,"#$%&$A(T(E-H,L(X5P7( N!( =LQ=:( QLS:?( (:L

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#SIIT Advanced Laboratory Building1996: The Cornerstone LayingCeremony of SIIT advancedlaboratory building was presidedover by His Excellency AnandPanyarachun.###1998: Advanced Laboratory Building Handing Over CeremonyFrom Right: Assoc. Prof. Dr. Naris Chaiyasoot, Chairman ofthe SIIT Board of Trustees and Rector of ThammasatUniversity; Mr. Masatake Kusamichi, Chairman of JapanThailand Trade & Economic Committee, Keidanren;Mr. Praphad Phodhivorakhun, Vice Chairman of FTI; andProf. Dr. Prida Wibulswas, Director of SIIT.1999: Inauguration of SIIT Advanced Laboratory Building presided over by Mr. Paron Israsena.!!"#

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""ASIIT at Bangkadi2000: The Cornerstone Laying Ceremony of New SIITBuilding at Bangkadi, Pathumthani, was presided over byThanpuying Niramol Suriyasat.2002: Opening Ceremony of SIIT at Bangkadi, presided overby His Excellency Anand Panyarachun.!!""#

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#Visits of Keidanren and FTIMay 26, 1995Visit of Keidanren MissionOctober 2, 1997: Toyota ThailandFoundation donated a Toyota SolunaCar to SIITFebruary 23, 1999SIIT Board of Trustees MeetingOctober 6, 1998: Visit of KeidanrenMission and FTI DelegationSeptember 11, 2000Mr. Togo Fund Presentation CeremonyMarch 30, 2002: Visit of Keidanren Mission!!"""#

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""AInternational Cooperation1995: University of Tokyo, Japan.!BBC2#0%,-'&/#D%(%&'@E#+,(-.->-%#)*#F/%@-'.@#=)G%'#+,8>(-'H#I0D+F=+J5#K&1&,L#1998: Saitama University, Japan.1998: University College London (UCL), UK.1999: Nottingham University, U.K.1998: University of Tokyo, Japan.1999: University of Strathclyde, U.K.2000: Kochi University ofTechnology, Japan.!!"#$

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#International Cooperation2000: Toyohashi Universityof Technology, Japan2000: Helsinki Polytechnic,Finland2000: Vaasa Polytechnic,Finland2001: University of AppliedSciences at Ravensburg-Weingarten, Germany2001: University of Manchester Institute ofScience and Technology (UMIST), U.K.2002: Technical University of Munich,Germany2002: Nanyang TechnologicalUniversity, Singapore2002: University of Melbourne,Australia!!#$

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#Industrial Cooperation and Research Funding AgenciesResearch Funding AgenciesEngineering/TechnologyTraining in Japan at 19 leadingcompanies duringMay 20 – June 7, 2002.MMC Sittipol Co., Ltd. signed acooperation agreement with SIITon conducting on-road tests ofMitsubishi’s “Lancer Cedia”,March 2002.SIIT Research Team conductedon-road tests of Toyota “HiluxTiger D4D 3000” for ToyotaMotor Thailand Co., Ltd.Asian Institute of TechnologyASK Corporation, JapanBangkadi Industrial ParkBangkok Metropolitan AdministrationCCVK Joint VentureChichibu Onoda Cement Corporation, JapanConcrete Products & Aggregate Co., Ltd.Department of Note Printing Works, The Bank of ThailandEEC-Energetics Co., Ltd.Electricity Generating Authority of Thailand (EGAT)Energetics Co., Ltd.Institute of Thai Studies, Thammasat UniversityInternational Atomic Energy Agency (IAEA)Japan International Cooperation Agency (JICA)Joint Graduate School of Energy & Environment (JGSEE)Mahaphant Fibre-Cement (Public) Co., Ltd.MDX Lao Co., Ltd.MMC Sittipol Co., Ltd.National Electronics and Computer Technology Center (NECTEC)National Energy Policy Office (NEPO)National Housing Authority of Thailand (NHA)National Research Council of Thailand (NRCT)National Science and Technology Development Agency (NSTDA)Nynex Science & Technology Asia, Ltd.Obayashi Co., Ltd.Obayashi Corporation, JapanQuality Team Consultant Co., Ltd.Siam Yamato Steel Co., Ltd.Sumitomo Heavy Industries, JapanSwedish International Development Agency (SIDA)Thai Acrylic Fibre Co., Ltd.Thaikhadi Research Institute, TUThailand Environment InstituteThe Thailand Research Fund (TRF)Thammasat UniversityTokyo Electric Power Company, JapanToyota Motor Thailand Co., Ltd.TPI Concrete Co., Ltd.Unique Engineering & Construction Co., Ltd.University of Tokyo, Japan!!"#$

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#Scholarships ! Awards!!"##$

!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""ADonors of Scholarships forUndergraduate Students1 Bangkok Cable Co., Ltd.2 Bank of Ayudhya Public Co., Ltd.3 Caltex Oil (Thailand) Ltd.4 Hitachi-Bangkok Cable Co., Ltd.5 Isuzu Engine Manufacturing (Thailand) Co., Ltd.6 Isuzu Motors Co., (Thailand) Ltd.7 Keidanren8 Lucent Technologies9 MMC Sittipol Co., Ltd.10 National Science and Technology DevelopmentAgency11 National Thailand Co., Ltd.12 Seagate Technology (Thailand) Ltd.13 Srithanathep Company Limited14 Superlite Trading Co., Ltd.15 T. N. Incorporated Ltd.16 Teijin Polyester (Thailand) Ltd.17 Thai Farmers Bank Public Co., Ltd.18 Thai Military Bank Public Co., Ltd.19 Thai Obayashi Corp. Ltd.20 Thai Olefins Co., Ltd.21 Thai Radiator Manufacturing Co., Ltd.22 Thai Sa-ngoanwanich 2489 Co., Ltd.23 Thailand Carpet Manufacturing Public Co., Ltd.24 The National Council on Social Welfare ofThailand under Royal Patronage25 The Professor Dr. Adul Wichiencharoen andHis Former Students' Foundation26 The Sanwa Bank Foundation27 The Siam Cement Foundation28 The Siam Cement Public Co., Ltd.29 Toshiba Thailand Co., Ltd.30 Toyota Motor Thailand Co., Ltd.31 TPI Polene Public Co., Ltd.32 United Communication Industry Public Co., Ltd.33 Thanpuying Niramol Suriyasat34 Dr. Vipan Rerngpittaya35 Mr. Yukiyasu Togo36 Mrs. Kanjanee WibulswasHost Companies of SIIT Students!"Engineering/Technology Training inJapan,Academic Years" 1998-20011 Ajinomoto Co., Inc.2 Asahi Glass Finetechno Co., Ltd.3 Daikin Industries, Ltd.4 Fuji Xerox Co., Ltd.5 Fujikura Ltd.6 Honda Motor Co., Ltd.7 Ishikawajima Harima Heavy Industries Co., Ltd.8 Isuzu Motors, Ltd.9 Kajima Corporation10 Komatsu, Ltd.11 Maeda Corporation12 Matsushita Electric Industrial Co., Ltd.13 Mitsubishi Electric Corporation14 Nikon Co., Ltd.15 Nippon Koei Co., Ltd.16 Nishimatsu Construction Co., Ltd.17 NKK Corporation18 NTT Communications Corporation19 Obayashi Corporation20 Oki Data Corporation21 Oki Electric Industry Co., Ltd.22 SANYO Electric Co., Ltd.23 Seiko Epson Corporation24 Sharp Corporation25 Teijin Limited26 Tokyo Electric Power Company27 Toray Industries, Inc.28 Toshiba Corporation29 Toyota Motor Corporation!!"###$

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!"#$%&'(#)*#+,-%',&-.),&/#0))1%'&-.),2#34+5#6.11),#7%.8&,'%,#&,8#49#:++4#0);;%;)'&-.?/.@&-.),5#A""A#Activity Time!!!"

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Finite Element Method as a Tool to Evaluate Damaged ExistingReinforced Concrete Structuresวิธีไฟไนตเอลิเมนตเพื่อเปนเครื่องมือสําหรับการวิเคราะหโครงสรางคอนกรีตเสริมเหล็กที่มีอยูและไดรับความเสียหายAmorn PimanmasBuilding Facilities Engineering ProgramSirindhorn International Institute of TechnologyP.O. Box 22, Thammasat-Rangsit Post Office, Pathum Thani 12121, Thailandอมร พิมานมาศสาขาวิศวกรรมระบบอาคาร สถาบันเทคโนโลยีนานาชาติสิรินธรตู ปณ. 22 ปทฝ. ธรรมศาสตร รังสิต ปทุมธานี 12121Abstract: In Thailand, the budget for repairing and upgrading existing structures has recently become comparablewith that for new construction. The direction of civil engineering will put more emphasis on repair, protection,inspection, maintenance and strengthening of old deteriorating structures. Many existing reinforced concretestructures in current service may be damaged by pre-cracking due to several factors such as drying shrinkage,temperature effect, heat of hydration, etc. Moreover, during the service life, a structure may be subject tounexpected loading as well as earthquakes. Recently, the author conducted an experiment and found that thebehavior of pre-cracked concrete members differs considerably from perfect un-cracked members. Hence, amultitude of past knowledge mainly obtained from laboratory tests of perfect non-damaged members may not bedirectly applicable to real structures with cracking damage. This paper presents the nonlinear finite element analysisas a tool to simulate the behavior of pre-cracked members in computer. The understanding of the behavior ofdamaged RC members will give useful information for the engineer to select the proper choice of repair,strengthening or demolition and reconstruction of the structures.บทคัดยอ: ในประเทศไทย งบประมาณที่ใชในการซอมแซมและการปรับปรุงโครงสรางที่มีอยูคิดเปนจํานวนเงินไมนอยเมื่อเทียบกับงบประมาณสําหรับการกอสรางใหม ทิศทางใหมของวิศวกรรมโยธาจะเนนไปที่การซอมแซม การปองกัน การตรวจสอบ การบํารุงรักษา และ การเสริมกําลังโครงสราง โครงสรางคอนกรีตที่มีอยูเปนจํานวนมากในปจจุบันมักไดรับความเสียหายจากรอยราวลวงหนาซึ่งเกิดจากสาเหตุหลายประการ เชน การหดตัวของคอนกรีตเมื่อแหง ผลของอุณหภูมิ ความรอนที่เกิดจากปฏิกิริยาไฮเดรชัน นอกจากนี้ในชวงอายุการใชงาน โครงสรางอาจตองรับน้ําหนักบรรทุกที่ไมไดพิจารณาไวในตอนออกแบบ รวมทั้งน้ําหนักบรรทุกที่เกิดจากแรงแผนดินไหว เมื่อไมนานมานี้ ผูเขียนไดทําการทดลอง และพบวา พฤติกรรมขององคอาคารคอนกรีตเสริมเหล็กที่มีรอยราวลวงหนานั้นมีความแตกตางจากพฤติกรรมขององคอาคารที่ไมมีรอยราวลวงหนาเปนอยางมาก ดังนั้น องคความรูจํานวนมากที่ไดจากการทดลององคอาคารที่สมบูรณ ปราศจากรอยราว ในหองปฏิบัติการ จึงไมสามารถนําไปประยุกตใชเพื่ออธิบายพฤติกรรมขององคอาคารที่มีรอยราวลวงหนาไดโดยตรง บทความนี้เสนอวิธีไฟไนตอิลีเมนตที่ใชแบบจําลองพฤติกรรมวัสดุแบบไมเชิงเสนในการจําลองพฤติกรรมขององคอาคารที่มีรอยราวลวงหนาในคอมพิวเตอร ความเขาใจในพฤติกรรมขององคอาคารคอนกรีตเสริมเหล็กที่มีรอยราวลวงหนาจะเปนขอมูลที่เปนประโยชนตอวิศวกรเพื่อการตัดสินใจทางเลือกที่เหมาะสมวาจะซอมแซม และ เสริมกําลังโครงสรางเกา หรือ จะรื้อทิ้งแลวกอสรางใหมKeywords: Pre-cracked RC members, Damaged RC member, Finite element, Elastic models, Non-linear model.1. IntroductionIn several developed countries such as Japan, USA andsome European countries, the prosperous era of newconstructions in civil engineering has passed. Thebudgets for repairing and upgrading structures builtseveral decades ago have increased considerably inrecent years. In Thailand, though some new structuresare being constructed, many structures in currentservice are subject to deterioration. These structures are1

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002in urgent need of repair, retrofit or strengthening if theowner wishes to prolong their life. Certainly thesestructures can be demolished and rebuilt but there maynot be sufficient funds for that.Reinforced concrete structures are durable but noteternal materials. Generally, RC structures aredesigned to stay for 50 to 100 years. But the actual lifemay be much shorter if the structures have to stay insevere environmental conditions (Fig. 1). Reinforcedconcrete structures constructed near the sea may sufferfrom corrosion of reinforcing bars. Some situated inthe industrial area may suffer from chemical attack.One of the major damages of reinforced concretemembers is cracking.Non-ProportionalLoading PathHowever, as mentioned, civil engineering will movetowards the direction of maintenance, repair andstrengthening of existing structures. Hence, we aredealing with existing structures with many cracksalready present, not new structures. Understanding thebehavior of pre-cracked structures will be valuable todecide whether to demolish old structures and constructnew ones or to repair and upgrade the old ones. Thispaper presents the advanced finite element analysisemploying non-linear material models to simulate theload-deformation response and failure of pre-crackedreinforced concrete members. The tool is not meant tothe design of new structures, but the evaluation ofexisting structures.2. Behavior of Pre-cracked ReinforcedConcrete Members2.1 RC Beams with Vertical Pre-CracksReal RCstructuresReal RCmembersRecently, the author has conducted the experiment toinvestigate the behavior of pre-cracked reinforcedconcrete members [1]. The shear failure was aimed as atarget of study. Dimension and cross sections of thebeam are shown in Fig. 2(a). To represent crackingdamages, vertical pre-cracks were introduced into thebeam by means of reversed flexural loading (Fig. 2(b)).The reversed flexural loading was conducted in twosteps. After the beam was loaded in flexure in the firststep, it was rotated by 180°, and then loaded in theFig. 1 Real reinforced concrete in real environments.For reinforced concrete members, cracking is almostinevitable. The longer the concrete structures have tostay, the more number of cracks are expected. Thereare several factors that cause cracking inside RCmembers. These include drying shrinkage, temperatureeffect, heat of hydration, etc. Moreover, the reinforcedconcrete members may be subject to unexpectedloading as well as earthquakes in some parts ofThailand.Faced with cracked structures, the engineer has todecide whether to demolish the old one and reconstructthe new one or to repair and upgrade the old one. Theappropriate choice can be selected if the behavior ofcracked structures is well understood. However, this isnot always so. It should be noted that a multitude ofreinforced concrete knowledge that we have today isthe accumulation of past researches mostly conductedin laboratory assuming perfect conditions. Underlaboratory environments, the prepared RC specimensgenerally have minimized pre-cracks. Normally, it isthe intention of researchers to prepare their specimensto be as perfect as possible to satisfy their assumptions.Because past research was conducted in the period ofbooming new constructions, the constructed theorieswere mainly directed towards design and constructionof new structures.D10@752507D132400A-AMaterial propertiesB-B350310 Concrete compressive strength= 26.5 MPaYield strength of main reinforcementA-A B-B = 338.4 MPaYield strength of shear reinforcementUnit:mm= 338.4 MPaFig. 2(a) Dimension and cross section of beams.450 4502300Unit: mmFirst stage - reversed flexureDiagonal shear crack750Vertical pre-cracksUnit: mmSecond stage - shear loadingFig. 2(b) Loading method.2

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002second flexure. After finishing the reversed flexuralloading, the beam was damaged by vertical penetratingcracks.In the second stage, shear loading was applied.Supports were moved towards beam mid-span suchthat shear span to effective depth ratio was 2.41 (Fig.2(b)). Load-displacement relationship of the precrackedbeam under shear loading is shown in Fig. 3. Itis noted that pre-cracked beams reached significantlyhigher loading capacity than the non pre-cracked one.Up to 50 % increase in loading capacity till yielding ofmain reinforcement was experimentally observed.Load (kN)300250200150Non pre-cracked beamLoad(kN)500300Yielding capacity = 233.5 kNPre-cracked beamReversed flexure100100-100 -20 0 2050-300-500Center span deflection(mm)00 5 10 15 20Center deflection (mm)Fig. 3 Load-deflection response of pre-cracked andnon pre-cracked beams.First diagonal crackseveral diagonal cracks, which formed independentlyalong the failure path.2.2 RC Beams with Inclined Pre-CrackThe beam with inclined cracking damage is studied inthis section. The beam dimension and cross section areshown in Fig. 5(a). The loading was composed of twosteps (Fig. 5(b)). The first step was to create inclinedpre-cracking damage and the second step was to applyshear loading to the damaged beam.3DB19f c = 33.4 MPaf y = 397.3 MPaρ = 1.65 %Unit : mm1800300 2602003DB19Fig. 5(a) Dimension and cross section of beams.200 275 275 200First stage - Introduction of inclined pre-crack750750PropagatesPropagatesControl beam (no pre-cracks)Unit : mma/d = 2.88Formation of Z-crackbdcFailure crack sequencea-b-c-dFig. 4 Comparison of crack patterns between pre-crackedand non pre-cracked beams.Failure crack patterns of non pre-cracked and precrackedbeams are also different from each other (seeFig. 4). In non pre-cracked beam, the failure wasgoverned by the propagation of a single diagonal crack.However, in pre-cracked beam, several discontinuousdiagonal cracks were created. The propagation ofdiagonal crack was arrested at the pre-crack plane. Dueto the presence of pre-cracks, the failure was not due tothe propagation of a single diagonal crack as in nonpre-cracked beam. Instead, the failure of the precrackedbeam was caused by the combination ofaLoad(kN)Second stage - Shear loading to damaged beam14012010080604020Fig. 5(b) Loading method.Inclinedpre-crackedbeamNon pre-crackedbeam00 2 4 6 8Center deflection(mm)Fig. 6 Comparison of load-deformation response betweeninclined pre-cracked beam and non pre-cracked beam.After inclined pre-cracks were created, the shearloading was applied to the beam. Loading arrangementfor the second stage shear loading is shown in Fig.5(b). The shear span to effective depth ratio is 2.88.3

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002The comparison of load-deflection relations betweennon pre-cracked and inclined pre-cracked beams areshown in Fig. 6. As shown in the figure, the loadingcapacity and stiffness of damaged beam weredecreased compared with the non pre-cracked one. Thefailure crack pattern of damaged beam is shown in Fig.7. The new diagonal crack was not formed. Instead,pre-crack itself was active from the beginning andtotally governed the beam behavior. This explainedwhy the inclined pre-crack accelerated the formation offailure.Most engineers have primarily used linear finiteelement analysis for the design of new structures. Butvery few employ non-linear finite element analysis as atool to evaluate the existing structures. However, asthe need to obtain information of existing structures isincreasing, the use of non-linear finite element willbecome more and more. At the present time, theJapanese design code [5] has endorsed the use of nonlinearfinite element analysis for evaluation of RCstructures damaged in the previous earthquakes.This paper presents a non-linear finite element analysisto evaluate the pre-cracked reinforced concretemembers. The intention is to show the possibility ofthis tool to simulate the behavior of pre-crackedreinforced concrete members tested in the experiments.The capabilities of non-linear finite element analysiswill be demonstrated through the correct prediction offailure mode, load-deformation response and capacityof the RC members with sufficient accuracy.4. Finite Element Analysis of Pre-CrackedBeam4.1 Beams with Vertical Pre-cracksPre-cracked condition (First shear)Shear failure (second shear)Fig. 7 Failure crack pattern of inclined pre-cracked beam.3. Finite Element Analysis as a Tool toEvaluate the Cracked StructuresThe finite element analysis is the numerical methodthat solves a set of governing partial differentialequation using computers [2, 3]. Recently, it has beenincreasingly used to analyze structures withcomplicated shape, load and boundary conditions.There are two major types of finite element analysis forstructural analysis: linear analysis and non-linearanalysis. In the linear finite element analysis, theconstitutive models or material models are linear. As aresult, it cannot deal with plasticity of reinforcementand fracture of concrete. On the other hand, the nonlinearfinite element analysis employs the non-linearconstitutive models [4]. Hence it can trace the loaddeformationresponse of structures throughout theloading history as well as the failure mode.It was shown in the previous section that the behaviorof pre-cracked beams significantly differ from the nonpre-cracked one. The capacity may decrease orincrease compared with the un-cracked beam. It wasclear that the conventional knowledge applicable toperfect un-cracked concrete could not be used toexplain the behavior of pre-cracked beam. In thissection, the non-linear finite element analysis is used tosimulate the behavior of pre-cracked reinforcedconcrete beams tested in the previous section [6].The finite element mesh of the pre-cracked beam isshown in Fig. 8. Discrete joint elements are used torepresent vertical pre-cracks. Due to the symmetry ofthe problem, the analysis of half-beam is sufficient.The load-displacement relationship under shear loadingRC Smeared elementsDiscrete joint element450750Unit:mm(a) Finite element meshFig. 8 FEM mesh of beam with vertical pre-cracks.Load(kN)25020015010050Non pre-cracked beamExperimentAnalysis00 5 10 15 20Center span deflection(mm)Fig. 9 Prediction of load-deflection by FEM.4

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Pre-crack zonePre-crack zone(a) Z-crackMain failure crackFig. 12 Comparison of numerical and experimentalcrack patterns.Load(kN)(b) Failure crackFig. 10 Comparison of experimental andnumerical crack patterns.120100806040200AnalysisExperiment0 1 2 3 4 5Center span deflection(mm)Fig. 11 Comparison of analytical and experimental loaddeflectionof beam with inclined pre-cracks.is shown in Fig. 9. It is seen that the FEM can predictthe load-deflection relation with sufficient accuracy.The comparison of crack pattern is shown in Fig. 10.The experimental crack pattern and the failure processare well captured by the finite element analysis.4.2 Beams with Inclined Pre-CracksThe comparison of experimental and numerical loaddisplacementrelationship for beam is shown in Fig. 11.Good agreement is obtained. Similar to the experiment,analysis predicts a reduction in shear capacity.Numerical crack pattern (Fig. 12) closely follows theexperiment. Similar to the experiment, the analysispredicts no new diagonal crack. Instead, it predicts theactivation of pre-crack, which brings failure to thebeam. It is seen that the non-linear finite elementanalysis can reliably trace load-deflection response,crack pattern and failure mode of the pre-crackedbeam.5. Recommendations for Future Researchand DevelopmentsThe finite element method with the nonlinearconstitutive models was demonstrated to be capable ofsimulating the behavior of damaged existing reinforcedconcrete member. This paper showed just onecapability of the method to deal with reinforcedconcrete members with cracking damages. There areother damages that need to be considered; for example,corrosion of reinforcing steels, chemical attack,carbonation and so on. The author believes that most ofall these damages can be simulated by the nonlinearfinite element method. The research is currentlyconducted to include the effect of these damages byemploying the constitutive (material) models fordamaged concrete and reinforcing steel. This is anadvanced step beyond the nonlinear constitutivemodels for perfect undamaged materials [4] and linearconstitutive models mostly used by engineersnowadays [2]. This method would be greatly valuablefor the engineers to decide the destiny of theirdeteriorated structures.6. ConclusionsThis paper presented a non-linear finite elementanalysis as a tool to evaluate the reinforced concretestructures with cracking damages. This tool isnecessary to understand the behavior of damaged RCmembers. The information will be useful for theappropriate measure against deteriorated structures. Incontrast to using linear finite element analysis for thedesign of new structures, the finite element analysiswith non-linear constitutive models can be used for theevaluation of existing structures. Here, the author hasdemonstrated the capability of this tool by simulatingthe experiment of pre-cracked in computer. It wasfound that the finite element method could reasonablycapture the behavior of pre-cracked members.5

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20027. References1. 2002 JSCE Design Code, Japan Society of CivilEngineers.2. Bathe, K.J. 1982. “Finite element procedures inengineering analysis”, Prentice-Hall, Inc.,Englewood Cliffs, NJ.3. Meyer, C. and Okamura, H. 1982. “Finite elementanalysis of reinforced concrete structures”,Proceedings of the joint US-Japan seminar,Tokyo, American Society of Civil Engineers.4. Okamura, H. and Maekawa, K. 1991. “Nonlinearanalysis and constitutive models of reinforcedconcrete”, Gihodo-Shuppan Co. Tokyo.5. Pimanmas, A. and Maekawa, K. 2001. “Behaviorand finite element analysis of pre-crackedreinforced concrete in shear”, Magazine ofConcrete Research, Vol. 53, No. 4, pp. 263-282.6. Pimanmas, A. and Maekawa, K. 2001. “Influenceof pre-cracking on reinforced concrete behavior inshear”, Concrete Library of JSCE, No. 38, pp.207-223.6

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002A Low-Power High-Frequency Low-DistortionSinusoidal Quadrature Oscillatorวงจรกําเนิดสัญญาณไฟฟาคลอดราเจอร ความถี่สูง สูญเสียพลังงานไฟฟาตํ่าและความเพี้ยนของสัญญาณตํ่าBanlue SrisuchinwongTelecommunications Program, Sirindhorn International Institute of TechnologyP.O. Box 22, Thammasat-Rangsit Post Office, Pathum Thani 12121, Thailand.บรรลือ ศรีสุชินวงศสาขาโทรคมนาคม สถาบันเทคโนโลยีนานาชาติสิรินธรตู ปณ. 22 ปทฝ. ธรรมศาสตร รังสิต ปทุมธานี 12121Abstract: A low-power high-frequency low-distortion sinusoidal quadrature oscillator through the use of the internalcapacitances of CMOS current mirrors and the negative resistance is summarized. The oscillation frequency is 1.9GHz and current-tunable over a range of 370 MHz or 21.6 %. The amplitude matching and the quadrature phasematching are better than 0.029 dB and 0.15°, respectively. Total harmonic distortions (THD) are less than 0.3 %.Carrier to noise ratio (CNR) is 90.01 dBc/Hz at 2 MHz offset from the 1.9 GHz carrier. The power dissipation isonly 0.45 mW. The figure of merit CNR norm is 153.03 dBc/Hz. The ratio of the oscillation frequency to the transitionfrequency of the transistor is 0.25. Techniques of CMOS current mirrors have been demonstrated for an off-chipcapacitorless(OCL) approach to a low power, high frequency, low distortion linear quadrature oscillator.Comparisons to other approaches are presented.Keywords: Sinusoids, Quadrature, High frequency, Low power, CMOS current mirrors, Negative resistance, Internalcapacitances.1. IntroductionIntegrated circuits have enabled millions of electroniccomponents in a small piece of a silicon chip where thearea is typically in the order of 100 mm 2 . Depending onthe complexity and the silicon areas, digital integratedcircuits can be referred to as small-scale (SSI), mediumscale(MSI), large-scale (LSI) and very-large scale(VLSI) integration. Techniques for the design of VLSIhave been suggested in, for example, Srisuchinwong et al(1995a, 1992) and York et al (1992, 1991, 1990).On the other hand, analogue integrated circuits haveplayed crucial roles in various applications includingcircuits for wireless communications (Srisuchinwong etal 2001a). One of a building block is the quadratureoscillator (QO) where two sinusoids with 90° phasedifference are generated for a variety of applicationssuch as in Hartley and Weaver image-reject receiversor in direct-conversion receivers (Razavi 1997).Typically, quadrature oscillators (OOs) can be eithernon-linear or linear types. Nonlinear QOs such asrelaxation and ring QOs are usually realized usingperiodically switching mechanisms and thereforeoutputs may not be readily low-distortion sinusoids(Johns and Martin 1997, Srisuchinwong 2000a, 1998).In contrast, linear QOs employ frequency-selectivenetworks such as LC or RC circuits and consequentlylow-distortion sinusoids can be readily generated(Sedra and Smith 1998, Srisuchinwong 2000a).Existing RC techniques for QOs include all-pass filters(Srisuchinwong 2000a, 1999, 1997), (Srisuchinwong etal 2001b), OTA-C (Ahmed et al. 1997), negativeresistance (Sedra and Smith 1998) and BJT currentmirrors (Pookaiyaudom and Samootrut 1987).Although a related RC current-mirror-only techniqueusing BJTs (Pookaiyaudom and Sitdhikorn 1996) orrelated RC current-tunable techniques (Srisuchinwongand Trung 1995b), (Pookaiyaudom et al 1987) havebeen suggested, they are not for QOs. Such RCtechniques, however, have suffered not only from arelatively low oscillation frequency due to the use ofrelatively large off-chip capacitors but also fromrelatively high power consumptions.Recently, internal capacitances of either BJTs (Tangand Kasperkovitz 1997, Tang et al 2002) or MOS(Sugimoto and Ueno 1997) have been exploited for the‘off-chip capacitorless (OCL)’ approaches to nonlinearQOs. Conversely, internal capacitances of onlyBJTs (Pookaiyaudom and Mahattanakul 1995) havebeen demonstrated for an OCL approach to a linearQO. Such OCL approaches enable high oscillationfrequencies but the power consumptions have remained7

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002relatively high. In particular, an OCL approach to alow power, high frequency, low distortion linear QOhas never been reported.In this paper, a 1.9-GHz, 0.45-mW, 2-V CMOScurrent-mirror sinusoidal quadrature oscillator(Leelasantitham and Srisuchinwong 2002) through theuse of the internal capacitances of CMOS currentmirrors (Srisuchinwong and Leelasantitham 2001c,2000b) and the negative resistance is summarized. Thetechniques result in a new OCL approach to a lowpower, high frequency, low distortion linear QO. Theoscillation frequency (f 0 ) is 1.9 GHz and currenttunableover a range of 370 MHz or 21.6 %.Figure 1. Oscillograms of quadrature waveformsat 1.9 GHz.The amplitude matching and the quadrature phasematching are better than 0.029 dB and 0.15°,respectively. Total harmonic distortions (THD) are lessthan 0.3 %. Carrier to noise ratio (CNR) is 90.01dBc/Hz at 2 MHz offset from the 1.9 GHz carrier withapproximately 0.45 mW dissipation. The figure ofmerit CNR norm is 153.03 dBc/Hz. The ratio of theoscillation frequency to the transition frequency of thetransistor is 0.25. Comparisons to other approaches arepresented.Frequency f o (GHz )2.01.81.61.41.2Expected Oscillation FrequencySimulated Oscillation FrequencyExpected AmplitudeSimulated Amplitude151050-5Amplitude (dB )2. Performance1.010.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0-10Figure 1 depicts the resulting cosine and sineoscillograms of the quadrature currents i O1 and i z ,respectively, at I = 20 µA where the oscillationfrequency f 0 = ω 0 /(2π) is measured to be 1.9 GHz.Figure 2 illustrates plots of the oscillation frequencies(GHz) and the amplitudes (dB) of i O1 versus bias currentI, where the dotted lines indicate the expected analysisand the solid lines indicate the SPICE analysis. Asshown in figure 2, the oscillation frequencies are tunableby the bias current I over approximately 370 MHz andtherefore the tuning range is 21.6 %.Figure 3 depicts the amplitude matching (dB) in terms ofthe ratio i z / i O1 as well as the quadrature phase matching(degrees) in terms of (θ z −θ O1 ) of the quadrature currentsversus frequency. The amplitude matching is as near as0.029 dB whilst the quadrature phase matching for −90°is better than 0.15°. Figure 4 shows the power spectrumlevels (dBm) of the fundamental frequency at 1.9 GHzand the next harmonics of the oscillogram i O1 previouslydepicted in figure 1 using a commercially available fastFourier transform (FFT) program.As shown in figure 4, the distortions are due mainly tothe presence of the second harmonics, which isapproximately 51.5 dBm down from the fundamentalfrequency, and they remain essentially at the samemagnitude over the entire operational bias-current range(13 µA to 20 µA). Consequently, the total harmonicdistortions (THD) are less than 0.3 %.Figure 2. Plots of oscillation frequencies and amplitude versusbias current I.Amplitude Matching (dB):iZ / iO10.050.030.01-0.01-0.03-0.05Current I (uA)Phase AngleAmplitude1.4 1.5 1.6 1.7 1.8 1.9 2.0Oscillation Frequency (GHz)-89.7-89.8-89.9-90-90.1-90.2-90.3Phase Matching (deg)= (phase of iZ) - (phase of iO1)Figure 3. Amplitude and phase matching of the quadraturesignals versus frequency.3. Figure of MeritThe phase noise in figure 4 is equal to –90.01 dBc/Hzat 2 MHz offset from the 1.9 GHz carrier. In otherwords, the carrier-to-noise ratio (CNR) is equal to90.01 dBc/Hz at 2 MHz offset from the 1.9 GHzcarrier. The oscillator requires the total currentconsumption of 8I + 3G 0 I. For I = 20µA and G 0 = 1.1,the power dissipation (P DC ) is only 0.452 mW. Thefigure of merit CNR norm is given by (Tang et al. 2002)8

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002amplitude matching, the quadrature phase matching andthe total harmonic distortion of this work are alsobetter. By using better transistors of very much higherf T than 7.5 GHz, much higher and more usefuloscillation frequency can be expected.5. Future WorkFigure 4. Harmonic spectrums of the output waveform i 01depicted in figure 1 and Carrier to Noise Ratio (CNR) at2 MHz offset frequency from the 1.9 GHz carrier.⎡⎛fCNR CNR 10 log ⎢⎢⎣⎝mnorm= − ⎢⎜⎟f02⎞P⎤DC⎥⎠1mW⎥⎦(1)where f m is the offset frequency and f 0 is the oscillationfrequency. Substituting f m = 2 MHz, f 0 = 1.9 GHz, P DC= 0.452 mW and CNR = 90.01 dBc/Hz into (1) yieldCNR norm = 153.03 dBc/Hz.4. Comparison to Other ApproachesTable 1 compares the performance of this work to thatof other OCL approaches to linear and non-linear QOs.It is evident from table 1 that this work offers not onlythe very much lower power consumption but also thebetter figure of merit CNR norm . In addition, theFuture work may concentrate on improvements of theoscillation frequency and the ratio of the oscillationfrequency to the transition frequency of the transistor.The internal capacitances of MOS may be exploited foroff-chip-capacitorless (OCL) approaches to otherapplications.6. ConclusionsA 1.9-GHz, 0.45-mW, 2-V CMOS current-mirrorsinusoidal quadrature oscillator through the use of theinternal capacitances of CMOS current mirrors and thenegative resistance has been summarized. Theoscillation frequency is 1.9 GHz and current-tunableover a range of approximately 370 MHz or 21.6 %. Theamplitude matching and the quadrature phase matchingare better than 0.029 dB and 0.15°, respectively. Totalharmonic distortions can be adjusted easily to be lessthan 0.3%. Carrier to noise ratio (CNR) is 90.01dBc/Hz at 2 MHz offset from the 1.9 GHz carrier. Thepower dissipation is only 0.45 mW. The figure of meritCNR norm is 153.03 dBc/Hz. The ratio of the oscillationfrequency to the transition frequency of the transistor is0.25. Comparisons to other approaches have beenpresented.Table 1: Performance of the off-chip-capacitorless (OCL) approaches to linear and non-linear QOsReferencesLinear (OCL) QOsNon-linear (OCL) QOsCMOS BJTs CMOS BJTsTang and Tang et al.This paper Pookaiyaudom and Sugimoto andKasperkovitz 2002PerformancesMahattanakul 1995 Ueno 19971997f o GHz 1.9 0.58 1.4 2.2 11.5f T GHz ∼7.5 - - 11 30f o / f T - ∼0.25 - - 0.2 0.38P DC mW 0.45 - 5.7 100 75CNR norm dBc/Hz 153.03 - - 146.8 150.4AM dB < 0.029 - < 1 < 0.1 -QPM deg < 0.15° - < 1° < 0.5° -THD % < 0.3 From 0.5-1.0 - - -ReferencesAhmed, M.T., Khan, I.Q., and Minhaj, N. 1997, “OnTransconductance-C Quardrature Oscillators,”International Journal of Electronics, 83, 201-207.Johns, D. A. and Martin, K. 1997, Analog IntegratedCircuit Design. New York : John Wiley & Sons.Leelasantitham, A. and Srisuchinwong, B. 2002, “A1.9-GHz, 0.45-mW, 2V CMOS Current-MirrorSinusoidal Quadrature Oscillator,” submitting toInternational Journal of Electronics.Pookaiyaudom, S., and Sitdhikorn, R. 1996, “Current-Differencing Bass-Pass Filter Realization withApplication to High - Frequency Electronically9

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Tunable Low-Supply-Voltage Current-mirror-onlyOscillator,” IEEE Transaction on Circuits andSystem-II, 43, 832-835.Pookaiyaudom, S., and Mahattanakul, J. 1995, “A 3.3volt high-frequency capacitorless electronicallytunablelog-domain oscillator,” Proceedings of the1995 IEEE International Symposium on Circuits andSystems, 2, 829-832.Pookaiyaudom, S., and Samootrut, K. 1987, “Current-Mirror Phase-Shifter Oscillator,” Electronics Letters,23, 21-23.Pookaiyaudom, S., Srisuchinwong, B. and Kurutach, W.1987, "A Current-Tunable Sinusoidal Oscillator,"IEEE Transactions on Instrumentation and Measurement,vol. IM-36, no. 3, September, pp. 725-729.Razavi, B. 1997, “Design Considerations for Direct-Conversion Receivers,” IEEE Transaction on Circuitsand System-II, 44, 428-435.Sedra, A., and Smith, K.C. 1998, MicroelectronicCircuits, 4 th edn. New York: Oxford University Press.Srisuchinwong, B. 1997, “A Fully-Balanced Wide-Frequency Current-Tunable All-Pass Filter,”Proceedings of the 1 st International Conference onInformation, Communications & Signal Processing(ICICS’97), IEEE Singapore, 9-12 September, pp.1732-1736.Srisuchinwong, B. 1998, “A Fully-Balanced Wide-Frequency Current-Tunable Integrator,” ThammasatInternational Journal of Science and Technology,vol.3, no.1, pp. 72-77.Srisuchinwong, B. 1999, “A Wide-Frequency Current-Tunable Sinusoidal Quadrature Oscillator UsingSignal-Differencing Phase Shifters,” Proceedings ofthe 1999 IEEE International Symposium onIntelligent Signal Processing and CommunicationSystem (ISPACS’99), Phuket, Thailand, 8-10December, pp. 497-500.Srisuchinwong, B. 2000a, “Fully balanced currenttunablesinusoidal quadrature oscillator,”International Journal of Electronics, 87, 547-556.Srisuchinwong, B. and Leelasantitham, A. 2001c, “A2V Capacitorless Current-Tunable All-Pass FilterUsing Current Mirrors,” Thammasat InternationalJournal of Science and Technology, vol.6, no.1, pp.46-51.Srisuchinwong, B. and Leelasantitham, A. 2000b, “A2V-700MHz Current-Tunable Phase Shifter UsingMOS Internal Capacitances,” Proceedings of the2000 IEEE Asia Pacific Conference on Circuits andSystems (IEEE APCCAS 2000), China, December 4-6, pp. 205-208.Srisuchinwong, B., Seedadan, I. and Surakampontorn,W. 2001b, “Fully Differential Sinusoidal QuadratureOscillator using Current-Tunable Phase-Lead All-Pass Filters,” Proceedings of 2001 IEEJ InternationalAnalog VLSI Workshop, May 14-15, Bangkok,Thailand, pp. 122-127.Srisuchinwong, B., Surakamporntorn, W. andTantaratana, S. Editors, 2001a, Circuits for WirelessCommunications, Selected Reading, IEEE Press.Srisuchinwong, B. and Trung, N.V. 1995b, "AnIntegratable Current-Tunable R-L Oscillator,”Proceedings of the 1995 International Symposium onSignals, Systems and Electronics (ISSSE’95),October 25 - 27, 1995, San Francisco, USA, pp. 541-544.Srisuchinwong, B., Tsalides, Ph., York, T.A., Hicks,P.J. and Thanailakis, A. 1992, “VLSI Implementationof Mod-P Multipliers Using Homomorphism andHybrid Cellular Automata,” IEE Proceedings-Part E,vol. 139, no. 6, November, pp. 486-490.Srisuchinwong, B., York, T.A. and Tsalides, Ph. 1995a"A Symmetric Cipher Using Autonomous and Non-Autonomous Cellular Automata,” Proceedings ofIEEE 1995 Global Telecommunications Conference(GLOBECOM’95), November 13 - 17, 1995,Singapore, pp.1172-1177.Sugimoto, Y., and Ueno, T. 1997, “The Design of a1V, 1 GHz CMOS VCO Circuit with In-phase andQuadrature-phase Outputs,” Proceedings of the 1997IEEE International Symposium on Circuits andSystems, 269-272.Tang, J. Van der, and Kasperkovitz, D., 1997, “A 0.9-2.2GHz monolithic quadrature mixer oscillator fordirect-conversion satellite receivers,” Proceedings ofthe 1997 IEEE International Solid-State CircuitsConference, 40, 88-89.Tang, J. Van der, Kasperkovitz, D., and Roermund, A.Van, 2002, "A 9.8-11.5 GHz Quadrature RingOscillator for Optical Receivers," IEEE Journal ofSolid-State Circuits, 37, 438-442.York, T.A., Srisuchinwong, B., Hicks, P.J., Tsalides,Ph. and Thanailakis, A. 1992, "VLSI Implementationof Cellular Automata : Modulo Arithmetic Units,"Applied Mathematics and Computer Science, SpecialIssue : Proceedings of the ACEP Workshop,Borowice, Poland, 1992, pp. 221-230.York, T.A., Srisuchinwong, B., Tsalides, Ph., Hicks,P.J. and Thanailakis, A. 1991, "Design and VLSIImplementation of a Mod-127 Multiplier UsingCellular Automaton-Based Data CompressionTechniques," IEE Proceedings - Part E, Vol. 138, No.5, September, pp. 351-356.York, T.A., Tsalides, Ph., Srisuchinwong, B., Hicks,P.J. and Thanailakis, A. 1990, "VLSI Implementationof Modulo-Arithmetic Units Using 2-D CellularAutomata," IEEE CompEuro Conference, Tel Aviv,Israel, May, pp. 558-559.10

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Mitigating Environmental Emissions fromthe Thai Power Sectorการลดมลภาวะสิ่งแวดลอมจากภาคผลิตกระแสไฟฟาในประเทศไทยBundit Limmeechokchai and Somporn TanatvanitEnergy Technology ProgramSirindhorn International Institute of TechnologyP.O. Box 22, Thammasat-Rangsit Post Office, Pathum Thani 12121, Thailand.บัณฑิต ลิ้มมีโชคชัย และ สมพร ธเนศวาณิชยสาขาเทคโนโลยีพลังงาน สถาบันเทคโนโลยีนานาชาติสิรินธรตู ปณ. 22 ปทฝ. ธรรมศาสตร รังสิต ปทุมธานี 12121Abstract: The technical and policy options in the mitigation of greenhouse gas (GHG) emissions and other harmfulemissions from the power sector in Thailand were analyzed by using a least cost model called the integratedresource planning (IRP) model. The IRP model generates alternative resource plans of both supply-side anddemand-side options for electricity generation based on the minimum total cost manner. In the traditional electricityplanning (TEP), only traditional technology plants are considered in the model. The clean supply-side options anddemand-side-management options are considered in the IRP case including the independent power producers (IPPs).When GHG emissions are taken into account, the model generates different plans that include more clean supplysideoptions and DSM options. Results of the study are presented of the GHG mitigation options in the power sectorand the impacts of IPP on electricity generation expansion planning in Thailand.บทคัดยอ: บทความนี้วิเคราะห ทางเลือกในเชิงเทคนิคและนโยบาย ของการลดการปลดปลอยกาซเรือนกระจกและมลพิษอื่นๆ จากภาคผลิตกระแสไฟฟาในประเทศไทย โดยใชแบบจําลองคํานวณตนทุนต่ําสุด ที่เรียกวาแบบจําลองการวางแผนทรัพยากรในการผลิตกระแสไฟฟาโดยรวม หรือที่เรียกวาแบบจําลอง IRP แบบจําลอง IRP สามารถสรางแผนการผลิตกระแสไฟฟาที่มีตนทุนต่ําสุดที่ประกอบดวยทางเลือกทั้งอุปทานและทางเลือกดานอุปสงค ในการวางแผนผลิตกระไฟฟาที่สืบตอกันมานั้น เราพิจารณาเพียงโรงไฟฟาที่ใชเทคโนโลยีแบบดั่งเดิม เชนโรงไฟฟาพลังความรอนจากกาซธรรมชาติ น้ํามัน และถานหิน แตในกรณีการวางแผนผลิตกระแสไฟฟาแบบ IRP มีการพิจารณาทั้งทางเลือกการผลิตกระแสไฟฟาที่สะอาดกวาและโครงการการจัดการดานการใชไฟฟา รวมถึงผลกระทบจากผูผลิตไฟฟาอิสระหรือ IPP เมื่อคํานึงถึงการปลดปลอยกาซเรือนกระจกจากการผลิตไฟฟา แบบจําลอง IRP ไดใหผลลัพธของแผนการผลิตกระแสไฟฟาที่รวมอุปทานที่สะอาดและโครงการการจัดการดานการใชไฟฟา บทความนี้นําเสนอผลลัพธของแนวทางในการลดกาซเรือนกระจกในการผลิตกระแสไฟฟาและผลกระทบของผูผลิตกระแสไฟฟาอิสระหรือ IPP ตอการวางแผนการขยายกําลังการผลิตกระแสไฟฟาในประเทศไทย1. IntroductionIn order to develop the international competitiveness ofthe country, a sufficient supply of energy to meet thedemand in various economic activities is essentialsince energy is an important production factor. Thesupply of energy must be sufficiently high quality atreasonable price. At present, electricity productionmust utilize energy not only in an efficient andeconomical manner but also in the way of lessenvironmental impact.In the past decade, Thailand has achieved a higheconomic growth rate. As a result, the growth ofelectricity demand in Thailand has a significant impactnot only in terms of capacity expansion but also interms of environmental emissions. In 1999, the powersector was a major energy-consuming sector and thelargest emitter of GHGs in Thailand due to the highfossil combustion in the thermal power generation [1].At present it appears that the fossil fuel resources arelimited and the combustion of fossil fuels pollutes theenvironment, especially in the case of electricitygeneration. The Thai power sector being one of themajor contributors to GHG emissions is likely to offersignificant potential for GHG mitigation options. Thetraditional electricity planning (TEP) is focused onlyon the identification of supply-side options to meet theforecasted demand at a minimum cost. An alternativeapproach for the power sector development is the11

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002integrated resource planning (IRP) which considersboth supply-side and demand-side options to formulatethe least cost power development plan. The IRP offersa wider choice of resources to meet the forecasteddemand at a least cost manner resulting in a lowerGHG emission level.2. MethodologyThe framework of the study is based on the least costgeneration expansion planning analysis. The objectiveis to determine the electricity generation expansionplan that results in the minimum total cost (comprisingcapacity, fuel, operation costs and DSM costs) ofpower generation over the planning period, as shown inFigure 1 [2].Electricity DemandIRP ModelFigure 1. Analytical framework of the IRP model.2.1 Descriptions of CasesThe article considers two main baseline cases: thebusiness as usual or the base case or the traditionalelectricity planning (TEP) baseline case and the IRPbaseline case.2.1.1 The traditional electricity planningThe TEP case considers only the conventional supplysidetechnologies/options to meet the forecasteddemand. The candidate plants in the TEP case includecoal and oil-fired steam plants, gas-based combinedcycleplant, and the diesel gas-turbine plant for thepeaking periods. The emissions of CO 2 , SO 2 and NO xare calculated by using emission factors provided bythe Intergovernmental Panel on Climate Change(IPCC) [3].2.1.2 The integrated resource planningExisting and Candidate plant dataOptimal DSM Optimal generation capacity Optimal fuel requirementsTotal costsDemand-side dataCO2 LimitationEnvironmental implications(CO2, SO2 and NOx)In the IRP case, three DSM options are consideredconsisting of 1) replacement of 20-W and 40-Wfluorescent lamps with 18-W and 36-W fluorescentlamps, respectively, hereafter called DSM1, 2)replacement of conventional refrigerator with the mostenergy efficient refrigerator, hereafter called DSM2,and 3) replacement of conventional air-conditionerwith the most energy efficient air-conditioner, hereaftercalled DSM3.2.1.3 The clean supply-side caseThree committed independent power producer (IPP)plants, based on coal-fired plants, and the integratedgasification combined cycle (IGCC) plant and thepressurized fluidized bed combustion (PFBC) plant arealso introduced to both the TEP and the IRP cases. TheTEP case with clean supply-side options is hereaftercalled CSS case. In this study, the total 6 cases areconsidered as follows:- the base case or the TEP case,- the clean supply-side or the CSS case,- the integrated resource planning (IRP) case,- the TEP with committed IPP plants, hereaftercalled the TIPP case,- the TEP with IGCC plants replacing the committedIPP plants, hereafter called the TIGC case,- the TEP with PFBC plants replacing the committedIPP plants, hereafter called the TPFB case.3. Power System in Thailand3.1 Load DataThe annual peak loads used in the study period areobtained from the load forecast of ElectricityGenerating Authority of Thailand (EGAT) [4], whichsupport information in the period of 2003 to 2011. Thetrend of the load is used to forecast the load from 2012to 2017 at a constant load factor of 0.723. The peakload is expected to increase from 18,300 MW in 2003to 39,490 MW in 2017.3.2 Existing Power PlantsIn 1998, the existing power plants consisted of 161units with total capacity of 18,174.5 MW, of which2,874 MW (15.8%) from hydro, 6,517 MW (35.9%)from conventional oil/gas and lignite-fired thermal,5,074 MW (27.9%) from gas-based combined cycle,892 MW (4.9%) from gas turbine and diesel, and 2,818MW (15.5%) purchased from other power suppliers[4]. The characteristics of each existing plant used inthe analysis are taken from the EGAT’s report [4].3.3 Clean Supply-side OptionsThough Thailand has large lignite reserves, lignite hasmany contaminants. Besides renewable energytechnology, EGAT has been interested in the researchand development of clean coal technology, such asIntegrated Gasification Combined Cycle (IGCC) [5].Therefore, the fuel choice of supply-side options isdivided into two categories: conventional supply-sideoptions and clean supply-side options, as shown inTable 1.12

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Table 1. The candidate supply-side options.Plant typeConventional optionsCoal-firedCoal-firedOil-firedGas turbineCombined cycleClean optionsIGCC 1PFBC 2BiomassFuel typeCoalCoalFuel oilDieselNatural gasCoalCoalBiomassCapacity(MW)70010001000200600500500100Notes: IGCC = Integrated Gasification Combined Cycle.PFBC = Pressurized Fluidized Bed Combustion.Sources: 1 IPCC (1996).3.4 Demand-side-management OptionsCO 2 emissionfactor 1(g/kWh)9479196368604738219030The demand-side-management options considered inthis study include i) replacing 20-W and 40-Wfluorescent lamps with 18-W and 36-W fluorescentlamps, respectively, hereafter called DSM1, ii)replacing the conventional refrigerator with the mostenergy efficient refrigerator, hereafter called DSM2,and iii) replacing the conventional air-conditioner withthe most energy efficient air-conditioner, hereaftercalled DSM3.3.5 Electricity Supply Industry Reform in ThailandAt present the electricity supply industry (ESI) inThailand consists of the generation agency, theElectricity Generating Authority of Thailand (EGAT),and distribution agencies, the Metropolitan andProvincial Electricity Authorities, called MEA andPEA, respectively. All are owned by the governmentplus some private power producers under contract toEGAT. However, the Royal Thai Government plans 3stages of reforming the electricity supply industry. Inthe initial stage (1999-2000), EGAT remained a stateenterprise. Some functions of EGAT have transformedinto business units. In the second stage (2001-2003),EGAT will be transformed into a holding companywith its business units operated under subsidiarycompanies. In the final stage (starting from 2003),power generation and transmission in Thailand will beunder a free market environment. Supplier andcustomers will be linked through a power pool. Theonly generation facility, which will be owned byEGAT, is the hydro power plant.The 1999 power development plan of EGAT hasincluded power purchased from IPPs. In this study, theIPPs are committed with generating capacity of 5,944MW during 2000 to 2007. [6]4. Results and Discussions4.1 Utility Planning ImplicationsThe results of the least cost planning from the IRPmodel show that coal-based plants take the largestshare in the total additional capacity in the TEP case,the CSS and the IRP cases, and also in the case of IPPcases, presented in Table 2. In the CSS case, 3 units ofbiomass-based plants are committed. However, theIGCC and PFBC plants are not selected due to theirhigh total costs. In the IRP case, both additionalTable 2. Generating capacity by plant typesin selected years.Capacity-mix (MW)Case Plant types 2003 2007 2012 2017TEP HydroCoal-firedOil-firedCCGTIPP2,8862,6255,5889,1478863,8972,8868,6254,6588,3878863,8972,88620,5004,1457,7088863,8972,88628,1002,97011,3991543,897CSS HydroCoal-firedOil-firedCCGTIPPBiomass2,8862,6255,5889,1478663,89702,8868,6254,6588,3878863,89702,88620,8004,1457,1088863,8973002,88626,0002,97013,1991543,897300IRP HydroCoal-firedOil-firedCCGTIPP2,8862,6255,5889,1478863,8972,8868,6254,6588,3878863,8972,88618,4004,1459,5088863,8972,88624,6002,97014,3991543,897TIPP HydroCoal-firedOil-firedCCGTIPPIPP_Coal2,8862,6255,5889,1478863,89702,8866,6254,6588,3878863,8972,0472,88619,1004,1457,1088863,8972,0472,88624,6002,97013,1991543,8972,047TIGC HydroCoal-firedOil-firedCCGTIPPIGCC2,8862,6255,5889,1478863,89702,8866,6254,6588,3878863,8972,0472,88617,4004,1458,9088863,8972,0472,88625,3002,97011,9993543,8972,047TPFB HydroCoal-firedOil-firedCCGTIPPPFBC2,8862,6255,5889,1478863,89702,8866,6254,6588,3878863,8972,0472,88618,5004,1457,7088863,8972,0472,88625,3002,97011,9993543,8972,04713

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002capacity and power generation are less than those inthe TEP case by approximately 1.5% in additionalcapacity and 61,681 GWh in generation due to energysavings from the DSM programs. The loss-of-loadprobabilities (LOLPs) in all cases are not changed.Table 3. Electricity generation in selected yearsCaseTEPCSSIRPTIPPTIGCTPFBGeneration-mix (TWh)PlantTypes 2003 2007 2012 2017HydroCoal-firedOil-firedCCGTIPPSubtotalHydroCoal-firedOil-firedCCGTIPPBiomassSubtotalHydroCoal-firedOil-firedCCGTIPPSubtotalHydroCoal-firedOil-firedCCGTIPPIPP_CoalSubtotalHydroCoal-firedOil-firedCCGTIPPIGCCSubtotalHydroCoal-firedOil-firedCCGTIPPPFBCSubtotal4.219.830.463.23.211.41324.219.830.463.23.211.401324.219.829.563.13.111.41314.219.830.463.23.211.401324.219.830.463.23.211.401324.219.830.463.23.211.401324.262.828.460.33.511.41714.262.928.460.33.511.401714.262.926.859.53.111.41684.248.528.360.33.411.414.51714.248.528.160.33.411.414.71714.248.528.160.33.411.414.71714.2147.816.347.42.711.42304.2150.016.343.12.711.42.12304.2132.816.657.02.811.42254.2137.816.143.12.811.414.62304.2125.616.954.42.811.414.72304.2133.516.247.22.711.414.72304.2202.011.754.10.511.42844.2187.011.767.60.511.42.12844.2177.010.473.00.511.42764.2177.611.464.90.511.414.52844.2182.010.759.91.011.414.72844.2182.011.758.81.011.414.7284Total2003-2017(TWh)621,648318859381713,096621,64531285238171163,096621,527308930371713,034621,497306833371711893,096621,432312890371711913,096621,466311857381711913,096In the IRP case, the cumulative generation avoidedthrough the DSM programs during 2003-2017 is61,681 GWh, of which 13,261 GWh from DSM1,15,629 GWh from DSM2 and 32,792 GWh fromDSM3. The DSM3, the efficient air conditionerprogram, has the highest contribution to the avoidedgeneration. In 2017, the peak load avoided in the IRPcase is 364.7 MW, of which 36.4 MW from DSM1,94.6 MW from DSM2 and 233.7 MW from DSM3. Inaddition to the reduction in electricity generation, theIRP case also shows a reduction in fuel consumption.The total fuel consumption in the IRP case is 640million tons of oil equivalent (toe), while that in theTEP case is 658 million tones.4.2 Environmental ImplicationsResults from the IRP model show that only the CSSand IRP cases have lower emissions. In all cases, theshare of coal-based plants is about 50% resulting inhigh CO 2 and SO 2 emissions. In the CSS case, thecoal-based plant is substituted with the biomass-basedplant resulting in lower CO 2 emission. In the CSS case,the total CO 2 , SO 2 and NO x emissions are found to beless than in the TEP case by 13 million tons, 77 ktonsand 31 ktons, respectively, while in the IRP case CO 2 ,SO 2 and NO x emissions are found to be less than in theTEP case by 89 million tons, 1017 ktons and 195ktons, respectively. The cumulative CO 2 , SO 2 and NO xemissions during the planning horizon are shown inTable 4. In the CSS case, one unit of 700-MW coalfiredpower plant and one unit of 200-MW gas turbineplant are replaced by three units of 600-MWcombined-cycle and three units of 100-MW biomassbasedplants resulting in total CO 2 reduction.Though the power generation from coal-based plantsincreases in the TIGC and TPFB cases, CO 2emissions are reduced by 70 and 53 million tons,respectively, and SO 2 emissions are reduced by 1,703and 1435 ktons, respectively, compared to the TEPcase. The electricity generation from the IGCC andPFBC plants in the TIGC and TPFB cases results inless CO 2 , SO 2 and NO x emissions compared to the TEPcase.In the TIPP case, the power generation from the coalbasedplants increases due to committed coal-basedIPP plants resulting in higher CO 2 emission by 35%million tons or approximately 1.6% compared to theTEP case.Table 4. Cumulative emissions during 2003-2017.Case study CO 2, 10 6 tons SO 2, 10 3 tons NO x, 10 3 tonsTEPCSSIRPTIPPTIGCTPFB2,2312,2182,1422,2662,1612,17914,85014,77313,83314,65313,14713,4156,9376,7436,9068,1686,5026,59014

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20024.3 Economic ImplicationsResults from the IRP show that the fuel and O&Mcosts take the largest share of total cost, byapproximately 80% as shown in Table 5. The totalcosts in the TIPP, TIGC and TPFB are higher than thatin the TEP case because of the high capital costs ofIPP, IGCC and PFBC plants. The total cost in the CSScase decreases by US$ 6 million compared to the TEPcase because more clean supply-side options whichhave lower capital costs are available while in the IRPcase the total cost including DSM costs are less thanTEP case by US$ 260 million due to efficient use ofelectricity in the DSM programs. The total costs affectthe long run average costs (LRAC). The LRACs in allcases are higher than that in the TEP case except in theCSS case.The average marginal cost of abatement (MAC) ofCO 2 emission can be calculated by using the followingequation:MAC =⎪⎧⎨⎪⎩ tT∑= 1( E( TC −TC)0 ,t− E) /( 1 + r )where TC c = present value of total cost correspondingto the least cost generation expansion plan with cleansupply-side options, TC 0 = present value of total costcorresponding to the least cost generation expansionplan without clean supply-side options, E 0,t = CO 2emission in year t corresponding to the least costgeneration expansion plan without clean supply-sideoptions, E c,t = CO 2 emission in year t corresponding tothe least cost generation expansion plan with cleansupply-side options, r = discount rate, and T = numberof years in the planning horizon. The MAC of CO 2emission in each case is also determined, as shown inTable 6.Table 5. Cumulative costs during 2003-2017.Cost components (10 6 US$)CaseTotalstudy Capital O&M DSMcostLRACcents/kWhTEP 5,031 25,152 0 30,183 3.10CSS 4,873 25,061 0 30,177 3.10IRP 5,637 24,816 234 29,923 3.11TIPP 5,177 25,797 0 30,973 3.18TIGC 5,637 25,167 0 30,804 3.16TPFB 5,697 25,220 0 30,916 3.18cThe MACs of IGCC in the TIGC case and PFBC in theTPFB case were found to be 76.4 and 116.8 US$/ton ofcarbon, respectively, compared to 35 US$/ton ofcarbon in the case of full global trade [8]. However, inthe case of DSM options in the IRP case and biomassbasedplants in the CSS case, MACs were found to be-30.2 and -10.3 US$/ton of carbon, respectively, whichreflect the no-regret options in terms of CO 2mitigation. Further analysis of MACs reveals that thec ,t0t⎪⎫⎬⎪⎭substitution of IGCC and PFBC plants for coal-basedplants in the IRP baseline results in MACs of 8.9 and18.4 US$/ton of carbon, respectively, which are lowerthan the price of carbon under full trading scenario.The implication of MACs of IGCC and PFBC plants inthe IRP baseline reveals as candidate in the cleandevelopment mechanism (CDM) project under theKyoto Protocol.Table 6. Marginal abatement costs in 1998 price.Countries MAC (US$/ton of carbon)Thailand 1Under TEP baseline cases- Biomass in the CSS case- IGCC in the TIGC case- PFBC in the TPFB caseUnder IRP baseline cases- DSM options- IGCC as IPP plant- PFBC asIPP plant-10.376.4116.8-30.29.018.4Japan 2 876.0European Union 2 409.5Other OECD Countries 2 349.5USA 2 279.0Full Global Trade 3 35.0Source: 1 carried out by authors [9, 10, 11]2 no trade [7]3 full global trade [8]5. ConclusionsResults of the least-cost power generation expansionplans reveal less CO 2 emissions due to clean supplysideoptions in the CSS, TIGCC and TPFB cases, andDSM options in the IRP case. In the CSS case,substitutions for coal-based plants are combined-cyclegas-based plants and biomass-based plants, which emitless CO 2 . The IGCC and PFBC plants are not selectedin the CSS case due to their high capital costs. Theintegration of DSM options and clean supply-sideoptions could reduce both total system costs and CO 2emissions. Therefore the IRP case is more suitable forCO 2 mitigation [9, 10].In the IPP case, environmental emissions are directlyrelated to the technologies of power plants used byIPPs. The committed IPPs in the TIPP case are basedon coal-based plants resulting in higher CO 2 emissions.The substitution of IGCC and PFBC plants for coalbasedplants could be the CO 2 mitigation in the IPPcase [11].6. Recommendations for Future Researchand DevelopmentAccording to the presented results in this article, theintroduction of Energy Efficient Technology (EET) isnot effective because the main barrier in adoption ofthe EETs is their high investment cost. Furthermore,there are some other barriers, which need to be15

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002analyzed such as the risk in adoption of EETs and themeasures to overcome barriers and risks. Therefore, thefuture research work would be related to this subject.7. AcknowledgementsThis article was summarized from a part of the researchwork carried out in the framework of the AsianRegional Research Programme in Energy,Environment and Climate (ARRPEEC) funded by theSwedish International Development Co-operationAgency (SIDA). The authors would like to thank Prof.Ram M. Shrestha from Asian Institute of Technologyfor the support of the IRP model used in the analysis,and Mr. Terry Avon for his English support to thispaper. However, only the authors are responsible forthe views expressed in this article and for any errors.8. NomenclatureCC = combined-cycle power plantCSS = clean supply-side caseDSM = demand-side managementEGAT = Electricity Generating Authority of ThailandESI = electricity supply industryIGCC = Integrated Gasification Combined CycleIRP = integrated resource planningLOLP = loss-of-load probabilityLRAC = long run average costMAC = marginal abatement costO&M = operating and maintenancePFBC = Pressurized Fluidized Bed CombustionTEP = traditional electricity planningtoe = tonne of oil equivalentTWh = teraWatt-hours9. References1. Department of Energy Development and Promotion(DEDP), Thailand Energy Situation 2000, Ministryof Science, Technology and Environment,Thailand, 2002.2. Shrestha, R.M., Shrestha, R. and Samarakoon, H.(2001). An electric utility integrated resourceplanning model, Energy program, School ofEnvironment, Resource and Development, AsianInstitute of Technology, Pathumthani, Thailand,2001.3. Intergovernmental Panel on Climate Change, IPCCGuideline for National Greenhouse GasInventories, 1996.4. System Planning Department, EGAT PowerDevelopment Plan (Revised PDP 99-01), ElectricityAuthority of Thailand, Thailand, 1999.5. R&D Division, EGAT, The study on lignitecoal/gasification process for combined cycle powerplant, 1999, Thailand. (URL: http://www.egat.or.th/english/generalinfo/r&dprojects)6. National Energy Policy Office (NEPO),privatization and Increasing Private Sectorparticipation in the Energy Sector in Thailand,Thailand, 1998.7. A. Denny Ellerman and Annelene Decaux. (1998).Analysis of Post-Kyoto CO 2 Emissions TradingUsing Marginal Abatement Curves.(http://web.mit.edu/globalchange/www/MITJPSGC_Rpt40.pdf)8. A. Denny Ellerman, Henry D. Jacoby andAnnelene Decaux (1998) The Effects on Developingof the Kyoto Protocol and CO 2 Emissions Trading.(http://web.mit.edu/globalchange/www/MITJPSGC_Rpt40.pdf )9. Sirindhorn International Institute of Technology(SIIT), 2000a, Least cost supply-side options formitigating GHG and other harmful emissions fromthe power sector subject to emission target, Reporton Issue # 1, Thammasat University, Thailand.10. Sirindhorn International Institute of Technology(SIIT), 2000b, Identification of some CleanDevelopment Mechanism (CDM) projects in thepower sector and assessment of their GHG andother harmful emissions mitigation potential,Report on Issue # 2, Thammasat University,Thailand.11. Sirindhorn International Institute of Technology(SIIT), 2001, Assessment of Environmental andUtility Planning Implications of IndependentPower Producers and Decentralized PowerGeneration, Report on Issue # 3, ThammasatUniversity, Thailand.16

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Progress and Open Problems inMultidimensional Multirate System DesignChalie CharoenlarpnopparutTelecommunications ProgramSchool of Communications, Instrumentations and ControlSirindhorn International Institute of Technology, Thammasat UniversityKlongluang, Pathumthani 12121, ThailandPhone : (+662) 986-9009 Ext. 1808, Fax : (+662) 986-9009 Ext. 1801E-mail : chalie@siit.tu.ac.thAbstract: Multivariate polynomial matrix factorization is tightly linked to many problems faced in multidimensionalsystem realization, parameterization and design. The bottlenecks in the one- and two-dimensional zeroprime matrix factorization have been resolved since 1982 by Guiver and Bose. However, since then, there was onlymoderate amount of progress toward the general n-variate matrix factorization problem. This paper presents theadvancement made toward this goal and posts some open problems in the area. As motivation, some applications ofmultivariate polynomial matrix algebra, namely multiband IIR and FIR filter bank designs, are discussed in concisefashion.1. IntroductionRecently, research in the area of multidimensionalsystem and signal processing has gained significantinterest due to an increasing demand in the usages ofmultidimensional data transmission as well as multidimensionalsystem controllers. The polynomialapproach used to solve many engineering problems hasa long history and in the last few decades, it hasattracted a lot of attention because of its importance inareas of contemporary applied mathematics, includingcontrol systems, network analysis, signal and imageprocessing, and coding theory [4], [13], [14], [16],[17].In general, a multi-input/multi-output (MIMO) systemcan be characterized by a matrix whose elementsbelong to the field of rational functions. The problemof multivariate polynomial matrix factorization isstrongly linked to the factor coprimeness. Theimportance of multivariate polynomial matrix couldnever have been over emphasized since it has so manyapplications in a wide range of engineering fields. Oneof the major applications of multivariate polynomialsystem description includes the design of multibandmultidimensional (n-D, n ≥ 2) filter banks whichpossess a broad spectrum of applicability in image andvideo processing.In this paper, the concept of matrix coprimeness isformally introduced and their related problems arediscussed. Progresses in the area of multivariate polynomialfactorization in recent years are reported. Theadvancement in the design of both FIR and IIR n-Dfilter banks is summarized in moderate detail. Theconclusion and open problems are then includedtoward the end of the paper.2. Coprimeness and Matrix FactorizationIn 1979, Youla and Gnavi [30] laid a foundation on theprimeness and formed basic structures of n-D systemtheory. The three notions of multivariate polynomialmatrix coprimeness, namely zero-, minor- and factorcoprimenesswere also proposed. The concept ofcoprimeness plays an important role in the polynomialmatrix approach to system and signal processingtheory. Here, the notations presented in [10] areadopted. We shall denote K[z] = K[z 1, z 2, …, z n ]the set of all polynomial in n independent complexindeterminants z 1, z 2, … z n with the coefficients in thearbitrary but fixed field K. Furthermore, let K mxl [z] bethe set of m x l matrices each of whose elementsbelongs to K[z]. To be consistent with the notation ofpolynomial module theory [1], the ring K mxl [z] iswritten as K m [z].Definition 1: Let A(z) Є K m x q [z] and B(z) Є K m x l [z]be a normal full rank polynomial matrix, q + l ≥ m > 1,and let C(z) = [A(z) |B(z)]. Then, the pair A(z), B(z) issaid to be1. zero left-coprime (ZLC) if there exists no n-tuplez =(z 1 , z 2 ,…,z n ) which is a zero of all m x mminors (or major determinants) of C (z),2. minor left-coprime (MLC) if these m x m minorsare relatively prime, that is, they are devoid ofcommon polynomial factors other than units,3. factor left-coprime (FLC) if in any polynomialmatrix decomposition C (z) = C 1 (z) C 2 (z) in whichC 1 (z) is square, the determinant of C 1 (z) is an elementof K. i.e. C 1 (z) is unimodular.Note: In dual fashion, A (z) and B (z) are zero rightcoprime(ZRC) etc. if A T (z) and B T (z) are zero leftcoprimeetc, where (.) T denotes transposition.17

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Fact 1 [30]: For n = 1, ZLC MLC FLC; for n = 2,ZLC MLC FLC and for n = 3, ZLC MLC FLC.The constructive algorithm for verifying whether agiven multivariate polynomial matrix is zero-, orminor-coprime has been implemented via the usage ofGröbner basis theory [1], [9] in [7], [8]. However, thesimilar algorithm for testing factor-coprimeness isavailable for bivariate (n = 2) polynomial matrices [15]and for multivariate (n > 2) ones only in some specialcases [19].The analysis, synthesis and design of many multidimensionalsystems [5] often involves the multivariatepolynomial matrix factorization of a specific form. Therealization of a complex system could be simplified bymeans of factorizing the overall transfer function into aset of lower order sub-systems and implementing themby a string of cascade realizations.In [20], it has been shown that any mx(m+1) n-Dpolynomial matrix C ( z ) whose reduced minors(maximal minors whose common factor are extracted)are zero-coprime, can be factorized in the formC ( z ) = C 1 ( z ) C 2 ( z ),where C 1 (z) is square and the determinant of C 1 (z) isthe common factor of all maximal minors of C(z).Some further results for the special case when thenumber of row and column are different by two arealso reported. In general, it is quite desirable to constructan algorithm for testing whether or not a n-Dpolynomial can be factorizable before the actual factorizationprocess has begun.3. Multidimensional Filter Bank DesignThe exponential growth in the research area of imageand video compression during the last decade has beendriven by the need to communicate through graphicand video media on the internet and wire-less networkwhere the bandwidth is a valuable commodity. Inrecent years, the subband coding scheme has gainedsignificant interest due to its flexibility of bandwidthallocation and its performance over narrow-bandchannels [22]. In the subband coding scheme, theimage decomposition is performed with the usage of ananalysis filter bank. The reconstruction of the originalimage, on the other hand, is obtained by using asynthesis filter bank [27]. This multi-resolutionmultirate scheme is conventionally performed byutilizing a series of one-dimensional filter banks onwhich the image data is processed after rearranging theinput image data into an one-dimensional sequence bymeans of scanning (e.g. row-wise, column-wise andzigzag scanning). Unlike in the one-dimensional imagefiltering counterpart, the two-dimensional filter bankcan be designed to accommodate the 2-D image datamore efficiently and more naturally.By means of polyphase matrix representation [28], anm-band (m ≥ 2) n-dimensional filter bank can becharacterized by an m x m n-variate polynomial matrix.As a result, one sensible approach to design andparameterization of multiband multidimensional filterbanks is to obtain multivariate polynomial matriceswhich have some specific structures. These structuresgenerally are translated from the desirable physicalproperties of filter banks into algebraical conditions inthe matrix structure.The multidimensional filter bank design generally ismore flexible due to the extra degrees of freedom in thedesign process. The only drawback of themultidimensional design using a polynomial approachlies in the complexity and the lack of some keytheorems for managing multivariate polynomialmatrices, namely, the prime factorization [20] and thealgorithm for constructing a multivariate polynomialwith pre-specified set of common zeros [11]. Some ofthe desirable properties of filter banks include1. Perfect Reconstruction (PR): To ensure that in anoise-free environment, the reconstructed signalimitates the original signal except with a possibledelay and scaling factor. This property can besatisfied only if the product of the polyphaseanalysis matrix (obtained from the analysis filterbank transfer function) and the polyphasesynthesis matrix has the form of a constantmultiplying some delay variables2. Linear Phase (LP): In the human visual system,our brains are more sensitive to the phasecharacteristic than the magnitude characteristic[18, p. 196]. A nonlinear phase distortion resultingfrom the non-linear characteristic of the filterbanks can be easily detected by human as opposedto the magnitude distortion.3. Having maximum number of vanishing moment[2]: This property plays a significant role ingenerating the wavelet bases with maximalsmoothness. It is also linked to the flatness of passbandof the filters in the filter bank.4. Nonseparable: It is favorable to utilize nonseparablefilters to form the filter bank since it givesoverall better performance due to its better adaptationto the human visual system and highernumbers of degrees of freedom in the design. Notethat a n-D filter is separable if its wavenumberresponse satisfiesH(z 1 ,z 2 ,..,z n ) = H 1 (z 1 )H 2 (z 2 )…H n (z n ).5. Having finite impulse response (FIR): All FIRfilters are inherently stable and simple toimplement.In the remaining of this section, a summary of thecurrent progress in the area of filter bank design ispresented in the following two main categories:18

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002A. Finite Impulse Response (FIR)For a pair of FIR synthesis and analysis filter banks tosatisfy a perfect reconstruction property, the polyphasematrix associated with the synthesis and thus, alsoanalysis filter banks must be unimodular, or in moregeneral algebraic terms, they must be units in the ring.The problem of parameterizing the class of FIR filterbanks with perfect reconstruction property can beequivalently reformulated as the unimodular matrixcompletion problem for multivariate polynomial matrix[21], [25], [24].When the class of FIR filter banks is restricted tosatisfy the conditions for linear phase as well as perfectreconstruction, the problem is completely solved forthe two-band n-D case using Gröbner bases in [10].Although, the problem is satisfactorily solved for themultiband 1-D case in [3] and the two-band multidimensionalcase in [10], the more general problemassociated with the multidimensional multibandcounterpart is still open. Partial Generalization ispossible to the n-D, m-band case, m > 2 [10].B. Infinite Impulse Response (IIR)Unlike that of FIR filters, the stability of IIR filters arenot guaranteed and a special treatment to ensure thestability of each filter in the filter bank is necessary.The linear phase property is, however, not dealt with inthe design since it is almost impossible to obtain. In thetradeoff with linear phase property, IIR filter generallyhas fewer coefficients and thus computationallyefficient implementation is obtainable.Similar to the FIR case, an m-band n-D PR IIR analysis(synthesis) filter bank can be described by an m x mpolyphase matrix H(z) whose elements belong to thering of n-variate rational function K(z 1, z 2, … z n ). Forexample for a two-band 3-dimensional IIR filter bank,the analysis polyphase matrix may take the formwhere h ij (z 1, z 2, z 3 )'s are rational functions in z 1, z 2, z 3.Let S be the set of all (structurally) stable properrational functions (in reduced form i.e. relatively primenumerator and denominator polynomials) in n variablesz 1, z 2, …, z n, having real coefficients. Structural stabilityrequires that the denominator polynomial of therational function be devoid of zeros in the closed unitpolydisc Ū n . Therefore, structural stability impliesbounded-input bounded-output (BIBO) stability but notvice versa [5]. This set S is known to form acommutative ring [2].Definition 2: A minimum-phase rational function is astable rational function with a stable inverse.In the context of PR subband coding, the framework ofIIR filter bank design then reduces to the constructionof a polyphase matrix, associated with the analysisfilter bank, whose determinant is a minimum-phaserational function [2]. Usually the first row of theanalysis polyphase matrix is obtained by computing thepolyphase representation of a low-pass nonseparablemultidimensional filter which can be designed using astandard method as shown in [18]. To design amultidimensional (n > 3) IIR filter bank (for n = 2; see[11]), it is then required to complete the remaining rowof the polyphase matrix such that the determinant ofthe polyphase matrix is of minimum-phase. Toillustrate this, let express the polyphase matrix aswhere n 00 (z), d 00 (z), n 01 (z), d 01 (z) are known.By Hilbert's Nullstellensatz, it follows that there existsan integer N and polynomials p ( z 1, z 2 ) andq ( z 1, z 2 ) such that Eq. (1) below holds.where h(z) is the polynomial that vanishes at thecommon zero(s) of n 00 (z)d 01 (z) and n 01 (z)d 00 (z). If thepolynomial is found, the polynomials p(z 1, z 2 ) andq(z 1 , z 2 ) can subsequently be constructed by applyingBuchberger's algorithm for construction of the Gröbnerbasis. However, the procedure for finding h ( z )currently, to the best of author's knowledge, can onlybe computed in general for the case when n ≤ 2. Whenthis is not the case, a constructive procedure forobtaining the n-variate counterpart of the right-handside of Eq.(1) has not, yet, been advanced in general.Some promising special cases, however, can be tackledusing the Gröbner basis. One of the approachesinvolves the construction of a Gröbner basis of theideal generated by the two generic polynomialsfollowed by a search for an element of the Gröbnerbasis for absence of zeros in Ū n . If such an element isfound, then it can be set to h(z 1 , z 2 ,…,z n ) with N = 1.Other approaches are also pursued by researchers(Lin/Bose/Xu).4. Open ProblemsIn this section three open problems are summarizedand a brief description is also given.A. Multivariate Polynomial Matrix Factorization(1)Let A(z) Є K m x q [z] and B(z) Є K m x l [z] be a normalfull rank polynomial matrix, q + l ≥ m > 1, and letC (z) = [A (z) |B (z)]. There are β = q+l C m m x mminors a 1 , a 2 , …, a β of the matrix C(z). Furthermore,19

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002extracting the greatest common divider (g.c.d.)d of a 1 ,a 2 , …, a β givesa i = db i , i = 1, 2, …, β.Then, b 1 , b 2 ,…,b β are called the generating set orreduced minors of C (z).With the above notation, prove or disprove the followingstatement:If b 1 , b 2 , …, b β are zero-coprime, then C (z) can befactored as C (z) = G 0 (z)C 0 (z), for some G 0 (z) ЄK m x m [z] and C 0 (z) Є K m x q + l [z] with det G 0 (z) = d.Note that the matrix factorization problem definedabove is only a special case of the general matrixfactorization problem which is known to be unsolvablewhen n ≥ 3. However, other special case may also beconsidered.B. Multidimensional Multiband FIR Filter Bank DesignGiven the first column-symmetric row of the mxmmultivariate polynomial matrix H (z), whose elementsare zero-coprime, find the algorithm to complete theremaining m - 1 row of the matrix H (z) such that1. (perfect reconstruction) the determinant of the mxmmatrix is in the formwhere k is a constant, and2. (linear phase) the element of H(z) are columnsymmetric [10].C. Multidimensional Multiband IIR Filter Bank DesignGiven a finite set of n-variate (n ≥ 3) polynomialm{f i (z)} i = 1 which are devoid of common zero in the unitpolydisc, defined as {(z 1 ,z 2 ,…,z n )| |z i |< 1, i = 1,2,…,n},find a polynomial h (z) such that it goes to zeromwhenever all polynomials {f i (z)} do.The solution to this problem will lead to a constructiveprocedure in the parameterizing and designing of themultiband multidimensional PR analysis (as thussynthesis) IIR filter bank.5. Conclusioni = 1In this paper, the recent progress in the area of multidimensionalfilter banks is emphasized and some openproblems are briefly stated.The designs of the multidimensional filter bank areinherently linked to the coprimeness and its variants ofmultivariate polynomial matrix. For the n-D FIR filterbank design, the problem can be increasinglycomplicated when the linear phase constraint is posed.Although a numerical method has recently beenproposed to remedy this problem, the algebraicallyprecise constructive method is still rather unexplored.For an application where the linear phase property isnot strictly enforced, the n-D IIR filter bank can beutilized instead. However, a bottleneck in the design ofmultiband (m ≥ 3), n-D IIR filter bank still exists andrequires further investigation.References[1] W.W. Adams and P. Loustaunau, “An introductionto Gröbner bases,” Graduate Studies in Mathematics,vol. 3, no. 1, American MathematicalSociety, 1994.[2] S. Basu, “Multi-dimensional filter banks andwavelets - a system theoretic perspective,” JFranklin Inst., vol. 335B, no. 8, 1998, pp. 1367-1409.[3] S. Basu and H.M. Choi, “Hermite reductionmethods for generation of complete class of linearphase perfect reconstruction filter banks: Part I-Theory,” IEEE Trans. Circuit and Systems: Part 2:Analog and Digital Signal Processing, vol. 46(2),April 1999, pp. 434-448.[4] K. Benmahammed, “Evaluation of complexpolynomials in one and two variables,” MultidimensionalSystems and Signal Processing, vol.5, no. 3, 1994, pp. 245-261.[5] N.K. Bose, “Applied Multidimensional SystemsTheory,” Van Nostrand Reinhold, New York,1982.[6] N. K. Bose, Multidimensional Systems Theory:Progress, Directions, and Open Problems,Dordrecht, Holland: D. Reidel Publishing Co.,1985.[7] N.K. Bose, C. Charoenlarpnopparut, “Multivariatematrix factorization: New results,” in MathematicalTheory of Networks and Systems Symposium,MTNS-98, Padova, Italy, July 6-10, 1998, pp. 97-100.[8] N.K. Bose and C. Charoenlarpnopparut, “Gröbnerbases for robust control,” in InternationalWorkshop: Control of Uncertain Systems:Emerging Directions, Hong Kong University ofScience and Technology, June 30-July 2, 1999.[9] B. Buchberger, “Gröbner bases: An algorithmicmethod in polynomial ideal theory,” in MultidimensionalSystem Theory (N.K. Bose, ed.),Reidel, Dordrecht, 1985, pp. 184-232.20

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002[10] C. Charoenlarpnopparut and N.K. Bose, “Multidimensionalfilter bank design using Groebnerbases,” IEEE Transactions on Circuits andSystems II: Analog and Digital Signal Processing,vol. 46, no. 12, December 1999, pp. 1475-1486.[11] C. Charoenlarpnopparut and N.K. Bose, “Gröbnerbases for problem solving in multidimensionalsystems,” Multidimensional Systems and SignalProcessing, vol. 12, no. 3-4, 2001, pp. 365-376.[12] D. Cox, J. Little and D. O'Shea, “IDEALS,VARIETIES, AND ALGORITHM: An Introductionto Computational Algebraic Geometry andCommutative Algebra,” 2 nd edition, Springer-Verlag, New York, 1996.[13] G. D. Forney, Jr, “Convolutional codes I:Algebraic structure,” IEEE Trans. Info. Theory,vol. IT-16, Nov. 1970, pp. 720-738.[14] G. D. Forney, Jr, “The Viterbi algorithm,” in Proc.of the IEEE, vol. 61, Mar. 1973, pp. 268-278.[15] J.P. Guiver and N.K. Bose, “Polynomial matrixprimitive factorization over arbitrary coefficientfield and related result,” IEEE Transactions onCircuits and Systems, vol. CAS-29, no. 10, 1982,pp. 649-657.[16] R. Johannesson and Z. Wan, “A linear algebraapproach to minimal convolutional encoders,”IEEE Trans. Information Theory, vol. 39, no. 4,Jul. 1993, pp. 1219-1233.[17] R. Johannesson and Z. Wan, “Some structuralproperties of convolutional codes over rings,”IEEE Trans. Information Theory, vol. 44, no. 2,Mar. 1998, pp. 839-845.[18] J.S. Lim, “Two-Dimensional Signal and ImageProcessing,” Prentice-Hall, Eaglewood Cliffs,New Jersey, USA, 1990 ISBN:0-13-935322-4.[19] Z. Lin, “Notes on n-D polynomial matrixfactorization,” Multidimensional Systems andSignal Processing, vol. 10, no. 4, Oct. 1999, pp.379-393.[20] Z. Lin, “Further results on n-D polynomial matrixfactorizations,” Multidimensional Systems andSignal Processing, vol. 12, 2001, pp. 199-208.[21] A. Logar and B. Sturmfels, “Algorithms for theQuillen-Suslin theorem,” Journal of Algebra, vol.145, 1992, pp. 231-239.[22] S. Mahapakulchai and R. E. Van Dyck, “Design ofring convolutional trellis codes for MAP decodingof MPEG-4 imagery,” in Proc. IEEE Inter. Conf.Commun. (ICC 2001), June 2001.[23] J. L. Massey and T. Mittelholzer, “Convolutionalcodes over rings,” in Proc. 4th Joint Swedish-Soviet Int. Workshop Information Theory Gotland,Sweden, Aug. 27-Sept. 1, 1989, pp. 14-18.[24] H. Park, A computational theory of Laurentpolynomial rings and multidimensional FIRsystems, Ph.D. dissertation, Department ofMathematics, University of California at Berkeley,1995.[25] H. Park and C. Woodburn, “An algorithmic proofof Suslin's stability theorem for polynomial rings,”Journal of Algebra, vol. 176, 1995, pp. 277-298.[26] A.A. Suslin, “Projective modules over apolynomial ring are free,” Soviet Math Dokl., vol.17, 1976, pp. 1160-1164.[27] M. Vetteri and J. Kovacevic, “Wavelets andSubband Coding,” Prentice-Hall, EaglewoodCliffs, New Jersey, USA, 1995 ISBN:0-13-097080-8.[28] P.P. Vaidyanathan, “Multirate Systems and FilterBanks,” Prentice-Hall, Eaglewood Cliffs, NewJersey, USA, 1993.[29] L. Xu, J.Q. Ying and O. Saito, “Feedbackstabilization for a class of MIMO n-D systems byGröbner basis approach,” submitted forpublication.[30] D.C. Youla and G. Gnavi, “Notes on n-dimensionalsystem theory,” IEEE Trans. Circuits andSystems, vol. 26, Feb. 1979, pp. 105-111.21

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Distribution Automation in Deregulated Power MarketJovitha JeromeElectrical Power Engineering ProgramSirindhorn International Institute of TechnologyP.O. Box 22, Thammasat-Rangsit Post Office, Pathum Thani 12121, Thailand.โจวิตา เจโรมสาขาเทคโนโลยีพลังงาน สถาบันเทคโนโลยีนานาชาติสิรินธรตู ปณ. 22 ปทฝ. ธรรมศาสตร รังสิต ปทุมธานี 12121Abstract: The power business is moving into new territory with market deregulation. Deregulation of the power industryhas made power quality a distinguishing feature of distribution service. The distribution automation system (DAS) is thekey to address all these challenges to improve the operation of the distribution system and the quality of supply.Distribution Automation (DA) aims at conservation of energy, including reduction of consumption and losses in thedistribution and transmission circuits, reduction of peak load, improvement in the reliability and quality of service,deferral of new construction, and recovery of lost revenue. Prominent features of electrical distribution are radial or nearradial structure; multiphase, unbalanced, grounded or ungrounded operation; dispersed generation; multiphase, multimodecontrol distribution equipment; unbalanced distributed loads and extremely large number of branches/nodes. Inorder to perform the desired functions of distribution management system (DMS), a data acquisition system fordistribution networks is needed similar to the real time supervisory control and data acquisition system (SCADA) ofenergy management system (EMS) used for transmission networks. Unmanned substations have become an importantpart of distribution systems in many countries. DAS is essential for monitoring, control and efficient operation of thedistribution networks.Keywords: Distribution automation system, Deregulation, Load flow, Reactive power compensation, Networkreconfiguration, State estimation, Network observability, Bad data processing, Power Quality.1. IntroductionReal power losses in distribution systems in general arequite appreciable, constituting a major portion of theoverall power system losses. Power distribution systemsespecially in developing countries are characterized byincreased power losses, poor voltage profile, inadequatemetering, frequent failure of major equipment mainly dueto non-availability of their loading information and lackof proper monitoring and co-ordinated controls.Distribution networks have been enormously extended,probably with less attention paid to the optimum growth,to meet the rapidly growing demand. Distributionautomation (DA) is getting worldwide attention toovercome these problems.Some of the DA application functions are distributionpower flow, distribution state estimation, distributionshort circuit analysis, distribution fault location,distribution feeder reconfiguration, and distributionfeeder voltage and var control. However, the on-linepower flow and state estimation algorithms employed inEMS cannot be used in a DMS. This is because the basicstructure of the EMS on-line power flow and stateestimation is based on the assumptions which are notgenerally valid for distribution systems. The transmissionsystems are generally assumed to operate under balancedthree-phase conditions, and the network is a symmetricalthree-phase system that is fully described with its positivesequence network. The R/X ratio of distribution lines ingeneral is high. The fast decoupled models and solutionmethods based on the assumption that R/X ratio of linesare quite small, may fail to provide solution to thedistribution networks. The distribution systems alsorequire information of all the phasor quantities, as thesystem is generally unbalanced.2. A Literature Review of DistributionAutomationThe past few years have seen an increasing interest inDA with the hope that automation will ultimately leadto a more efficient and economic operation ofdistribution circuits (Narendranath et al., 1994).Rapid growth in industrialization and irrigation needshave resulted in an enormous increase in the demand forelectricity. All the three constituents of the power system,generation, transmission and distribution have increasedin size and complexity. Generation and transmission haveexpanded in a relatively planned manner using moderntechnology. Expansion of the distribution system, on theother hand, has been mostly in an unplanned way indeveloping countries. A typical DAS consists of remoteterminal units (RTUs) at various levels of the distributionnetwork. These are interconnected in a hierarchicalmanner by a suitable communication network. Acomputer at the master control station is typically at the22

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002top of this hierarchy. RTUs are interfaced to sensors,meters and control switches to monitor and control thedifferent equipment. RTUs at different levels may havedifferent capabilities depending on the need and cost.DAS must meet the needs of the customers and of thedistribution, transmission, and generation systems of thepower utility. Modern computer-aided DASs canimprove the operation of distribution system and thequality of supply. This provides direct benefit not only topower utilities and consumers, but also has a directimpact on the national economy (Gupta et al., 1996).The distribution automation system (DAS) is acombination of,• Real-time remote monitoring of key parameters ofthe operating conditions• Real-time detailed modeling of the operatingconditions of the distribution system and equivalentmodeling of generation, transmission, and customercomponents of the power system• Real-time optimization of the operating conditionsvia closed-loop control, and via advisory messages;remote control of breakers and switches, and ofsetting of local controllers (Markushevich et al.,1994).Advanced applications fall into two main categories,1. Applications that define and analyze thedistribution system’s current operating conditions.2. Applications that provide recommendations forimproving the reliability, security, and efficiencyof the current and future operating conditions.Advanced applications that are designed to define andanalyze the current operating conditions are:1. Distribution state (load) estimation, whichestimates the current operating status of adistribution feeder by using limited real-timesupervisory control and data acquisition (SCADA)data and historical load data.2. Distribution power flow, which analyzes what-ifscenario studies, based on real-time saved cases.3. Distribution short circuit analysis, which calculatesthree-phase, unbalanced fault currents.4. Distribution fault location, which determinespossible fault locations, based on the fault sensorsignals and fuzzy logic algorithm.Advanced applications that are designed to makerecommendations to improve the reliability, security,and the efficiency of the current operating conditionsare:1. Distribution feeder reconfiguration, whichrecommends the optimal open/closed status ofswitches to isolate faults, restore services, reducelosses, and eliminate feeder overload.2. Distribution feeder voltage and var control, whichrecommends optimal tap positions and capacitorstatus to minimize losses and improve voltageprofile (Shirmohammadi et al., 1996).Electric power quality can be defined as a measure ofhow well electric power services can be utilized bycustomers. Losses in the transmission and distributionsystem have come under greater scrutiny in recentyears, and certain types of power quality degradationresults in losses. A number of mitigation methods areavailable. Improving the performance of the powersystem by introducing automation reduces theoccurrence of faults and interruption and improvespower quality (IEEE power engineering review, 2001).The development of new DA applications isconsiderably wide spread nowadays. In this research,many components of DA application functions alongwith their interactions with one another have beendeveloped using C programming language.3. MethodologyA distribution automation system (DAS) aims for bettermanagement and control of the distribution networks. Anew and robust algorithm for network configuration,power flow analysis, reactive power compensation,state estimation, network observability analysis, baddata processing and fault analysis for application indistribution automation has been developed. Thismethod exploits the radial nature of the network anduses forward and backward propagation scheme toestimate the line flows, node voltages and loads at eachnode, based on the measured quantities. The proposedmethod has been tested to analyze several practicaldistribution networks of various voltage levels and alsohaving high R/X ratio of lines.3.1 Basic Block DiagramOne of the basic modules in DA is the state estimator andthe basic block diagram is given in Figure 1. Nodenumbers are ordered to generate proper parent-childrelation based on the network topology. Themeasurement values are checked. If the measurementsare not available then the values are either calculated orpseudo measurements provided. Accordingly appropriateindex is provided to the measurement values. Selectionsof pseudo measurements, filling of missing data,providing appropriate weightage are the functions of theobservability analysis algorithm. Backward propagationis used to calculate branch currents, branch flows and theaverage of calculated and measured branch flowsproviding weights. During the iterative process the baddata is detected and replaced by pseudo or calculatedvalues. Computation of errors between measured andestimated values, detection of bad measurements,replacement by pseudo or calculated values in the placeof bad measurements are the functions of the bad dataprocessing algorithm. Node voltages and load at each23

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002node are calculated during forward propagation, basedon branch currents and weighted average of calculatedand measured loads at each node. Voltage at each node iscomputed and the test for convergence is performed. Theabsolute errors of measured and calculated values of realand reactive power flows and injection are alsoperformed. Computation of branch losses, total losses,and quantity of unbalance in current and voltage is doneonce the program converges.Ordering of node numbers to generate properparent-child relation based on the networkFigure 1: Basic block diagram of state estimator.3.2 Iterative SchemeSelection of pseudo measurements, fillingof missing data, providing appropriateweightageBackward propagation to calculate branchcurrents, branch flows and average of calculatedand measured branch flows providing weightsCompute errors between measured and estimatedvalues, detect bad measurements, replace by pseudo orcalculated values in the place of bad measurementsForward propagation to calculate node voltages, load ateach node based on branch currents and weightedaverage of calculated and measured loads at each nodeNoTest for convergenceYesComputation of branch losses, totallosses, quantity of unbalance etcInitially the node voltage magnitudes are set to themeasured voltages if they are available. Otherwisevoltage magnitudes are set to 1.0 pu and voltage anglesare set to 0.0, -120, 120 degrees in phase A, phase B,phase C, respectively and also all the branch currents,powers (complex) are set to (0.0,0.0) pu. V (1) is thesource node and its value is assumed to be known. Alsoits angle δ=0 (taken as reference).3.3 Backward PropagationThe purpose of the backward propagation is tocalculate branch currents and then the branch flows ineach section. During backward propagation, voltagevalues are held constant and information about branchcurrents and averaged flows are transmitted backwardalong the feeder using backward walk. During thispropagation the load current is calculated assuming theload as the demand measurement in each node. In theexample network of Figure 2, the backwardpropagation starts from branch 8-5 and proceeds alongthe path 7-5, 6-4, 5-4, 4-2, 3-2 and 2-1. Table 1 givesthe Parent child relationship.2(2)4(1)(7)1 6(3) (4)25(2) Branch numberNode numberFigure 2: Sample feeder with new node numbers.Parent node 1 2 2 4 4 5 5Child node 2 3 4 5 6 7 8Table 1: Parent child relationship.3.4 Forward PropagationThe purpose of forward propagation is to calculate thevoltage and load at each node starting from the sourcenode of the feeder. The feeder substation source voltageis set to its measured value. During forward propagation,the branch currents are calculated based on the averagedflows, are used to calculate the nodal voltages and hencethe loads at each node.3.5 Convergence CriteriaThe steps outlined in backward and forwardpropagation are followed during each iteration ofvoltage computations. The convergence criterion isthat, voltage magnitudes of real and imaginary parts ofcomplex voltage at each node are compared with itsprevious iteration values. Therefore the voltagemismatch for j th node during k th iteration is given byfollowing equations.∆V k (j) = V k (j)- V k-1 (j) for a,b, and c phasesReal ⎟ (∆V(j))⎟ < eps, j ε all the nodesImag ⎟ (∆V(j))⎟ < eps, j ε all the nodes3If both the equations are satisfied the iterative process iscompleted. Once the voltages are estimated all the branchcurrents and the real and reactive power flows, losses,effect of unbalanced can be calculated. In addition tovoltage, the absolute errors of measured and calculatedvalues of real and reactive power flows, real and reactivepower injection in branch are checked.Change in the supply frequency, voltage and waveformoutside the normal range gives rise to power qualityproblems. Power quality problems solved at the service7(5)(6)824

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002entrance are economical. DA is the best solution tomonitor and control these parameters.4. Developed Algorithms for DistributionAutomation Application FunctionsDistribution networks are fed from alternativesources/substation feed-points for a reliable supply ofpower to consumers. In real time environmentdistribution network configuration changes dynamicallydue to switching. Real time network model depends onthe correctness of the network topology determined fromthe telemetered data. This research presents a networktopology processing (NTP) algorithm suitable fordistribution networks. A simple data structure fordistribution network connectivity information storage isproposed for efficient implementation of networktopology processing. The developed method has beentested on a large practical distribution network withseveral feeders (Thukaram et al, 1999a).An efficient load flow solution technique is required as apart of the distribution automation system for takingvarious control and operation decisions. A robust threephasepower flow algorithm is presented (Thukaram etal, 1999b). This method exploits the radial nature of thenetwork and uses forward and backward propagationtechnique to calculate branch currents and node voltages.The proposed method considers all aspects of threephasemodeling of branches and detailed load modeling.The merits of the method are, guaranteed convergenceeven for heavily loaded network with poor voltageprofile. The method has been tested on practicaldistribution systems with many feeders emanating fromthe grid substation with large number of nodes andbranches.The application of the proposed method was alsoextended to find optimum location for reactive powercompensation and network reconfiguration for planningand day-to-day operation of distribution networks(Jerome, 2001a).An efficient and robust state estimation solutionalgorithm has been presented (Thukaram et al, 1998).The algorithm is based on forward and backwardpropagation. The new methodology has been tested onsample systems to analyze distribution networks havinghigher R/X ratio of lines. The proposed method hasworked well regardless of the feeder r/x ratio while theconventional WLS method failed to give a solution inmost of the cases. Distribution state estimators (DSE)will also play a critical role in distribution managementsystem to estimate those real-time system states whichare unable to be obtained from the limited measurementinstruments in the distribution network. The success ofDAS largely depends on the availability of reliabledatabase of the control center and thus requires anefficient state estimation (SE) solution technique. Themethod estimates the line flows, node voltages and loadsat each node based on the measured quantities.Real-time control of the distribution system requires anestimate of the system states. Distribution, state estimatorestimates the current operating status of a distributionfeeder by using limited real-time SCADA data andhistorical load data. In modern energy managementsystem (EMS), a state estimation (SE) program processesa set of raw measurement data and provides a real-timeload flow solution which is the basis of the advancedfunction for system security monitoring and control. SEis based on the mathematical relations between thesystem state variables (node magnitudes and angles) andthe measurements. As the automation of powerdistribution progresses, it will become necessary to applystate estimation techniques as part of the DAS. SEsuitable for unbalanced three-phase radial distributionnetworks has been developed and tested on practicaldistribution network and presented (Thukaram et al,2000a).The SE cannot be executed without an adequate numberof measurements. The extension of the method to thenetwork observability analysis and bad data detection isalso discussed. The proposed method has been tested ona few sample and practical distribution networks withsimulated data for real-time measurements (Jerome,2001b).Unlike in transmission system, distribution networks maynot be provided with protective devices or circuitbreakers in each branch of the feeder. Although RTUsmay be installed at various nodes/branches of the feederfor various measurements, circuit breakers may be onlyat the substation/switching station in the network. For anyfault in the feeder, a large part of the feeder, may beisolated depending on the circuit breaker installation. Forthe purpose of speedy repair work and maintenance, it isimportant to find the exact fault location and type offault. An algorithmic approach for finding the locationand type of fault based on the three phase measurementsobtained for state estimation is presented. Results of thesimulated fault conditions on practical distributionsystems are also presented (Thukaram et al, 2000b).The advent and widespread use of high-powersemiconductor switches at the utilization, distribution andtransmission levels have made non-sinusoidal loadcurrents more common. Power quality engineering hasnow become a subject of more focused interest.Distribution Automation can improve the quality ofpower (Jerome, 2001c). When wave shapes are irregular,voltages are poorly regulated, harmonics and flicker arepresent, and then power utilization will be degraded.Power Quality enhancement trends has become a timelytopic in power engineering (Jerome, 2002)25

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20025. ConclusionsDistribution automation will play a vital role inderegulated power market to provide reliable, economic,secure, stable and quality power. The algorithmsdeveloped and presented in this research work can bebroadly grouped as follows:• Optimal ordering of nodes and distribution networktopology processing• Three-phase network and load models, power flowanalysis, network reconfiguration and reactive powercompensation studies• Three-phase state estimation, bad data detection andnetwork observability analysis• Three-phase fault detection analysis• Power Quality enhancement using distributionautomationAn efficient load flow solution technique is required as apart of the distribution automation system for takingvarious control and operation decisions. A new, simplerobust three-phase power flow model and solutionalgorithm is presented. The proposed method is extendedto reconfiguration and reactive power compensation. Anefficient three-phase state estimation algorithm forapplication to radial distribution networks is presented.The SE cannot be executed without an adequate numberof measurements. Due to the radial nature of thedistribution system in some cases, it may be possible togenerate the required number of data using the availablemeasurements. The proposed method is extended to thenetwork observability analysis and bad data detection.An algorithmic approach for finding the location andtype of fault based on the three-phase measurementsobtained for state estimation is also presented.Several 11 kV sample systems of 12 node, 18 node, 19node, 28 node, 38 node and a 132/33 kV practical systemwith 7 major feeders, nearly 1000 nodes, severalswitching stations/feed-points are used for testing theproposed algorithms in this research. Results of thestudies indicate that the developed algorithms aresuitable for application to practical systems. Thealgorithms have no convergence problems. Thecomputational time is fast and suitable for real timeapplications.ReferencesGupta R. P, Gopesh Tiwari, P. V. K. Reddy, R. K.Varma, and T. V. Prabhakar (1996). “An Approachfor Development of Distribution Automation6. Scope for Future ResearchIn light of the present work, further investigations can becarried out in the following related areas:6.1 Meshed NetworksThough distribution networks are generally operated asradial, there is tendency to operate as meshed networks.Extension of the proposed methods to meshed networksis worth investigating. Use of FACTS devices indistribution network is also gaining importance.6.2 Deregulated Power SystemsThe international electric utility industry is undergoing aradical transformation from essentially regulated as amonopolistic industry to an industry made uncertainwithin impending deregulation and the advent ofcompetitive forces. This calls for additional functionsinvolving techno-economical aspects in the distributionsystems operation. Captive power plants, specificindustrial loads, custom power, power quality relatedaspects are to be addressed.6.3 Artificial Intelligence (AI) TechniquesExpert systems, Heuristic techniques, Artificial NeuralNetworks (ANNs) and fuzzy logic techniques areemerging in the field of artificial intelligence (AI)techniques. Some of the analytical functions of DMSmay not perform properly in the absence of adequatereliable data. In such cases expert systems/heuristicalgorithms are likely to provide an acceptable level ofsolution. Using more realistic models, training patternscan be generated, which can be used to train the NeuralNetworks for application in DMS.The electric power industry is in the midst of a majorrestructuring in which electric energy will be traded as acommodity. Electric power markets will foster openaccess to all suppliers of electric power. Discriminationagainst any user of transmission system will be reducedor eliminated. A competitive wholesale market at thenational level will be fostered to reduce prices and acompetitive retailed market at the state level will beencouraged to provide customer choice and competitionin service and reliability. Ultimately, small customerswill be able to choose their electric suppliers much asthey currently select their long-distance telephonecarriers.Software” Ninth National Power Systems Conference(NPSC’96), Vol. 1, pp. 49-53.26

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002IEEE Power Engineering Review on Power Quality,September 2001, Vol. 21, No. 9, pp. 5-11.Jerome Jovitha (2001a). “Distribution NetworkReconfiguration and Reactive Power Compensation”,Proceedings of the 2001 Large Engineering SystemsConference on Power Engineering (LESCOPE 01),11-13 July 2001, Halifax, Nova Scotia, Canada, pp.117-183.Jerome Jovitha (2001b). “Network Observability andBad Data Processing Algorithm for DistributionNetworks”, Proceedings of the IEEE PowerEngineering Society 2001 Summer Meeting, 15-19July 2001, Vancouver, BC, Canada, pp. 1692-1697.Jerome Jovitha (2001c). “Distribution AutomationImpact on Power Quality”, Proceedings of theInternational Conference on Power Quality SynergyAsia 2001, 14-16 May 2001, Bangkok, Thailand.Jerome Jovitha (2002). “Power Quality EnhancementTrends”, Proceedings of the InternationalConference on Power Quality Synergy Asia 2002, 20-22 May 2002, Bangkok, Thailand.Markushevich S. Nokhum, Ivan C. Herejk and Ron E.Nielsen (1994). “Functional Requirement and Cost-Benefit Study for Distribution Automation at B. C.Hydro”, IEEE Trans. on Power Systems, Vol. 9, No.2, pp. 772-781.Narendranath Udupa, D. Thukaram, K. Parthasarathyand G. S. Raju (1994). “Computer Aided Algorithmsfor Distribution System Planning and Improvements”,Proceedings of VIII National Power SystemsConference (NPSC 94).Flow Method for Weakly Meshed Distribution andTransmission Networks”, IEEE Trans. on PowerSystems, Vol. 3, No. 2, pp. 753-762.Thukaram D., Jerome Jovitha and C. Surapong(2000a). “A Robust Three-Phase State EstimationAlgorithm for Distribution Networks”, ElectricalPower Systems Research, September 2000, Vol. 55,No. 3, pp. 191-200.Thukaram D., Jerome Jovitha and C. Surapong(2000b). “A Three Phase Fault Detection Algorithmfor Radial Distribution Networks”, Proceedings ofEleventh National Power Systems Conference(NPSC), Indian Institute of Science (I.I.Sc.), 20-22December 2000, Bangalore, India, pp. 376-382.Thukaram D., Jerome Jovitha, H. M. Wijekoon Banda,C. Surapong, S. C. Srivastava (1999a). “A RobustPower Flow Algorithm for Radial DistributionSystems”, Proceedings of the International PowerEngineering Conference (IPEC'90), 24-26 May 1999,Singapore, pp. 458-463.Thukaram D., Jerome Jovitha, H. M. Wijekoon Banda(1999b). “A Robust Three-Phase Power FlowAlgorithm for Radial Distribution Systems”,Electrical Power Systems Research, June 1999, Vol.50, No. 3, pp. 227-236.Thukaram D., Jerome Jovitha, H. M. Wijekoon Banda,S. C. Srivastava (1998). “A Robust State EstimationAlgorithm for Radial Networks”, Proceedings of the14th National Convention of Electrical Engineers onModern Trends in the Transmission Systems, IndianInstitute of Technology (I.I.T.), 20-22 December1998, Kanpur, India.Shirmohammadi Dariush, H. W. Hong, A. Semlyen andG. X. Luo (1988). “A Compensation-based Power27

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10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20024. Experimentation and ResultsThe objective of the pilot cutting experiments was thecalibration of the parameters involved in the inversekinematics. The inverse kinematics transforms the toolreference vector (x, y, z, i, j, k) fixed to the workpieceinto the machine coordinates X, Y, Z, A, B fixed to themachine frame. A parametric saddle surface designedfor a telephone set, which contains both convex(maximum) and concave (minimum) regions was usedas a case study to demonstrate the tool path simulationand error estimation and minimization. The experimentconstituted a basic test of how our graphic simulationsoftware would detect such kinematics errors, locate theproblem areas, as well as minimize the errors. Theparametric saddle surface is given below.Fig 6 illustrates the saddle surface produced by thevirtual machine without angle switching. Fig 7 showsthat the maximum error is at vertex number 272 and thevirtual machine automatically locates the vicinity of thelarge milling errors to determine the source vertex as268 and the destination vertices as 285. Note that, thelarge circle or loop represents the maximum error of thesaddle surface at vertex 272.⎡⎤⎢⎥⎢⎥⎢⎢20 u − 10⎥⎥P ( u,v)⎢⎥=⎢20 v − 10⎥⎢ ⎡⎤ ⎡⎤ ⎥⎢⎥⎢ − ⎢ 22− + − ⎥ − ⎢ 2220 ( u 0.3) ( v 0.3) 20 ( u − 0.7) + ( v − 0.7) ⎥⎢⎥ ⎢⎥ ⎥⎢ ⎣⎦ ⎣⎦ ⎥⎢e+ e⎣⎥⎦Fig 6. Error identified by the virtual machine for thesaddle surface.Fig 4 The saddle surfaceThe a-angle and b-angle are computed using machineinverse kinematics described in section 2. A and B axisare rotating simultaneously ranging from 0 to 360 andfrom 0 to -90 respectively. Fig 4 shows the requiredsaddle surface. Fig 5 shows the corresponding graphsof the a-angles and the angle adjustment. Note that theangle adjustment is required to eliminate sharpvariations of the rotation angles near minimum or themaximum of the surface. Note that although in manycases this requires an adjustment of B as well, the caseof the saddle surface implies that B ∈[-90,0]. Thereforethe adjustment is not required.Fig 7. Maximum error identified for the saddle surface.New angleJump from max to minrequires angle adjustmentOriginal angleFig 5. Angle adjustment for the saddle surface.Fig 8. Optimized tool path with minimum error of thesaddle surface.36

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10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Production and Inventory Control Systems in ThaiIndustries: Problems and Solutionsระบบควบคุมการผลิตและวัสดุคงคลังของอุตสาหกรรมไทย: ปญหาและวิธีแกไขPisal YenradeeIndustrial Engineering ProgramSirindhorn International Institute of TechnologyThammasat University, Pathumthani 12121, Thailandไพศาล เย็นฤดีสาขาวิศวกรรมอุตสาหการ สถาบันเทคโนโลยีนานาชาติสิรินธรมหาวิทยาลัยธรรมศาสตร ปทุมธานี 12121Abstract: This paper discussed production and inventory control (P&IC) problems of and the solutions for Thaiindustries. The P&IC problems include aggregate production planning, master production scheduling, and detailedproduction and purchasing scheduling related problems. The P&IC problems can be alleviated by developingappropriate P&IC system based on requirements and constraints of Thai industries, and applying some engineeringand management techniques to simplify manufacturing processes for improving effectiveness of the P&IC system.บทคัดยอ: บทความนี้ไดอธิบายปญหาของระบบการควบคุมการผลิตและวัสดุคงคลัง และวิธีแกไขปญหาในอุตสาหกรรมไทยปญหานี้แบงเปนปญหาดานการวางแผนการผลิตรวมการจัดตารางเวลาการผลิตแมบท และการจัดตารางเวลาการผลิตและการสั่งซื้อในรายละเอียด ปญหาของระบบการควบคุมการผลิตและวัสดุคงคลังสามารถบรรเทาลงไดดวยการพัฒนาระบบการควบคุมการผลิตและวัสดุคงคลังที่เหมาะสมกับความตองการและขอจํากัดของอุตสาหกรรมไทย และการประยุกตใชเทคนิคดานวิศวกรรม และการจัดการพื่อทําใหกระบวนการผลิตมีความซับซอนนอยลงซึ่งสงผลในการปรับปรุงประสิทธิผลของระบบการควบคุมการผลิตและวัสดุคงคลังKeywords: Production and Inventory Control, Aggregate Production Planning, Master Production Scheduling,Purchasing Scheduling, Finite Capacity MRP System, and Techniques for Improving Effectiveness.1. IntroductionProduction and inventory control (P&IC) is one of themost important functions and activities in all industries.The objectives of the P&IC activities are to determinethe most suitable production and inventory plans, andthen execute and control manufacturing processesbased on these pre-specified plans.The P&IC activities are facilitated and supported byP&IC systems. Examples of well-known P&ICsystems in Thai industries are Just-in-time (JIT),Materials Requirement Planning and ManufacturingResources Planning (MRP and MRP-II).The P&IC system can be divided into three subsystemsas shown in Fig. 1.Aggregate Production Planning (APP) aims todetermine the production quantity and inventory levelin an aggregate term, for example, tons, dollars, orproduction hours. The APP usually covers a 12 to 24Aggregate Production PlanningMaster Production SchedulingDetailed Production and Purchasing SchedulingFig. 1 P&IC Subsystems.month period. Data in the APP is usually monthlydata. The APP is greatly needed when the demand ishighly seasonal.Master Production Scheduling (MPS) aims todetermine production quantities and timing ofindividual end products. The planning horizon of theMPS should be longer than the cumulative productionand purchasing lead-time. The MPS is obtained bydisaggregating the APP.39

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Detailed Production and Purchasing Schedulingdetermines the schedule of producing sub-assemblies,assemblies, and components or parts, and the schedulefor acquiring purchased parts and raw materials. Theschedule is obtained by applying MRP logic or the pullsystem of Kanban.Thai industries face many P&IC problems from eachsubsystem. To solve the P&IC problems, we have tounderstand the problems and the ways to solve theproblems. Therefore, this paper summarizes P&ICproblems found in Thai industries and proposes somepossible ways to solve them.This paper is organized as follows. The P&ICproblems are discussed and the solutions are proposedin the next section. In Section 3, engineering andmanagement (E&M) techniques that can be applied toimprove an effectiveness of the P&IC systems arepresented and an appropriate sequence of applying theE&M techniques are proposed.2. P&IC ProblemsIn this section, the P&IC problems are discussedstarting from common problems of all subsystems, andfollowed by specific problems of each subsystem.Some possible ways to solve or alleviate the problemsare also proposed.2.1 Common P&IC Problems and SolutionsYenradee et al. (1999) conducted a mail survey of 204factories to identify factors that significantly affect thedegree of P&IC problems. The factors are: 1)complexity of production system, 2) appropriateness ofP&IC techniques, and 3) perceived source of P&ICproblems.The complexity of the production system is dividedinto 2 types, namely simple and complex. Thecomplex one is characterized by a long setup time andlarge production lot size, wide variety of products,complicated products having many components, andinsufficient production capacity of some processes.The simple one is the opposite.The P&IC techniques are considered appropriate whenthey are systematic, developed based on soundassumptions and theories, and adapted regularly basedon practical limitations.The perceived source of P&IC problems is related tothe perception of personnel in the company. They mayfeel that major causes of P&IC problems come fromexternal or internal sources. An internal one meanssome sections in the company but an external onemeans its suppliers and/or customers.The degree of encountered P&IC problems is indicatedby the seriousness of the following problems:Overstocking, understocking, overtime, purchasingwrong items, inappropriate purchasing quantity anddue date, producing wrong items, inappropriateproduction quantity and due date, wrong productionpriority, and unbalanced flow of materials.The effects of the factors on the degree of encounteredP&IC problems are summarized in Table 1. It can beseen that use of appropriate P&IC techniques results ina low degree of P&IC problems while inappropriatetechniques results in a high degree of the problems.This emphasizes the need of developing and applyingappropriate P&IC techniques for Thai industries.A simple production process results in a low degree ofP&IC problems even if the P&IC techniques beingused are inappropriate. This indicates that the simpleproduction process is easy to plan and control and doesnot require advanced P&IC techniques. Complexproduction system requires appropriate P&ICtechniques otherwise the degree of P&IC problemswill be high. Therefore, the companies should tryto simplify the production process, e.g., reducing setuptime and lot size, decreasing numbers of products andtheir components, and providing sufficient capacity toall processes. This result corresponds to that ofKrajewski et al. (1987), which indicates thatTable 1 Effects of the factors on the degree of encountered P&IC problems.Complexity ofproduction systemFactors affecting P&IC problemsAppropriateness of P&ICtechniquesPerceived source ofP&IC problemsDegree of encounteredP&IC problemsSimple Complex Appropriate Inappropriate Internal External Low High♦♦♦♦♦ ♦ ♦♦ ♦ ♦♦ ♦ ♦♦ ♦ ♦♦♦♦♦40

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002simplifying the manufacturing environment results insignificant improvements of the manufacturingperformances. Techniques for improving theeffectiveness of the P&IC system will be discussed inSection 3.When most personnel in the company believe thatcauses of P&IC problems come from external sources(suppliers and customers), the degree of P&ICproblems is high. This indicates that the perception ofpersons in the company is also important. If theyblame the customers and suppliers, none will try tosolve the P&IC problems created within theorganization. This perception should be altered. Weshould believe that the P&IC problems come frominternal sources and try to identify the causes of theproblem and solve them.2.2 P&IC Problems of the APP SubsystemsThe APP problems in Thai industries are summarizedas follows.1. APP is not performed explicitly andsystematically.2. APP is performed without explicitlyconsidering related costs, such as, regular andovertime labor costs of permanent andtemporary workers, inventory holding cost, andsubcontracting cost.3. The current policy of varying the productionrate and keeping inventory results in relativelyhigh total costs. This means that the currentpolicy is not optimal.4. Mathematical models for determining anoptimal APP are not applied.Techawiboonwong and Yenradee (2002) developed aspreadsheet APP model based on general requirementsand constraints of Thai industries. The model candetermine the optimal way of varying production rateand keeping inventory that minimizes total relatedcosts.The model was presented in a workshop jointlyorganized by the Federation of Thai Industries andSIIT. Most participants feel that the model can beapplied to the real cases of their companies if it isslightly modified. Thus, Thai industries cansignificantly improve the APP subsystem by using theproposed APP model.2.3 P&IC Problems of the MPS SubsystemThe MPS problems in Thai industries are summarizedas follows.1. The MPS and APP are developedindependently. Therefore, the APP and MPSmay not match each other.2. The mathematical model has not been appliedfor disaggregating the APP into the MPSconsidering related objective and constraints.Some well-known models, such as that of Haxand Meal (1975), Bitran et al. (1981) cannot beapplied to Thai industries since the objectiveand constraints are not the same.The author and his doctoral student are studying theseproblems and developing a workable model andmethod for determining the MPS based on the APP forThai industries.2.4 P&IC Problems of the Detailed Productionand Purchasing Scheduling SubsystemMany Thai industries apply MRP-II system formanaging the detailed production and purchasingplanning subsystem. The MRP-II system is veryeffective as a tool for performing inventorytransactions. The inventory transactions that the MRP-II system can manage very well are:1. Receiving customer orders.2. Issuing invoices and updating stock offinished products.3. Issuing purchase orders of raw materials andcomponents of the products.4. Receiving the purchased items based on thepurchase orders and updating stock of thepurchased parts.5. Issuing work orders for manufactured itemsand printing pick-lists for withdrawing therequired parts into the work orders.6. Withdrawing the required parts into the workorders. They will become the work-inprocess.7. Completing the work orders to receive themanufactured parts to stock and reducing thework-in-process level of the required parts.8. Checking stock of all items and makingadjustments if it is necessary.If any inventory transaction stated above is notperformed correctly, accurately, and promptly, the dataof the system will be inaccurate. When the data isinaccurate, the MRP-II system will not be able togenerate reliable detailed production and purchasingschedules.Even if the data of the MRP-II system is accurate, itmay not be able to generate reliable production andpurchasing schedules because of the following reasons.1. The MRP-II system assumes a fixedproduction lead-time regardless of the lotsize, load on work centers, and priority ofjobs.2. The MRP-II system assumes a fixedproduction route of each item in capacityplanning. Alternative routings are notconsidered. Thus, heavy load on the firstpriority machine cannot be allocated to thesecond priority machine with light load.41

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20023. The detailed production plan is generated byassuming that all work centers have unlimitedcapacity. Capacity checking is performedafterward. When a capacity problem isevident, the planner has to solve it manually.There is no automatic routine to avoid thecapacity problem.There are many attempts to modify the MRP-II systemto be able to generate reliable production andpurchasing schedules by considering finite capacity ofkey work centers. Pandey, Yenradee, andArchariyapruek (2000) developed a finite capacitymaterial requirement planning (FCMRP) algorithm,which is executed in two stages. First, capacity-basedproduction schedules are generated from the input data.Second, the algorithm produces an appropriate materialrequirement plan to satisfy the schedules obtained fromstage 1.The author and a doctoral student are developing analgorithm and a computer program to determine theproduction and purchasing schedules considering finitecapacity of the bottleneck work centers based on theschedules generated by the MRP system. Alternateproduction routes are considered and some jobs on thebottleneck may be allocated to the non-bottlenecks.The excessive loads in some periods on the bottleneckwill be shifted earlier or later to smooth the loads.Finally, the production schedules of other items, whichare not produced on the bottleneck, and the purchasingschedules of all items will be revised accordingly. Thisprogram is appropriate for small industries facingcapacity problems.3. Techniques for Enhancing Effectivenessof P&IC systemsYenradee and Dangton (2000) summarized engineeringand management (E&M) techniques that can be used tosimplify the production process and also to improve theperformances of the P&IC systems. The E&Mtechniques are listed in Table 2. The details of theE&M techniques can be obtained from Bartezzaghi andTurco (1989), Fearon et al. (1989), Voss andClutterbuck (1989), Wantuck (1989), Harrison (1992),Schonberger (1992), Hernandez (1993), Hobbs (1994),and Stein (1997).Basically, an E&M technique may influence (i.e.support or be the prerequisite of) other techniques, andat the same time it may also be influenced by others.Because the E&M techniques are interrelated, theyshould be implemented following a systematicsequence. Following a wrong implementationsequence not only results in excessive implementationtime but also degrades the performance (e.g. inventorylevel, customer service, and productivity) of themanufacturing system. It is thus essential that weimplement the E&M techniques according to theappropriate sequence.Table 2 Sequence of implementing E&M techniquesTiming ofImplementationFirstSector 1SecondSector 2ThirdSector 3Any timeSector 4LastSector 5E&M Techniques1.1 Lifetime employment1.2 Training and education1.3 Suggestion and QCC2.1 Product modularization2.2 Parts standardization2.3 Quality function deployment2.4 Equipment selection2.5 Setup reduction techniques2.6 Method analysis2.7 Supplier evaluation and selection2.8 Reduction of sources of supply2.9 Supplier audit2.10 Supplier training and development3.1 GT and flow based layout3.2 Total preventive maintenance3.3 Process flow chart3.4 Less than capacity scheduling3.5 Leveled MPS3.6 Low season promotion3.7 Five S’s (Orderliness, cleanliness,arrangement)3.8 Fishbone analysis3.9 Dedicated capacity from suppliers3.10 Attitude changes3.11 Long term purchasing contract3.12 Freight consolidation3.13 Timely communications betweensuppliers and companies4.1 Design tools4.2 Value engineering4.3 Standard container and bar codes4.4 Autonomous defect control andfoolproof devices4.5 Failure mode effect analysis4.6 Taguchi design of experiments4.7 Statistical process control5.1 Mixed model scheduling andoverlapping of batches5.2 Pull control, Kanban5.3 Set small lot sizes5.4 Set short lead-times5.5 Set small safety and anticipationstocks5.6 Total quality control, company wideQCYenradee and Dangton (2000) determined theappropriate sequence of implementing the E&Mtechniques by firstly analyzing direct relationshipsamong the techniques. Then, overall direct andindirect relationships are determined using a procedurebased on max-min fuzzy composition. The E&Mtechniques are classified into five sectors based on theoverall direct and indirect relationships, and presentedin Table 2. The recommended sequence ofimplementing the E&M techniques is Sectors 1, 2, 3,and 5, respectively. Those in Sector 4 can beimplemented at any time (see Table 2).Steps for improving the effectiveness of P&IC systemsare explained as follows. First, some E&M techniques,which are possible to be implemented, are selectedfrom the list in Table 2. Second, implement the E&M42

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002techniques following the recommended sequence, thatis Sectors 1, 2, 3, and 5, respectively. The techniquesin Sector 4 can be implemented when it is convenient(any time). This will make the implementation processprogress smoothly since the techniques that support (orare prerequisite of) others have been alreadyimplemented.4. Recommendations for FurtherResearch and DevelopmentAs mentioned in Section 2.3, Thai industries still havea problem of disaggregating the APP into the MPS.Some mathematical models for this purpose should bedeveloped by considering real objective and constraintsof Thai industries.The MRP-II system has a drawback in that it cannotgenerate reliable production and purchasing scheduleswhen a bottleneck exists in a shop floor. The finitecapacity MRP (FCMRP) system is more appropriate tohandle this situation. From the fact that industries withdifferent requirements may need different FCMRPalgorithms, the FCMRP system with a number ofselectable algorithms should be developed based onvarious requirements of Thai industries.ConclusionsThis paper discusses P&IC problems in each subsystemand proposes some possible ways to solve theproblems. There are many serious P&IC problems inThai industries and some techniques to solve theproblems are not easy and simple. It requires someengineering and management techniques to help solvethe problems. Practical methods for solving theproblems should be further developed based on thecontext of Thai industries, which is sometimesdifferent from that of other developed countries.ReferencesBartezzaghi, E., and Turco, F. 1989. The Impact ofJust-in-Time on Production System Performance: AnAnalytical Framework, International Journal ofOperations and Production Management, Vol. 9, No.8, pp. 40-62.Bitran, G.R., Haas E.A., and Hax, A.C. 1981.Hierarchical Production Planning: A Single StageSystem, Operations Research, Vol. 29, No. 4, pp.717-743.Fearon, H.E., Ruch, W.A., and Wieters, C.D. 1989.Fundamentals of Production/Operations Management,4 th ed. (New York: West Publishing).Hax, A.C., and Meal, H.C. 1975. HierarchicalIntegration of Production Planning and Scheduling.In Studies in Operations Management, Hax A.C.(ed.) North Holland, Amsterdam.Hernandez, A. 1993. Just-in-Time Quality: A PracticalApproach (New Jersey: Prentice Hall).Hobbs, O.K. (1994). Application of JIT Techniques ina Discrete Batch Job Shop, Production and InventoryManagement Journal, 1 st quarter, pp. 43-47.Krajewski, L., King, B.E., Ritzman, L., and Wong,D.S. 1987. Kanban, MRP, and Shaping theManufacturing Environment, Management Science,Vol. 33, No. 1, pp. 39-57.Pandey, P.C., Yenradee, P., and Archariyapruek S.2000. A Finite Capacity Material RequirementPlanning System, Production Planning and Control,Vol. 11, No. 2, pp. 113-121.Schonberger, R.J. 1992. Japanese ManufacturingTechniques: Nine Hidden Lessons for Simplicity(London: The Free Press, Collier Macmillan).Stein, R.E. 1997. The Theory of Constraints:Applications in Quality and Manufacturing, 2 nd ed.(New York: Marcel Dekker).Techawiboonwong, A., and Yenradee, P. 2002.Aggregate Production Planning Using SpreadsheetSolver: Model and Case Study, ScienceAsia, Vol. 28,No. 3.Voss, C. and Clutterbuck, D. 1989. Just-in-time, AGlobal Status Report, (UK: IFS Publications).Wantuck, K.A. 1989. Just-in-Time for America(Milwaukee: The Forum).Yenradee, P., Areekul, A., Panthueng, S., Ocha, W.,Chaisawangwong, T., Decharin, W, and Jumpasri, T.1999. Analyses of Production and Inventory ControlProblems in Thai Industries: A Survey, Proceedingsof the 1999 IE Network National Conference, pp.801-807.Yenradee, P., and Dangton, R. 2000. ImplementationSequence of Engineering and ManagementTechniques for Enhancing the Effectiveness ofProduction and Inventory Control System,International Journal of Production Research, Vol.38, No. 12, pp. 2689-2707.Yenradee, P., Oudheusden, D.L., and Tabucanon, M.T.1995. Sequence for Managing the Situational Factorsto Improve the Performance of Production andInventory Control System, International Journal ofProduction Research, Vol. 33, No. 12, pp. 3349-3366.Harrison, A. 1992. Just-in-Time Manufacturing inPerspective (UK: Prentice Hall).43

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Structural Design Optimization by Nature-InspiredOptimization Techniquesการหาคาเหมาะที่สุดในการออกแบบโครงสรางโดยเทคนิคการหาคาเหมาะที่สุดที่ไดรับแนวคิดจากธรรมชาติPruettha NanakornCivil Engineering ProgramSirindhorn International Institute of TechnologyP.O. Box 22, Thammasat-Rangsit Post Office, Pathumthani 12121, Thailand.พฤทธา ณ นครสาขาวิชาวิศวกรรมโยธา สถาบันเทคโนโลยีนานาชาติสิรินธรตู ปณ. 22 ปทฝ. ธรรมศาสตรรังสิต ปทุมธานี 12121Abstract: This paper reviews the potential of nature-inspired optimization techniques in solving structural designoptimization problems. As examples, two promising nature-inspired techniques are selected for detailed discussion. Theyare Genetic Algorithms (GAs) and the Ant Colony Optimization (ACO). In the paper, the concepts of the two techniques,which are inspired by real models in nature, are carefully explained and the implementations of the techniques forstructural design problems are also described. The discussion on the efficiency of both techniques is included as well.บทคัดยอ: บทความนี้ไดพิจารณาถึงศักยภาพของเทคนิคการหาคาเหมาะที่สุดที่ไดรับแนวคิดจากธรรมชาติในการแกปญหาการหาคาเหมาะที่สุดสําหรับการออกแบบโครงสราง โดยไดยกเอาเทคนิคที่ไดรับแนวคิดจากธรรมชาติสองเทคนิคที่มีแนวโนมดีมาพิจารณาเปนตัวอยางอยางละเอียด เทคนิคทั้งสองนี้ไดแกวิธีการพันธุการและการหาคาเหมาะที่สุดโดยอาณานิคมมด บทความนี้ไดอธิบายหลักการพื้นฐานของเทคนิคทั้งสองซึ่งไดรับแนวคิดจากตนแบบจริงในธรรมชาติ และกลาวถึงวิธีการนําเทคนิคทั้งสองไปใชสําหรับปญหาการออกแบบโครงสราง นอกจากนี้บทความยังไดรวมการพิจารณาเรื่องสมรรถภาพของเทคนิคทั้งสองไวอีกดวยKeywords: Structural Design Optimization, Nature-Inspired Optimization Techniques, Genetic Algorithms, Ant ColonyOptimization.1. IntroductionMain concerns of structural engineers when designingstructures include, among others, performance of thestructures as well as cost to construct them. Gooddesigners are actually those who can balance these twoconcerns properly. Giving too much weight on the costmay result in unsafe structures while too much concernon the performance may yield expensive structures. Thispredicament can be resolved by employing optimization.Unfortunately, this is something that is easy to say butdifficult to do. The biggest obstacle to achieve theoptimal structural design is the nature of the structuraldesign process itself. For example, to be able to designmembers of a steel structure, it is necessary to know theinternal forces of each member of the structure. Once theinternal forces of the members are known, the sectionalsizes and other details of the members can be computed.However, to be able to obtain the internal forces, priorknowledge of the sizes of the members is required. Tocircumvent this dilemma, the designer will have toestimate the sizes of the members first. After that, theassumed sizes will be used for the calculation of theinternal forces. The obtained internal forces will in turnbe used to calculate the appropriate sizes of the members.The process can be repeated until a satisfactory result isobtained. Although this technique can be considered asan optimization technique, it is a crude iterative process,which does not guarantee convergence and can be veryinefficient. The first estimation of the sectional sizes isthe most crucial step and it usually requires a great dealof experience. If the first estimation is far from theoptimal solution, there is a high possibility that the finalresult will be poor.Even though it is obvious that the popular process ofstructural design mentioned above is not efficient andthere are quite a number of other long-establishedoptimization techniques (see, for example, Rao 1996),these techniques are rarely used in the design process.The main reason is that these conventional techniquesare generally gradient-based optimization techniques.The gradient-based optimization originates from aproblem of finding the conditions that give themaximum or minimum value of a function in calculus.The concept is simple; the gradients of a function with44

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002respect to all variables of the function must vanish at thelocations of the minimum and maximum. To find theminimum, the search in each step is directed into adirection that has the strongest negative gradient until aminimum point where the gradients vanish is found. Tofind the maximum, a direction with the strongest positivegradient can be considered, instead. Since the gradientbasedtechniques employ calculus, they are generallysuitable for problems with continuous design variables.Unfortunately, structural optimization problems dealmostly with discrete design variables. For example, if thedesign variables are sectional sizes, only those sizesavailable in the market can be selected. Another problemwith the gradient-based techniques is that they search fora local optimum solution, not the global one. Thisdeficiency in the gradient-based techniques is inheritedfrom calculus. Vanishing of the gradients is in fact anecessary condition for local extremums but it isdefinitely not a sufficient condition for the globalextremum. As a result, the quality of the obtained resultcan very much depend on the starting search point. If thestarting point is close to a local optimum, the search maybe trapped in that local optimum.Recently, several new optimization techniques that arenot based on calculus have been proposed. Examplesinclude Genetic Algorithms (GAs), Simulated Annealing(SA), Ant Colony Optimization (ACO), etc. Many ofthese techniques are inspired by nature. Geneticalgorithms are in fact inspired by Darwin’s survival-ofthe-fittesttheory. The simulated annealing is inspired bythermal annealing of critically heated solids. The antcolony optimization mimics the foraging behavior of antcolonies in the real world. The inspirations of thesetechniques are in a sense optimization processesperformed by nature. With the current rapid advances inthe field of computer science, many natural behaviorshave been artificially simulated by computers. Theoriginal aim of the simulations is either to understandthese natural behaviors better or to actually inventartificial lives. The nature-inspired optimizationtechniques are, to a certain degree, byproducts of theseresearches. In the optimization field, these techniques aregaining popularity over the calculus-based techniquesdue to the fact that they are generally more robust thanthe calculus-based techniques, meaning that theirperformances are nearly problem-independent. Also,these techniques are more efficient than the calculusbasedtechniques in searching for the global optimum. Inaddition, they are suitable for problems with discretevariables; consequently, they are also suitable forstructural design optimization problems.This paper discusses two of these nature-inspiredoptimization techniques, i.e. genetic algorithms and theant colony optimization. The paper aims to demonstratethe potential of nature-inspired optimization techniquesin solving structural design optimization problems.These two techniques are selected because of theirstriking resemblance to their corresponding naturalmodels as well as their promising capability inoptimization. In this paper, only structural designapplications will be discussed.2. Genetic Algorithms for StructuralDesign OptimizationGenetic algorithms (GAs) are global probabilistic searchalgorithms inspired by the survival-of-the-fittest theory(Goldberg 1989). The algorithms have receivedconsiderable attention because of their versatileapplication in several fields (Grefenstette 1986, Goldberg1989, Deb 1995, Marcelin et al. 1995, Dawid 1999).GAs start their search from many points in search spaceat the same time. These starting points are usuallyselected randomly and known as the initial population.Through the consideration of fitness values of thesesearch points, which are given based on their merit, andthe randomized information exchange among the points,a new set of search points with higher merit is created.The process is then repeated until a satisfactory result isobtained. Since the technique utilizes information frommany search points at the same time, there is less chancefor the search to be trapped in any of local optimalpoints. Another distinguishing characteristic of GAs isthat the algorithms do not directly work with designvariables. Rather, they work with codes that represent thevariables. Generally, binary coding is used. Because ofthe discrete nature of coding, GAs are the perfect choicefor those problems with discrete variables.To understand the concept of GAs in structural designoptimization, it is advisable to begin with coding. Asalready mentioned, GAs employ codes to representdesign variables and consequently to represent designsolutions, which are in fact combinations of designvariables. Consider a truss structure shown in Figure 1.The structure has three members. Assume that the sectionof each member is to be selected from a list of fouravailable sections, i.e. SECT1, SECT2, SECT3, andSECT4. Since there are four available sections, a two-bitbinary string can be used to code them; i.e. 00=SECT1,01=SECT2, 10=SECT3, and 11=SECT4. A structurewith A 1 =SECT1, A 2 =SECT3, and A 3 =SECT1 can, forexample, be coded by using a six-bit binary string as 0010 00.To begin the calculation, the initial population will haveto be created at random. In the truss example shown inFigure 1, several six-bit binary strings can be randomlycreated to form the first population. Each six-bit stringrepresents one truss structure and is called an individualin the GA terminology. To be able to use the survival-ofthe-fittesttheory, it is necessary to define the fitness ofeach individual or each design. This can be done bytaking into account the admissibility and the objective45

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002A 2A 1Available sectionsItem1234SectionSECT1SECT2SECT3SECT4Code00011011A 3PSECT3SECT1SECT1SECT4SECT4SECT2SECT1SECT2SECT3(A) 00 10 00 (B) 11 00 01 (C) 01 11 10Fig. 1 Coding of a truss structure for genetic algorithms.function value of the design. For example, in sizingoptimization, designs with higher fitness are those thatsatisfy all design criteria and have small weight. With themeasure of fitness specified, the survival-of-the-fittesttheory can then be applied and the evolution process canbe artificially created.After the first population is randomly obtained, thefollowing three basic operators, i.e. reproduction,crossover, and mutation, will be performed. Thereproduction operator defines a process in whichindividuals are selected, based on their fitness, formating and subsequent genetic actions. Individuals withhigher fitness will have higher chance to be selected intothis mating pool. Consequently, highly fit individualscan reproduce and pass their genes to the next generationwhile less fit individuals simply disappear. Afterreproduction, the crossover operator is implemented. Inthe crossover operator, new individuals are created byexchanging information among the individuals in themating pool generated by the reproduction operator.There are many crossover operators available in theliterature (Goldberg 1989, Jenkins 1997, Camp et al.1998). Nevertheless, in most crossover operators, twoindividuals are selected at random from the mating pooland some portions of their strings are exchanged. Thetwo original strings are called parent strings and the tworesulting strings are called child strings. Figure 2 showsan example of one of the simplest crossover operators,namely the one-point crossover. In this type ofcrossover, a crossing site is randomly selected and allbits on one side of the selected site of the two strings areswapped to create two child strings. It is clear thatcrossover may yield better or worse child strings. To beable to adjust the degree of uncertainty of the crossoverphase, it is not necessary to use all individuals in themating pool in the operator. This is done by adjusting theprobability that crossover is performed (crossoverprobability). The last GA operator is the mutationoperator. The mutation operator changes from 1 to 0 andSECT3SECT1Randomly selected crossing site0 0 1 0 0 00 0 1 1 0 1SECT4SECT1SECT1Parent 1Child 1SECT2SECT4SECT4SECT21 1 0 1 0 11 1 0 0 0 0SECT1SECT2Parent 2Child 2SECT1Fig. 2 An example of the one-point crossover.46

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002vice versa in a randomly chosen bit. The operator is usedsparingly with a small probability (mutation probability).Mutation allows for the possibility that features that donot exist in both parent strings may be created andpassed to their children. After reproduction, crossover,and mutation, a new generation is obtained. The processis repeated until a satisfactory result is achieved.Conceptually, reproduction exploits available goodstrings while crossover and mutation explore newstrings. Exploitation and exploration must be kept inbalance in order to obtain good results.(A)Nest(B)NestFoodFoodThe application of GAs to structural optimization wasfirst employed by Goldberg (1989). The success of hiswork became the turning point in the field of structuraldesign optimization. Many studies have been done toimprove the efficiency of GAs for structural designoptimization problems (for example, Jenkins 1997,Rajeev and Krishnamoorthy 1992, Camp et al. 1998,Nanakorn and Meesomklin 2001). The application ofGAs to solve relatively large structural systems has alsobeen explored (Rajan 1995, Galante 1996). GAs havebeen well accepted among researchers as a high-qualitytechnique that is not difficult to implement and use.3. Ant Colony Optimization for StructuralDesign OptimizationAnt colonies can collectively perform complicated taskseven with a low intelligent level of each individual ant.One of the examples is the foraging behavior of ants. Anant colony is capable of finding the shortest path betweenits nest and a food source without using visual clues. Thiscapability of the colony is achieved by indirectcommunication between ants via the use of pheromone. Itis well-known that ants lay and follow pheromone trails.These simple trail-laying and trail-following mechanismsenable the colony to seek out the shortest path from allpaths that the ants in the colony have ever explored.Consider a colony of ants shown in Figure 3. In thefigure, it is assumed that there are two available paths ofdifferent distance between the colony’s nest and a foodsource from which ants may select. At the beginning, theants will select the two paths with equal probability,meaning that there will be approximately half of the antsselecting each path. Since the shorter path requires lesstime to complete, for the same amount of time, the antson the shorter path will be able to complete more rounds.As a result, the quantity of pheromone on the shorter pathgrows faster than on the longer one. Due to the shorterpath’s higher pheromone level, more ants will beprobabilistically attracted to the shorter path and lay evenmore pheromone on this path. Finally, the levels ofpheromone on the two paths will be so different thatvirtually all ants will select the shorter one.(C)NestFoodFig. 3 (A) At the beginning, ants select the longer and theshorter paths with equal probability. (B) Pheromone isdeposited faster on the shorter path. More ants select theshorter path. (C) Finally, all ants select the shorter path.It is important to note that pheromone trails establishedby ants do not last forever but rather they evaporate. Thispheromone evaporation is also an important mechanismsince it avoids too rapid a convergence towards a suboptimalpath. The three aforementioned mechanisms, i.e.pheromone trail laying, pheromone trail following andpheromone evaporation, can be artificially simulated bycomputers and constitute the Ant Colony Optimization(ACO) technique.Recently, the ACO technique is becoming popularamong researchers in the field of heuristic optimization(see, for example, a survey in Dorigo et al. 1999). Theproblem that seems to fit the technique naturally is thetraveling salesman problem (Dorigo and Gambardella1997). Nevertheless, the technique has been applied tovarious types of problem, such as the quadraticassignment problem (Talbi et al. 2001), the just-in-timesequencing problem (McMullen 2001), optimizationproblems for designing and scheduling of batch plants(Jayaraman et al. 2000), etc. The application of thetechnique in the field of civil engineering is still rare (see,for example, Abbaspour et al. 2001).The ACO technique has been developed forcombinatorial optimization problems. Most of practicalstructural design optimization problems consider onlysizing optimization, which is basically combinatorialoptimization. As a result, the ACO technique can beapplied to solve them. To this end, structural designoptimization problems under consideration have to beprepared in such a suitable way that the problems fit theACO technique. After that, a simple ACO algorithm canbe implemented. To understand the concept of the ACOfor structural design optimization, consider in Figure 447

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002A 1A 2A 31: SECT11: SECT11: SECT12: SECT22: SECT22: SECT21 2 3 4Nest3: SECT33: SECT33: SECT3Food4: SECT44: SECT44: SECT4A 3PA 1A 2 Available sectionsItem Section1234SECT1SECT2SECT3SECT4Fig. 4 An ACO approach for design optimization of a three-bar truss.the same truss structure as discussed in the previoussection. This sizing optimization problem is acombinatorial optimization problem and can be thoughtof as a foraging problem of an ant colony. As shown inFigure 4, an artificial nest and a food source can beestablished. In the figure, node 1 represents the nest andnode 4 represents the food source. The ants will have tomove from node 1 to node 4 by passing all other nodes inbetween. Between each pair of nodes, there are fouravailable sub-paths, representing four different availablesections for each design variable. The partial walk of theants between nodes 1 and 2 represents the selection forthe design variable A 1 , and the partial walks between thesubsequent nodes are for the subsequent design variables.For example, if an ant walks between nodes 1 and 2 onthe sub-path SECT1, and between nodes 2 and 3 on thesub-path SECT4, and finally between nodes 3 and 4 onthe sub-path SECT2, it means that this ant has selected atruss with A 1 =SECT1, A 2 =SECT4 and A 3 =SECT2.For the ACO to work, artificial ants will have to makemany artificial tours and they must obey the followingsimple rules; i.e.1) Ants will probabilistically select paths with higherlevels of pheromone. In other words, paths withhigher pheromone level will have a higher chance tobe selected by ants.2) The amount of pheromone laid by an ant on the pathwhich it has walked depends upon the quality of thepath. If the path is of high quality, the ant that haswalked the path will lay a large amount ofpheromone on the path. For structural designoptimization, a path is considered high quality if itrepresents an admissible structure with low weight.These two rules, though simple, are enough for thecolony to perform its task. The complete algorithm maybe summarized asTour=1;All_Ants_Select_Paths(Random);Calculate_Paths_Quality( );Find_The_Best_Ant_of_The_Tour( );Update_The_Best_Ant_of_All_Tours( );All_Ants_Lay_Pheromone( );For Tour=2 to N{All_Ants_Select_Paths(Pheromone_Based);Calculate_Paths_Quality( );Find_The_Best_Ant_of_The_Tour( );Update_The_Best_Ant_of_All_Tours( );Pheromone_Evaporation( );All_Ants_Lay_Pheromone( );}Note that, for the first tour where there is still nopheromone on any sub-paths, the random selection canbe used.It can be seen that the concept of the ACO is very simpleand its implementation is straightforward. Theperformance of the technique has been found to becomparable with that of GAs when used in structuraldesign optimization. Even though the technique is still inits infancy, its potential is apparent. As the application ofthe technique in the field of civil engineering, not tomention the field of structural engineering, is still rare, alot of improvements are to be expected.4. Concluding RemarksThis paper demonstrates the potential of nature-inspiredoptimization techniques in solving structural designoptimization problems. Two promising nature-inspiredtechniques are discussed; they are genetic algorithms(GAs) and the ant colony optimization (ACO). GAs areglobal probabilistic search algorithms inspired byDarwin’s survival-of-the-fittest theory. When GAs areused in structural design optimization, a population ofstructures is created and an evolution process under thesurvival-of-the-fittest rule is applied to the population.48

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002Similar to what really happens to lives in nature, betterstructures can be expected after many generations havepassed. The evolution process is controlled by threeoperators, i.e. reproduction, crossover and mutation. GAshave been used successfully in many different fields,including structural design. Compared with GAs, theACO is a newer technique and has not been used much inthe field of civil engineering. The ACO is inspired by theway ant colonies function in the real world. An antcolony is capable of finding the shortest path between itsnest and a food source by the use of pheromone. In fact,the task of finding the shortest path is achieved by usingthree basic mechanisms, i.e. pheromone trail laying,pheromone trail following, and pheromone evaporation.These three mechanisms constitute the ACO. When theACO is applied to structural design optimization, theoptimization problem under consideration is transformedinto a foraging problem of an ant colony. Each designsolution will be interpreted as a route that ants can use towalk from the colony’s nest to a food source. A betterdesign will be made to be equivalent to a shorter route.For structural design optimization, the ACO has beenfound to be comparable with GAs. Nevertheless, thetechnique is still new and much progress can beexpected.5. Recommendations for Future Researchand DevelopmentThe two natural-inspired optimization techniquesdiscussed in this paper, i.e. genetic algorithms and the antcolony optimization, are both global search algorithms.Their searches are considered global because bothtechniques consider many search points at the same time.This will naturally allow the search to avoid beingtrapped in any local optimal points. If the advantage ofexisting local search algorithms in fine-tuning localoptimal solutions is combined with the global searchcapability of these two techniques, even better resultsmay be expected. This idea can possibly be implementedby using genetic algorithms or the ant colonyoptimization to globally search for the neighborhood ofthe global optimal solution first. After that, a local searchalgorithm can be used to improve the solution obtainedfrom the global search.ReferencesAbbaspour, K. C., R. Schulin, and M. Th. vanGenuchten. 2001. Estimating unsaturated soilhydraulic parameters using ant colony optimization.Advances in Water Resources, Vol. 24, pp. 827-841.Camp, C., S. Pezeshk, and G. Cao. 1998. Optimizeddesign of two-dimensional structures using a geneticalgorithm. Journal of Structural Engineering, Vol.124, No. 5, pp. 551-559.Dawid, H. 1999. Adaptive learning by geneticalgorithms: analytical results and applications toeconomic models. New York: Springer.Deb, K. 1995. Optimization for engineering design:algorithms and examples. New Delhi: Prentice-Hallof India Private Limited.Dorigo, M. and L. M. Gambardella. 1997. Ant coloniesfor the travelling salesman problem. BioSystems, Vol.43, pp. 73-81.Dorigo, M., G. D. Caro, and L. M. Gambardella. 1999.Ant algorithms for discrete optimization. ArtificialLife, Vol. 5, No. 2, pp. 137-172.Galante, M. 1996. Genetic algorithms as an approach tooptimize real-world trusses. International Journal forNumerical Methods in Engineering, Vol. 39, pp.361-382.Goldberg, D. E. 1989. Genetic algorithms in search,optimization, and machine learning. Reading,Massachusetts: Addison-Wesley.Grefenstette, J. J. 1986. Optimization of controlparameters for genetic algorithms. IEEE Transactionson Systems, Man, and Cybernetics, Vol. 16, No. 1,pp. 122-128.Jayaraman, V. K., B. D. Kulkarni, S. Karale, and P.Shelokar. 2000. Ant colony framework for optimaldesign and scheduling of batch plants. Computers andChemical Engineering, Vol. 24, pp. 1901-1912.Jenkins, W. M. 1997. On the application of naturalalgorithms to structural design optimization.Engineering Structures, Vol. 19, No. 4, pp. 302-308.Marcelin, J. L., P. Trompette, and R. Dornberger. 1995.Optimization of composite beam structures using agenetic algorithm. Structural Optimization, Vol. 9,pp. 236-244.McMullen, P. R. 2001. An ant colony optimizationapproach to addressing a JIT sequencing problemwith multiple objectives. Artificial Intelligence inEngineering, Vol. 15, pp. 309-317.Nanakorn, P. and K. Meesomklin. 2001. An adaptivepenalty function in genetic algorithms for structuraldesign optimization. Computers and Structures, Vol.79, pp. 2527-2539.Rajan, S. D. 1995. Sizing, shape, and topology designoptimization of trusses using genetic algorithm.Journal of Structural Engineering, Vol. 121, No. 10,pp. 1480-1487.Rajeev, S. and C. S. Krishnamoorthy. 1992. Discreteoptimization of structures using genetic algorithms.Journal of Structural Engineering, Vol. 118, No. 5,pp. 1233-1249.Rao, S. S. 1996. Engineering optimization: theory andpractice. New York: John Wiley & Sons.Talbi, E. -G., O. Roux, C. Fonlupt, and D. Robillard.2001. Parallel ant colonies for the quadraticassignment problem. Future Generation ComputerSystems, Vol. 17, pp. 441-449.49

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10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002additional cuts (if applicable) into a single outputblock.However, such a strategy invokes a substantialincrease of the CL-points and consequently asubstantial increase of the machining time. Therefore,recent papers have displayed a number of sophisticatedmethods to optimize a zigzag or spiral patterncombined with techniques dealing with the geometriccomplexity of the workpiece, for instance, Ahmadi andMatsuo (1991), Altan et. al. (1993), Jeong and Kim(1999). Besides, there exists a variety of off-linemethods to generate a suitable non-uniform tool-path,for instance: the neural network modeling approach(Suh and Shin, 1996) and the Voronoi diagramtechnique (Jeong and Kim, 1999). However, a robustalgorithm to generate such complicated patterns is stillan open problem. On the other hand, the structuredzigzag/spiral-pattern is simple and robust and thereforesuitable to be embedded into conventional CAMapplications.3. Preliminary ExamplesGrid generation techniques are surprisingly welladaptedto tool-path optimizations, containing almostall the main ingredients of the tool-path planning. Thekinematics equations imply that the deviation of thetool from the linear trajectory increases with thevariation of the rotation angles. In turn, the rotationangles depend on the curvature of the required surface.Therefore, the grid adapted to the regions of largecurvature may produce a better surface. Consider asurface having sharp variations along a sinus shapedcurve (Fig.4 (a)). The corresponding adapted grid isdepicted in Fig.4 (b). The tool moves along thecurvilinear coordinate enhancing the quality of therequired surface. Roughly speaking, the machining isan interpolation endowed with adaptation to the regionsof large milling errors. Furthermore, the gridgeneration techniques are applicable to generate a toolpath in the case of complex boundary. Consider theexample of a complex shaped domain depicted in Fig.5 (a). First of all, note that such regions are not likelyto often appear in the practice of conventionalmanufacturing. However, Jeong and Kim (1999)address this domain as an example of complex pocketmilling which may not be solved by means of a regularzigzag pattern. Consequently, the solution proposed byJeong and Kim is based on the Voronoi diagramtechniques combined with the distance map algorithm.However, the grid generation technique enables us togenerate an appropriate zigzag tool path depicted inFig. 5(b). The grid is well adapted to the internal andexternal boundary. Furthermore, and of greaterimportance, the flexibility of the grid generationapproach allows adaptation to the regions where thehigh quality milling is required. Finally, it should benoted that the above examples are valuable from themethodological viewpoint, exemplifying the gridgeneration approach embedded into the frame work ofthe conventional tool-path optimization.4. Grid as an Optimizer of the Tool PathThe concept of grid contains almost all the mainingredients for tool-path planning, such as: adaptationto regions of large milling errors, conventionalzigzag/spiral patterns and constraints related to thescallop height. Moreover, in contrast to the standardtechniques characterized by local (point by point) errorestimates, the grid generator deals with a global spatialerror and consequently adapts all the CL-pointssimultaneously. In this section, a rigorous formulationof the optimization problem and the numerical methodis presented. Our optimization approach is based on aninterpolating surface comprising (by means of theinverse kinematics) the tool trajectories. Introduce a setof CL-points {(u,v) i,j } ,0 ≤ i ≤ N ξ , 0 ≤ j ≤ N η , being adiscrete analogy of a mapping from the “computationalregion” ∆={0 ≤ ξ ≤ N ξ , 0 ≤ η ≤ N η ,} onto the physicalregion defined in the parametric coordinate system(u,v). In other words, the set of CL-points (u,v) i,j is astructured curvilinear grid. Next, consider trajectoriesbetween the points (ξ i ,η ,j ),(ξ i+1 ,η ,j ) and(ξ i ,η ,j+1 ),(ξ i+1 ,η ,j+1 ), respectively denoted by T i+0.5,j (t)and T i+0.5,j+1 (t). An obvious change of variables t=(ξξi )/(ξ i+1 -ξ i ) combined with the standard blendinginterpolation technique produces a subsurfaceT ξ,i+0.5,j+0.5 (ξ,η) spanned onto the grid-cell {(u,v) i,j,(u,v) i+1,j (u,v) i,j+1 ,(u,v) i,j+1 }. The subscript ξ indicatesthat the movement of the tool is specified along thecurves ξ=const. The non-linear interpolating surfaceT ξ (ξ,η) is then composed from the sub surfacesT ξ,i+0.5,j+0.5 (ξ,η). A spatial deviation of the interpolatingsurface from the required surface is given byw ≡ w( ξ , η)= | S ( ξ , η ) − T ( ξ , η ) | .It is not hard to demonstrate that w is proportional tothe derivatives of the rotation angles. Therefore, thetool-path regarded as a discrete analogy of thecontinuous mapping {u(ξ,η), v(ξ,η)} may be adaptedto the curvature of the required surface. Furthermore,the optimization procedure is endowed with constraintsrelated to the required scallop height whichcharacterizes the errors between the consecutive tooltracks.The scallop height is evaluated by standardengineering procedures, based on the local quadraticapproximation (Koren, 1995). For instance, a flat-endcutter with a radius r requires that the distance betweenthe CL points belonging to neighboring tracks on thesurface satisfies ρ ≤ d , where d = d (h ) isthe maximum allowable distance h maxξmaxthe maximumprescribed scallop height (Makhanov, 1999). Therequired grid is constructed by minimizing thefollowing functional (Ivanenko, 1992; Ivanenko andMuratova, 2000).F ≡∫∫u(2ξ+ u2η)(1 + K2K2u) + ( v2∆ J 1 + Ku+uK ( u vvξ2ξη+ v2η+ u v )Kη2v)(1 + Kξ2v) +dξdη71

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002subjected to the conditionD ≡ d − ρ ≥ 0 ,2 2 2where ρ ≡ ρ ( ξ , η ) = xη+ yη+ z .J≡J(ξ,η)ηdenotes the Jacobian of the mapping, K≡K(ξ,η), thecurvature of the surface, the subscripts ξ,η, u and vdenote the partial derivatives. Furthermore, consider anapproximation of F introduced by Makhanov (1999).Denote the approximation by F h . Let D h be anappropriate approximation of D in the parametricdomain ∆. In order to solve the constraint minimizationproblem, the penalty functionI = λ p D ), and the grid-functionp∑i , ji , j(h i , jIh≡ Fh+ λpIpare defined, where λ ij are thepenalty coefficients, λ p a weight coefficient. p(D), D∈(-∝,0), is a convex decreasing function with p(D)→∝if D → -∝. The corresponding algebraic system issolved by the quasi Newtonian iterations designed insuch a way that the grid consists of the convexquadrilaterals. In order to construct the convex grid,positiveness of the discrete Jacobian is controlled. Seefurther details in Bohez, Makhanov andSonthipaumpoon, (2000) and Makhanov et. al. (2002).The penalty coefficients are computed by a specialiterative procedure (Makhanov, Vanderperre andSonthipaumpoon, 1999). The initial mesh is generatedby means of a marching method. Finally, in order toimprove the stability of the algorithm, a smoothingprocedure introduced in Makhanov (1999) is used.Example: A spiral tool-path embedded into a zigzagtool-path. This example demonstrates how to producea tool-path composed from segments corresponding todifferent types of motion. The tool-path will be adaptedto the curvilinear boundaries and to the three zones oflarge milling errors located inside the circular regionand at the left part of the workpiece. R min =63,h max =0.01. Furthermore, in order to prevent too smallof a distance between the CL-point located in theirrelevant regions (i.e. regions where the milling errorsare small) the following lower bound is imposed:−D ≡ ρ − dmin≥ 0 , ( u , v ) ∈ Ω ≡{( u , v ) : ω ( u , v ) < ε },where ε 1 is a small positive constant. Observe that theconstraint related to the scallop height is quitesignificant. Fig. 6 (a) displays a grid (147 iterations)constructed by an unconstrained minimization of F.Clearly, the grid is unacceptable, moreover h is about50 times more than the prescribed value. Thecomposed grid tool-path shown in Fig. 6 (b),175iterations, generated with ε 1 =10 -5 , d max =1.6, d min =0.6 isadapted to both prescribed constraints withp(D)=[min(D,0)] 2 , δλ=1, λ p =0.25. Clearly, the secondgrid-tool-path is well adapted to the region adjacent tothe large milling errors. The application clearlydemonstrates that too small (too large) steps are oftenside effects produced by an adaptation to the zones of1large milling errors. Our method satisfies theprescribed constraints by modifying the space steps inthe irrelevant regions. The number of the required stepsdoes not increase significantly. Therefore, the penaltyfunctions technique constitutes an essentialsupplementary measure to improve the properties ofthe tool-path. Finally, the scallop height constraintD≡d-ρ ≥0 has been replaced by D≡ d max -ρ ≥0 whichcould be rather restrictive in some applications.However, our further results show that the proposedtechniques are applicable to the workpieces withcomplex geometries comprising “islands”, boundarieswith sharp edges, as well as to the workpiecesrequiring a combined spiral-zigzag pattern.5. Practical MachiningA large series of surfaces has been tested by means ofthe proposed algorithm. A typical testing Bezier typesurface is shown in Fig.7 (a). The correspondingmachined part (steel) is displayed in Fig.7 (b). Theconvex-concave type surface is characterized bydeflection points along the diagonal. Consequently, therotation angle a varies in the full range from 0 to 360 0 .The real machining presented in Table 1 demonstratesa significant increase in the accuracy of milling. h Cdenotes the step for the rectangular pattern, δ A theaccuracy increase, R C , R A denote the roughness of theworkpiece produced by the conventional and theadaptive method, symbol * indicates that realmachining was not performed. Observe that theconstraint minimization techniques could produce agrid which actually does not decrease the error w, evenincrease it with regard to the rectangular tool-pathgenerated without regard to the constraints. Indeed, ourformulation represents the accuracy by the twocriterions w and d rather than solely by w.Table 1. Accuracy, roughness of the surfaceN CL h C ,mmδ A, ,% / mmR CµmR Aµm100 3.60 34 / 0.2600 * *400 1.80 41 / 0.0930 * *900 1.20 34 / 0.0580 34.8 17.31600 0.90 36 / 0.0410 14.3 6.63600 0.60 32 / 0.0260 5.9 4.36400 0.45 40 / 0.0180 2.6 2.1Therefore, the constraints related to h max maysubstantially affect the solution. Besides, the solutionto the minimization problem is not unique or may notexist. In practice the initial grid typically lies outsidethe feasible region and there is no prior knowledgewhether the set of grids satisfying the prescribedconstraints comprises at least one element.Consequently, the convergence is analyzed by meansof numerical experiments. Table 2, illustrates thecomputational complexity of the algorithm for a zigzagtool-path having 40 x 40 CL-points. ε A denotes the72

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10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002control approach are not easy to implement. Forexample, it is recommended that workers rotate amongworkstations to reduce their daily noise exposurelevels. Since there are numerous combinations of workassignments to consider, it is almost impossible for thesupervisor to find the optimal work assignmentsolution.An Excel-based computer program called “NoiseManager” is developed to assist safety practitioners topractically implement several recommendations basedon the administrative approach. Noise Manager can beused to evaluate existing noise conditions andrecommend practical solutions. It consists of eightmodules. The first four modules are for noiseevaluation and the last four are for noise management.The user can select the preferred module from themenu as shown in Fig. 190n ⎡ L j − ⎤D T = 12.5 C 25∑⎢ ⎥j(2)j=1⎢ ⎥⎣ ⎦3.2 Module 2 – Calculation of Noise Level andPermissible Duration at any LocationTo predict the combined noise level at any location,Noise Manager needs information such as the ambientnoise level, noise levels of all noise sources in the workarea, and the locations (in x and y coordinates) of thosenoise sources. The following formula can be used todetermine the combined noise level at location i ( L i ).L i =⎡n ( Lj−120)/10( 120)/10 10 ⎤Lab−10log ⎢10 + ∑ ⎥+1202⎢⎣j=1 dij⎥(3)⎦where d ij is the distance between location i and noisesource j, L j the noise level of noise source j, and L ab theambient noise level.If the worker is required to be at one location for theentire day, it is possible to determine the permissibleexposure time (T) at that location from the known noiselevel.−1−90⎡ L ⎤5T = 8(4)⎢2⎣⎥⎦Fig. 1 Noise Manager and its modules.3.1 Module 1 – Calculation of TWA and Noise Doseof an 8-hour DayA time-weighted average (TWA) is an index thatmeasures the amount of long-term noise exposure. Todetermine the 8-hour TWA, Noise Manager needs toknow the noise levels at the locations that the worker ispresent and the exposure duration at those locations. Itthen uses the following formula to calculate the 8-hourTWA (W) in dBA,3.3 Module 3 – Construction of Noise Contour Mapof a FacilityA noise contour map depicts noise levels at variouslocations in the work area. Usually, locations that havethe same noise level are connected together by thesame contour line. To analytically construct a noisecontour map, Eq. (3) can be used to determine thecombined noise level. Figure 2 shows an example of a3-dimensional noise contour map constructed by NoiseManager.W =⎡ ⎧90nLC ⎛ j − ⎞⎫⎤j16.61⎢ ⎪log ⎜2 5 ⎟ ⎪⎥+ 90⎢ ⎨∑ ⎬(1)j=1 8 ⎜ ⎟ ⎥⎢ ⎪ ⎝ ⎠⎪⎣ ⎩⎭⎥⎦where C j and L j are the exposure duration and thecombined noise level at workstation j, respectively.The daily noise dose (D T ), in percent, can also becalculated using the same data. The daily noise doseand the 8-hour TWA present similar information aboutthe noise condition (Nanthavanij 1998).Fig. 2 A noise contour map.78

10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 20023.4 Module 4 – Estimation of Ambient andMachine Noise LevelsThe important pieces of information that are verydifficult to obtain are the ambient noise level and thenoise levels of noise sources in the work area. This isbecause it is not practical to shut off all other noisesources so that the noise level of the given noise sourcecan be separately measured. Noise Manager allows theuser to conveniently estimate the wanted information.It uses a simple linear algebra approach to set up a setof linear equations in which the ambient noise andmachine noise levels are unknown variables. Theequations are then simultaneously solved to determinethe unknown variables.Note that in order to solve for the ambient noise leveland the noise levels of n sources, n + 1 known noiselevels are required. In practice, any locations in thework area may be chosen for noise measurements. It ishowever recommended that the locations that are farfrom the wall or corner be chosen to avoid the effect ofsound reflection.3.5 Module 5 – Determination of Minimax WorkAssignments (Heuristic)The minimax work assignments are the assignmentsdeveloped for m workers, n workstations, and p workperiods per day. Generally the assignment will specifythat “who will do what during when.” According toNanthavanij and Yenradee (1999b), the minimax workassignments are a set of work assignments that themaximum noise level that any of the workers receive isminimized. Noise Manager determines the minimaxwork assignments based on the heuristic approachdeveloped by Tarathorn and Nanthavanij (underreview). It can solve both balanced (m = n) andunbalanced (m > n) problems, and both unconstrainedand constrained problems.Figure 3 shows the data and the minimax workassignments determined by Noise Manager. When thenumber of workers is greater than the number ofworkstations, some workers will be idle in some workperiods. Also, note that the maximum noise exposurelevel may be beyond 90 dBA.The mathematical model of the complex workassignment model can be described as follows. Let a ijrepresent a component of a matrix A that definesworker-workstation feasible assignments, where a ij = 1if worker i can be assigned to workstation j, and a ij = 0Lj−901otherwise. The term 25is viewed as a noisepweight per period of a given workstation j (or w j ). Anyworker who is assigned to that workstation will alwaysreceive that amount of noise weight.Minimizesubject toZ⎡ ⎧ n p⎪ ⎫⎪⎤16.61⎢log10⎨ wx j ijk⎬⎥+90⎢⎣⎪⎩j= 1k=1 ⎪⎭⎥⎦nxijkj=1∑∑ ≤ Z, i = 1,…, m∑ ≤ 1 i = 1,…, m; k = 1,…, pm∑ x ijki=1m pxijki= 1k=1≤ 1∑∑ ≤ p j = 1,…, nx ijk ≤ a ijx ijk = 0, 1}j = 1,…, n; k = 1,…, pi = 1,…, m; j = 1,…, n; k = 1,…, pi = 1,…, m; j = 1,…, m; k = 1,…, pThe number of possible work assignments (orsolutions) is equal to (m!) p . For a small workassignment problem in which there are five workers(m = 5) and a workday is divided into four workperiods (p = 4), there are 207,360,000 assignments toconsider.Fig. 3 Input data screen and the work assignment solution.79

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10 Years of International Cooperation: FTI, Nippon Keidanren and TUSIIT Commemorative Publication, 2002σ x /σ b1.61.41.21.00.80.6L/B = 10L/B = 4L/B = 2.50.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5Position across top flange (m)Fig. 8 Longitudinal stress distributions across top flange atmid span section (load case: uniform load).Table 1. Effective Flange Width Ratio of SimplySupported Box Girders at Mid SpanFlange: 1x.01 mLWeb: 2x.01 m(a) Cross sectionP10x.01 m6x.05 m⎧1tf for I girderP = ⎨⎩75tf for box girder ⎭ ⎬⎫(b) Boundary conditionFig. 9 Configuration of I girder and box girder.L/B1042.5Effective flange width ratio (B e/B)Concentrated load Uniform load0.8780.9850.7360.9220.6170.796(a) I girder(b) Box girderFig. 10 Finite element models of I girder and box girder.4. Stress Singularity Problem at SharpGeometries of Thin-Walled SteelStructuresIn general, geometric discontinuities of thin-walledsteel structures may cause a complicated distribution oflocal stress, and often produce a significant increase instress. This increment of local stress due to the sharpgeometry is typically called stress concentrations. Instress analysis based on FEA, in order to guaranteeaccuracy of the results, fine element mesh and higherorder of element type such as shell or solid elementsare preferable around the sharp geometry. In most casesof thin-walled steel structures, FEA may give stresses atthe geometric discontinuities that are incredibly high,and the stress value tends to increase endlessly againstmesh size. In this section, the stress singularity of thinwalledsteel structure is investigated around sharpgeometry.4.1 Some Examples of Stress Singularity Problemin FEACantilever is widely used in thin-walled steel girders ofbridge structures. In this study, cantilever beam is usedto study the stress singularity problem at stressconcentrated area by means of FEA. In order todetermine the location where stress singularity occurs,thin-walled steel I and box girders, are studied.Longitudinal stress distributions at fixed-end support ofcantilever girders are investigated. Figure 9 and 10show the configuration and 3D finite shell elementmodels of I and box girder.4.2 Numerical ResultsFigure 11 and 12 present the longitudinal stressdistributions across top flange width of I girder and boxgirder, respectively. At fixed-end support, longitudinalstresses arise significantly at the web-flange junction aswell as at the outmost edges of top flange for I section,and at the corners of top flange for box section. Toinvestigate these rising stresses, the study on stressconvergence is performed in order to demonstratewhether these increasing stresses result from the stresssingularity problem in FEA. Figure 13 and 14 presentthe non-convergent nodal longitudinal stresses at thefixed-end support for I section (outmost edge of topflange and web-flange junction), and for box section(corners of top flange), respectively. The results atthese sharp geometries indicate that nodal longitudinalstresses seem to go up to infinity when element sizegoes down. It is confirmed by the results of nodallongitudinal stresses at various points away from a fixendcorner of box section as shown in Figure 15.Longitudinal stress x 10 5 (kgf/m 2 )9.08.07.06.05.04.03.00.0 0.2 0.4 0.6 0.8 1.0Position across top flange (m)Fig. 11 Longitudinal stress distribution across top flangeat fixed end of a cantilever I girder.97

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10 Years of International Cooperation: Federation of Thai Industries,Nippon Keidanren, and Thammasat UniversitySIIT Commemorative Publication, 2002Sirindhorn International Institute of Technology (SIIT)Thammasat University (TU), Pathum Thani, ThailandExecutive Editor: Professor Dr. Prida WibulswasChief Editor: On-Anong Suraniranat Assistants: Mo Mo TinEditors: David Chatham Ornanong ChoosuwanPaul V. NeilsonTharinee AnodardTerry Robert Avon