Backward automatic calibration for three-dimensional landslide models
Backward automatic calibration for three-dimensional landslide models作者机构:DICAM-UNIBODepartment of CivilChemicalEnvironmental and Materials EngineeringAlma Mater Studiorum University of BolognaViale Risorgimento 2Bologna40136Italy IRPI-CNRResearch Institute for Geo-Hydrological ProtectionNational Research CouncilC.so Stati Uniti4Padova35127Italy IMHE-CASKey Laboratory of Mountain Hazards and Earth Surface Process/Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesBlock 4Renminnanlu RoadChengdu610041China
出 版 物:《Geoscience Frontiers》 (地学前缘(英文版))
年 卷 期:2021年第12卷第1期
页 面:231-241页
核心收录:
学科分类:081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程]
主 题:Automatic landslide modelling FLAC3D~(TM) Back-analysis Optimization Decision Support System
摘 要:Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor *** landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error *** paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring *** method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured *** reduces the operator biases while on the other hand allows the operator to control each step of the *** final selection of the preferred solution among the ten best-fitting solutions is carried out by an *** operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system *** proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.