Interpreting random fields through the U-Net architecture for failure mechanism and deformation predictions of geosystems

Abstract

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 (P_failure)”. 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 examples that are modified from a real case study are presented. The first example pertains to the stability analysis of a multi-layer -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 proposed to offer a soft computing strategy to facilitate the investigation. The spatial distribution of the failure 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 mechanism and deformation field of slopes in spatially variable soils; therefore encouraging probabilistic study of slopes in practical scenarios.

Keywords

Deep-learning / Spatial variability / Slope stability / Failure mechanism / Sliding volume

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