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In modern times, various empirical and theoretical methodologies have been proposed for the determination of undrained shear strength (USS) via the application of field tests, with particular emphasis on the evaluation of pertinent soil properties. Several techniques utilized in this area incorporate correlation assumptions that yield imprecise results. Moreover, conventional methodologies display a dearth of efficacy with respect to both temporal and financial resources. Through novel machine learning strategies that utilize the Random Forest model, this study aims to rectify the deficiencies of current approaches and achieve more accurate assessments of the undrained shear strength of fragile soils. Three meta-heuristic optimization techniques, including Manta Ray Foraging Optimization (MRFO), Weevil Damage Optimization Algorithm (WDOA), and School-Based Optimization (SBO), were used in this research to accomplish optimization goals. The models were trained using four designated intake parameters, which included liquid limit (LL), plastic limit (PL), overburden weight (OBW), and sleeve friction (SF). To evaluate the models, the last phase of the study utilized a set of five statistical metrics, which included R2, RMSE, MSE, U95, and WAPE. In general, it can be concluded that machine learning in the hybrid method has been shown to be an effective technique for predicting the USS.

来源平台：MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN