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Evaluation of rock fall Hazards Using Lidar Technology

Journal of Civil Engineering and Architecture 2015年第1期9卷 80-89页

作者： Norbert Maerz Travis Kassebaum Ken Boyko James Otoo Geological Engineering Missouri University of Science and Technology Missouri 6540 USA Terracon Consulting Engineers and Scientist Missouri 63055 USA Chevron North America Exploration and Production Texas 77002 USA

Lidar （light detection and ranging） is a relatively new technology that is being used in many aspects of geology and engineering, including researching the potential for rock falls on highway rock cuts. At Missouri University of Science and Technology, we are developing methods for measuring joint orientations remotely and quantifying the raveling process. Measuring joint orientations remotely along highways is safer, more accurate and can result in larger and more accurate data sets, including measurements from otherwise inaccessible areas. Measuring the nature of rock raveling will provide the data needed to begin the process of modeling the rock raveling process. In both cases, terrestrial lidar scanning is used to generate large point clouds of coordinate triplets representing the surface of the rock cut. Automated algorithms have been developed to organize the lidar data, register successive images without survey control, and removal of vegetation and non-rock artifacts. In the first case, we look for planar elements, identify the plane and calculate the orientations. In the second case, we take a series of scans over time and use sophisticated change detection algorithms to calculate the numbers and volumes of rock that has fallen off the rock face.

关键词： Lidar rock fall hazard rock cuts highway.

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The Volume Calculation Method of rock Collapses and Loess Landslides Triggered by the 1556 AD Huaxian M81/2 Earthquake in Shaanxi Province, China

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Earthquake Research in China 2020年第4期34卷 599-616页

作者： ZHANG Yanbo XU Yueren DU Peng LI Wenqiao CHEN Lize TIAN Qinjian Key Laboratory of Earthquake Prediction Institute of Earthquake ForecastingChina Earthquake AdministrationBeijing 100036China

Accurate volume calculation of each individual landslide triggered by strong historical earthquakes can help understand the characteristics of the typical earthquake-induced landslides,thus providing significant information for the modification of the focal parameters of historical *** this study,we select one rock fall and three loess landslides triggered by the 1556 AD Huaxian M8⅟􀄟earthquake,compute their volumes using the low-altitude high-precision Unmanned Aerial Vehicle(UAV)photogrammetry and landslide profile restoration *** results show that:①the whole influencing area of the Huangjiagou rock fall is approximately 3.03×105 m2 and the area of the collapsed rock accumulated at the slope foot is 3.33×104 m2,accounting for approximately 10%of the entire influencing ***,the estimated volume of the collapsed rock is only 0.699×106 m3,indicating a rock fall with large influencing range but limited collapsed rock;②the geological form of thethree loess landslides are preserved intactly,with volumes of 0.283×108 m3,0.074×108 m3,and 0.377×108 *** important geological hazard relics reflect the strong vibrations and severe casualties in the meizoseismal area;③loess landslides are the key reason of the serious death toll in the hilly-gully loess *** new method can be used to estimate the influencing area and the actual volume of each individual landslide,and rationally evaluate the role of earthquake landslides in the *** addition,quantitative research on secondary disasters triggered by strong historical earthquakes is beneficial for understanding the surface process and focal parameters of the earthquakes.

关键词： 1556 AD Huaxian earthquake rock fall Loess landslide Volume calculation

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Uncertainties of landslide susceptibility prediction considering different landslide types

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Journal of rock Mechanics and Geotechnical Engineering 2023年第11期15卷 2954-2972页

作者： Faming Huang Haowen Xiong Chi Yao Filippo Catani Chuangbing Zhou Jinsong Huang School of Infrastructure Engineering Nanchang UniversityNanchang330031China Jiangxi Provincial Key Laboratory of Interdisciplinary Science Nanchang UniversityNanchang330031China Department of Geosciences University of PadovaPadovaItaly Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleNSW2287Australia

Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction *** construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as ***,105 rock falls,350 colluvial landslides and 11 related environmental factors are *** four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial ***,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of ***,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard *** is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang *** united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility *** cases are needed to verify this result *** considering different landslide types is superior to that taking only a single type of landslide into account.

关键词： Landslide susceptibility Landslide type rock fall Colluvial landslides Machine learning models

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