Knowledge-Informed Machine Learning of Slope Stability with Consideration of Soil Property Spatial Variability at a Specific Site Using Limited Site Investigation Data

Abstract

Soil slope stability is profoundly affected by the spatial variability of soil properties. Although random field is a widely used tool for characterizing spatial variability in slope reliability analysis, its application to a real slope at a specific site is often constrained. The challenge lies in the difficulty of properly determining random field parameters from sparse and limited geotechnical investigation (GI) data (e.g., boreholes and test data) at a specific site. To tackle this challenge, this study proposes a novel knowledge-informed machine learning framework for slope stability analysis. The proposed framework is conditional on limited site-specific GI data and bypasses a need for estimation of random field parameters. The exploited knowledge includes geometry and soil stratigraphy of a target slope, typical statistics of soil properties, and physics-based model of slope stability analysis. The knowledge is then collectively represented by a series of simulation slopes with different spatial variability. Stability of the target slope is predicted by machine learning of similar simulation slopes identified by site-specific GI data. The proposed framework is illustrated through a numerical example and a real case history, demonstrating its ability to deliver accurate and efficient stability analysis for real slopes with consideration of spatial variability using limited GI data.