Engineering monitoring of rockfall hazards along transportation ...

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942 M. Lato et al.: Using mobile terrestrial LiDAR for rockfall hazard analysisFig. 11. Kinematic identification of possible rockfall failure modes present near mile 94 along the Algoma Central Railway. Topple andwedge failures are both identified as possible modes based on the LiDAR data analysis.4.1 Airborne and TITAN data: the ACR experiment,June 2008Fig. 12. Histogram illustrating the alignment comparison of TITANdata with helicopter data. The inset figure illustrates the alignmentof the helicopter data (shown in black) with the titan data (shownin white). As well, illustrated in the inset figure is the general lackof helicopter data collected along the vertical rockface, due to thelook-angle of the airborne laser. The two data sets are not manuallyaligned, rather they are simply viewed in a common space based ontheir individually collected geographic coordinate information.4 Data fusionTITAN data provides data ranging from track level to roughly30 m beyond the track. Although the maximum scanning distanceof TITAN is 200 m, along the ACR there is a denseBoreal forest and numerous outcrops that limit the visibilityof TITAN from the track outwards. There were however noinstances where known track-side hazards were occluded inthe TITAN dataset.The ability to align and fuse TITAN data with other datasources will allow larger areas to be collected via airbornemethodologies or detailed studies to be completed with highresolutionstatic systems.Aside from using TITAN along the ACR track, a “low-range”LiDAR system mounted on a helicopter was deployed togenerate a valley scale LiDAR model. The low-range airborneLiDAR systems data were collected between 200–300 m above ground level. Low-range helicopter systemsare designed to collect relatively dense data in comparisonto fixed-wing aerial systems. This was completed during thesummer of 2007, 12 months before the scanning of the ACRusing the TITAN system. The resultant point-cloud producedby the helicopter system contains roughly 10 pts/m 2 . The airbornedata were reported in two formats: un-edited and bareearth.A “bare earth” model consists of the points that aredetermined to lie directly on the earth’s surface. Points representingtrees, shrubs, and infrastructure, for example, areremoved from the data set.Fusion of the TITAN and Airborne bare earth data wasachieved with the 3-D functionality of ArcGIS (ESRI, 2008).There was no adjustment of either dataset; they were simplyaligned and merged based on their independently collectedlocation information. For computing efficiency, only 1/12 ofthe data generated by TITAN were used for modelling completedin ArcGIS.To determine the correlation between the TITAN and airbornedata sets, an ArcGIS TIN model was built from thebare earth airborne data. Using the ArcGIS spot elevationfunction, the elevation on this TIN model at each TI-TAN point location was determined and then subtractedfrom the actual elevation recorded by the TITAN equipment.This operation was performed for a 1 m wide, 1 km longstrip that roughly followed the centerline of the track. Theresults, illustrated by Fig. 12, show a good match betweenthe datasets, with a distribution of height differences centredabout a point about 1 cm below the airborne data (median=−0.133 m). The small bump around the 0.3 m division representsthe ability of TITAN to accurately collect data fromNat. Hazards Earth Syst. Sci., 9, 935–946, 2009www.nat-hazards-earth-syst-sci.net/9/935/2009/
M. Lato et al.: Using mobile terrestrial LiDAR for rockfall hazard analysis 943Fig. 13. (a) Raw TITAN data and raw airborne data collected alongthe ACR, the data is viewed in a common geographic space. (b)Raw TITAN data extracted from the combined model in Fig. 13a.the surface of the rails along the track surface. 91% of thedata lie within ±10 cm and 96% within ±15 cm. The curvealso shows a long positive tail likely caused by vegetation onthe track and overhanging vegetation caught by the sidewaysand upward looking TITAN sensors. Once it was determinedthat the two datasets agreed vertically, they were merged intoa single file. Vegetation was removed from the TITAN databy filtering any TITAN points that were more that 0.15 mabove the bare-earth airborne data. A 7 m wide strip runningparallel to the railway centerline was defined and the airbornedata within this strip was replaced by the filtered TITAN data(Fig. 13).This process was completed to establish that althoughthe data were collected by different equipment, at differenttimes, using different GPS-INS solutions, the data could beintegrated for engineering purposes.4.2 Fusing TITAN and static dataA segment of data collected by TITAN near Ottawa, Ontariowas used to evaluate the fusion of high-resolution static datawith TITAN data. The static data were not collected withGPS so the cloud had no global spatial properties. The datasets were aligned in true space using visible points of commonalityand best-fit algorithms in the PolyWorks softwarepackage, allowing the high-resolution data to assume true coordinatesbased on the information extracted from TITAN.Figure 14 illustrates the alignment. Error mapping was completedto assess the alignment and the datasets line up withan average error of ±3 cm. This is not a simple or logicalprocess because it involves the fusion of data collected atdifferent times, different resolutions, and most challengingly– using different data formats.Fig. 14. Fusion of high-resolution Leica HDS 6000 data with mobileTITAN data collected near Ottawa, Ontario – resulting in theproduction of a locally precise and globally accurate point cloud.4.2.1 Difference monitoringThe ability to fuse high resolution LiDAR data with TITANallows the inherent qualities of the high-resolution data tobe exploited. One significant advantage of high-resolutiondata (up to 10 000 pts/m 2 ) over TITAN data (practically upto 500 pts/m 2 ) is the ability to conduct small scale differentialmonitoring. Differential monitoring requires successivescans to be completed evaluating the same area, effectivelygenerating a 4-dimensional model (Donovan and Raza,2008). Figure 15 illustrates an example of a rockcut that wasscanned at two intervals. The first scan is the baseline datathat will be used to evaluate successive scans for apparentmovement or rock block release. Before the second scan wascompleted small rock blocks were manually removed fromthe face (hence only apparent differentiation is observed inthe lower section of the outcrop, where rocks were safely accessibleby the scanning team). The coulour ramp indicatesdistance from the nearest neighbor – comparing one scan tothe next (grey indicates no apparent differential movement).The amount of differential movement observed in this exampleis between 3 and 15 cm. The main assumption made inthis analysis is that the tie-points used to align the successivescans are in the same position during both data collection periods.If the entire rock slope has moved tie-points locatedaway from the rock face should be used.These results conclude that differential monitoring usinghigh-resolution LiDAR can enable the detection of smallrock block release (sub 15 cm). This is an extremely valuabletool for monitoring progressive failure in a given rockmass.TITAN data can be used for temporal modelling as long asthe detectable movement is greater than the known combinedglobal and local errors. This enables, in a best-case scenario,detection of roughly 30–50 cm edge length blocks.www.nat-hazards-earth-syst-sci.net/9/935/2009/ Nat. Hazards Earth Syst. Sci., 9, 935–946, 2009
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