The undrained shear strength of clay is an important index for the calculation of the bearing capacity of the foundation soil, the calculation of the soil pressure of the foundation pit, and the analysis of the slope stability. Therefore, the purpose of this paper is to conduct a comprehensive study of the combined use of machine learning with clay theoretical equations to estimate it. Under the Bayesian framework, the CatBoost algorithm (CatBoost–Bayesian) based on Bayesian optimization algorithm was developed to obtain the feature importance level of soil parameters affecting the undrained shear strength of clay, so as to adaptively couple the theoretical equation of undrained shear strength of K0 consolidated clay, which was derived from the modified Cambridge model. Then, the theoretical equation of undrained shear strength of the isotropically consolidated clay was established from the critical state of the clay parameters. Finally, it was illustrated and verified using the experimental samples of Finnish clay. The results indicate that the theoretical equation established by the overconsolidation ratio and effective overburden pressure parameters can well estimate the undrained shear strength of isotropically consolidated clays, and the parameter uncertainty can be considered explicitly and rigorously.

In engineering practice, properly characterizing the spatial distribution of soil liquefaction potential and induced surface settlement is essential for seismic hazard assessment and mitigation. However, geotechnical site investigations (e.g., cone penetration test (CPT)) usually provide limited and sparse data with high accuracy. Geophysical surveys provide abundant two-dimensional (2D) data, yet their accuracy is lower than that of geotechnical investigations. Moreover, correlating geotechnical and geophysical data can effectively reduce site investigation costs. This study proposes a data-driven adaptive fusion sampling strategy that automatically develops an assessment model of the spatial distribution of soil liquefaction potential from spatially sparse geotechnical data, performs monitoring of liquefaction-induced settlement, and integrates spatiotemporally unconstrained geophysical data to update the model systematically and quantitatively. The proposed strategy is illustrated using real data, and the results indicate that the proposed strategy overcomes the difficulty of generating high-resolution spatial distributions of liquefaction potential from sparse geotechnical data, enables more accurate judgment of settlement variations in local areas, and is an effective tool for site liquefaction hazard analysis.

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