首页|Interpreting random fields through the U-Net architecture for failure mechanism and deformation predictions of geosystems
Interpreting random fields through the U-Net architecture for failure mechanism and deformation predictions of geosystems
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NETL
NSTL
万方数据
维普
The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability.In many studies,the safety margin of the system is typically characterized by the term"probability of failure(Pfailure)".As the intensity and spatial distribution of soil properties vary in different random field realizations,the failure mechanism and deformation field of a slope can vary as well.Not only can the location of the failure surfaces vary,but the mode of failure also changes.Such information is equally valuable to engineering practitioners.In this paper,two slope exam-ples that are modified from a real case study are presented.The first example pertains to the stability analysis of a multi-layer Su-slope while the second example deals with the serviceability analysis of a multi-layer c-φ slope.In addition,due to the large number of simulations needed to reveal the full picture of the failure mechanism,Convolutional Neural Networks(CNNs)that adopt a U-Net architecture is pro-posed to offer a soft computing strategy to facilitate the investigation.The spatial distribution of the fail-ure surfaces,the statistics of the sliding volume,and the statistics of the deformation field are presented.The results also show that the proposed deep-learning model is effective in predicting the failure mech-anism and deformation field of slopes in spatially variable soils;therefore encouraging probabilistic study of slopes in practical scenarios.
Department of Civil and Environmental Engineering,National University of Singapore,Singapore 117576,Singapore
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education and Department of Geotechnical Engineering,Tongji University,Shanghai 200092,China
国家自然科学基金China National Postdoctoral Program for Innovative TalentsShanghai Science and Technology Committee Program
NETL
NSTL
万方数据
维普
