Critical failure domain-informed smart sampling for slope stability assessment using Bayesian compressive sensing and reliability sensitivity analysis

Slope reliability critically depends on accurately modelling spatially variable soil properties. While borehole sampling provides essential data for modelling soil variability, conventional borehole optimization strategies typically focus on minimizing spatial uncertainty, without accounting for slope failure mechanisms. There is a lack of failure mechanism-informed tools for optimizing borehole locations and number while considering cross-correlated random fields. This study develops a data-driven sampling strategy that integrates multi-task Bayesian compressive sensing (BCS), Karhunen–Loève expansion, and reliability sensitivity analysis (RSA) to adaptively identify critical failure domains and optimize borehole planning. The framework introduces a nonparametric method for interpolating cross-correlated soil fields, incorporates a spatially continuous mean reliability sensitivity indicator (MRSI) derived from First order reliability method (FORM) to guide domain identification, and extends the analysis to multi-mode failure mechanisms through multi-point FORM. The optimal borehole locations are adaptively determined based on the principle of maximum reduction in cumulative MRSI. The proposed method is illustrated through both a simulated example and real-life investigation data. Results indicate that the proposed framework can automatically identify critical failure domains for slope reliability analysis, and the determined optimal sampling locations can significantly improve the accuracy of slope reliability analysis. Limitations regarding the model’s assumptions are discussed.