Report - PEER - University of California, Berkeley
Report - PEER - University of California, Berkeley Report - PEER - University of California, Berkeley
parameter k (the slope of the hazard curve when plotted in a log-log form) which is inessence a Jacobian insuring that the two net dispersions are reflecting uncertainty incommon (i.e., frequency) terms. As shown in Figure 3 the value of capacity randomor aleatory dispersion, β RC , in that example is about 0.37. Representative values forβ UC and β UH might be 0.5 and 0.35 in coastal California, while k there (for S a in themoderate period, here 1.5 seconds, and order 10 -3 hazard range) might be 2.3. For acase in which the best (median) estimate of the median, Cˆ , is 0.45g (as in Figure 3)and the mean estimate of the hazard at this level is 0.0025, the mean estimate of thecollapse limit state frequency is 0.0068 or (1.44)(1.9) = 2.7 times the (mean estimateof the) likelihood that ground motion exceeds the estimated median capacity. Theincrease reflects the indicated product of the effects of aleatory and epistemicuncertainty (respectively) in the capacity (Eq. 5). In this case the mean estimate ofthe hazard curve at 0.45g is only 6% larger than the median estimate due to the lowestimate of the β UH value; this 6% ground motion hazard uncertainty effect on themean limit state estimate will be larger in many locations and at lower hazard levelsof usual safety interest. On the other hand at non-coastal California or analogous highseismicity areas the slope k will typically be lower reducing the impact of the capacityuncertainties. The total epistemic uncertainty in the limit state frequency,2 2 2β λ = βUH+ k β , is about 1.45 and is dominated in this case by the secondLimit StateUCterm, reflecting the factors just cited and the high epistemic uncertainty we now faceas professionals trying to estimate the highly nonlinear, near-collapse regime, whichis governed by factors such as P-delta and post-peak force decay in the hystereticmodels of nonlinear elements.ACKNOWLEDGEMENTSResearch by my students and co-researchers such as P. Bazzurro, N. Shome, J.Carballo, N. Luco, D. Vamavatsikos, F. Jalayer, G.-L. Yeo, J. Baker, P. Tothong,Prof. G. Beroza, Prof. Yasuhiro Mori, and Dr. Iunio Iervolino have contributedimportantly to the results and conclusions reported here. Stanford Blume Centercolleagues and their students have been a constant source of stimulation, insights andresults. Their contributions and those of many researchers every where are notadequately reflected in the references below. These efforts have been supportedgenerously by the US National Science Foundation, through the U.S.-Japan Programand through PEER.REFERENCESDeierlein, G. (2004). Overview of a comprehensive framework for earthquake performanceassessment. Proceedings Inter. Workshop on Performance Based Design, Bled, Slovenia,June.51
Krawinkler, H., F. Zareian, R. Medina, and L. Ibarra. (2004). Contrasting performance-baseddesign with performance assessment. Proceedings Inter. Workshop on Performance BasedDesign, Bled, Slovenia, June.Miranda, E., H. Aslani, and S. Taghavi. (2004). Assessment of seismic performance in terms ofeconomic losses. Proceedings Inter. Workshop on Performance Based Design, Bled,Slovenia, June.Jalayer, F. (2003). Direct Probabilistic Seismic Analysis: Implementing Non-Linear DynamicAssessments. PhD Theses, Dept, of Civil and Environmental Engr., Stanford University.Iervolino, I., and C. A. Cornell. (2004). “Record Selection for Nonlinear Seismic Analysis ofStructures”, Accepted for publication, Earthquake Spectra.Baker, J., and C. A. Cornell. (2004b). A vector-valued ground motion intensity measureconsisting of spectral acceleration and epsilon. Manuscript in preparation.Carballo, J. E., C. A. Cornell. (2000). Probabilistic seismic demand analysis: spectrummatching and design, Report No. RMS-41, Reliability of Marine Structures Program,Department of Civil and Environmental Engineering, Stanford University.Kennedy, R. P., C. A. Cornell, R. D. Campbell, S. Kaplan, and H. F. Perla. (1980).Probabilistic Seismic Safety Study of an Existing Nuclear Power Plant, NuclearEngineering and Design, Vol. 59, No. 2, August, pp. 315-338.FEMA-SAC. (2000). Recommended seismic design criteria for new steel moment-framebuildings. Report No. FEMA-350, SAC Joint Venture, Federal Emergency ManagementAgency, Washington, DC.Baker, J., and C. A. Cornell. (2003). Uncertainty Specification and Propagation for LossEstimation Using FOSM Methods, Report 2003/07, PEER, Berkeley, CA, Nov. 2003Ibarra, L. (2003). Global collapse of frame structures for seismic excitations. Ph. D.Dissertation, Dept. Of Civil and Environmental Engineering, Stanford Univ., Stanford,CA, USA.Porter, K. A., (2004). Propagation of Uncertainties from IM to DM, Chapter 6, PEERPerformance-Based Earthquake Engineering Methodology: Structural and ArchitecturalAspects, Van Nuys Testbed Committee, Report in Progress. PEER, Berkeley, CA.Cornell, C. A., F. Jalayer, R. Hamburger, and D. Foutch. (2002). The probabilistic basis for the2000 SAC/FEMA steel moment frame guidelines, Journal of Structural Engineering, Vol.128, No. 4, pp. 526-533, April 2002.Jalayer, F., and C. A. Cornell. (2003). A technical framework for probability-based demandand capacity factor design (DCFD) seismic formats, Report 2003/6, PEER, Berkeley, CA,Nov., 2003Vamvatsikos, D., and C. A. Cornell. (2002). Incremental dynamic analysis, EarthquakeEngineering and Structural Dynamics, 31(3): 491-514, MarchLuco, N. et al. (2002). Probabilistic seismic demand analysis at a near-fault site using groundmotion simulations based on a kinematic source model,” Proceedings 7 th U.S. NationalConference on Earthquake Engineering, Boston, MA, July.Bazzurro, P., and C. A. Cornell. (2002). Vector-valued probabilistic seismic hazard analysis.Proceedings 7th U.S. National Conference on Earthquake Engineering. Boston, MA, July.Baker, J., and C. A. Cornell. (2004a). Choice of a vector of ground motion intensity measuresfor seismic demand hazard analysis, 13 th World Conference on Earthquake Engineering,Vancouver, Canada, August.Pagni, C. A., and L. N. Lowes. (2004). Tools to enable prediction of the economic impact ofearthquake damage in older RC beam-column joints, Proceedings Inter. Workshop onPerformance Based Design, Bled, Slovenia, June.52
- Page 18 and 19: exists to develop testing protocols
- Page 20 and 21: to be sent soon to the 28 members o
- Page 22 and 23: factor γ I is 1.4 or 1.2 for essen
- Page 24 and 25: i. The well-known relation µ θ -
- Page 26 and 27: γ s =1.15. Values less than 1.0 me
- Page 28 and 29: efore (factor α in Eq.(4)). Materi
- Page 30 and 31: the force demand from the analysis,
- Page 32 and 33: OVERVIEW OF A COMPREHENSIVE FRAMEWO
- Page 34 and 35: ground motion Intensity Measure (IM
- Page 36 and 37: 2.2 Simulation of Engineering Deman
- Page 38 and 39: describing the economic losses asso
- Page 40 and 41: practice the localized gravity load
- Page 42 and 43: Whereas financial and insurance org
- Page 44 and 45: AN OUTLINE OF AIJ GUIDELINES FOR PE
- Page 46 and 47: (7) a method of performance evaluat
- Page 48 and 49: where, T: natural period of structu
- Page 50 and 51: 6. DAMAGE AND LIMIT DEFORMATIONSThe
- Page 52 and 53: The limit inter-story deformations
- Page 54 and 55: DirectionX-directionY-directionSkew
- Page 56 and 57: HAZARD, GROUND MOTIONS AND PROBABIL
- Page 58 and 59: of events with [X1>x 1 , X 2 >x 2 ,
- Page 60 and 61: 2.4 Option C: Sufficient IMs: Estim
- Page 62 and 63: predictions and hence required samp
- Page 64 and 65: PEER has put forward PBSA methodolo
- Page 66 and 67: 3.2.1 A DCF Displacement-Based Form
- Page 70 and 71: POST-EARTHQUAKE FUNCTION OF HIGHWAY
- Page 72 and 73: ln( EDP) a b ln ( IM )= + (1)Probab
- Page 74 and 75: terms of global and local bridge pe
- Page 76 and 77: Figure 3. Bridge column component d
- Page 78 and 79: 5.2 Method B: MDOF Residual Displac
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- Page 84 and 85: location. Transverse reinforcement
- Page 86 and 87: 2.50.1000Spectral Accel. (g)2.01.51
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- Page 96 and 97: The survey stood on the condition t
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- Page 112 and 113: Peak Interstory Drfit Ratio0.120.10
- Page 114 and 115: Conditional Probability ofDamage St
- Page 116 and 117: Probability of Non-Exceedance10.80.
parameter k (the slope <strong>of</strong> the hazard curve when plotted in a log-log form) which is inessence a Jacobian insuring that the two net dispersions are reflecting uncertainty incommon (i.e., frequency) terms. As shown in Figure 3 the value <strong>of</strong> capacity randomor aleatory dispersion, β RC , in that example is about 0.37. Representative values forβ UC and β UH might be 0.5 and 0.35 in coastal <strong>California</strong>, while k there (for S a in themoderate period, here 1.5 seconds, and order 10 -3 hazard range) might be 2.3. For acase in which the best (median) estimate <strong>of</strong> the median, Cˆ , is 0.45g (as in Figure 3)and the mean estimate <strong>of</strong> the hazard at this level is 0.0025, the mean estimate <strong>of</strong> thecollapse limit state frequency is 0.0068 or (1.44)(1.9) = 2.7 times the (mean estimate<strong>of</strong> the) likelihood that ground motion exceeds the estimated median capacity. Theincrease reflects the indicated product <strong>of</strong> the effects <strong>of</strong> aleatory and epistemicuncertainty (respectively) in the capacity (Eq. 5). In this case the mean estimate <strong>of</strong>the hazard curve at 0.45g is only 6% larger than the median estimate due to the lowestimate <strong>of</strong> the β UH value; this 6% ground motion hazard uncertainty effect on themean limit state estimate will be larger in many locations and at lower hazard levels<strong>of</strong> usual safety interest. On the other hand at non-coastal <strong>California</strong> or analogous highseismicity areas the slope k will typically be lower reducing the impact <strong>of</strong> the capacityuncertainties. The total epistemic uncertainty in the limit state frequency,2 2 2β λ = βUH+ k β , is about 1.45 and is dominated in this case by the secondLimit StateUCterm, reflecting the factors just cited and the high epistemic uncertainty we now faceas pr<strong>of</strong>essionals trying to estimate the highly nonlinear, near-collapse regime, whichis governed by factors such as P-delta and post-peak force decay in the hystereticmodels <strong>of</strong> nonlinear elements.ACKNOWLEDGEMENTSResearch by my students and co-researchers such as P. Bazzurro, N. Shome, J.Carballo, N. Luco, D. Vamavatsikos, F. Jalayer, G.-L. Yeo, J. Baker, P. Tothong,Pr<strong>of</strong>. G. Beroza, Pr<strong>of</strong>. Yasuhiro Mori, and Dr. Iunio Iervolino have contributedimportantly to the results and conclusions reported here. Stanford Blume Centercolleagues and their students have been a constant source <strong>of</strong> stimulation, insights andresults. Their contributions and those <strong>of</strong> many researchers every where are notadequately reflected in the references below. These efforts have been supportedgenerously by the US National Science Foundation, through the U.S.-Japan Programand through <strong>PEER</strong>.REFERENCESDeierlein, G. (2004). Overview <strong>of</strong> a comprehensive framework for earthquake performanceassessment. Proceedings Inter. Workshop on Performance Based Design, Bled, Slovenia,June.51