Enhancing undrained shear strength prediction: a robust hybrid machine learning approach with naïve Bayes modeling

Abstract In geotechnical engineering, it is crucial to make sure that the undrained shear strength (USS) of soft, sensitive clays is accurately assessed. The accuracy in forecasting USS is pivotal for ensuring the structural integrity and stability of foundations and earthworks. Addressing this concern, advanced data-driven NB techniques are utilized to disclose the complex interactions of USS with basic soil parameters. This paper presents a novel methodology for the USS prediction in soft clays using machine learning techniques, and particularly it highlights the attention on the following five important input variables: pre-consolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL), and natural water content (W). These are selected in view of their well-understood impact on the USS. This study reports an innovative effort to use SHO and AOSM for the model's hyperparameter tuning, reducing heuristic methods and computationally expensive brute-force searches. This will provide a neat methodology for improving accuracy in USS predictions and maintaining the optimality of the model performance. The results, therefore, provide geotechnical engineers and researchers with considerable benefits. They give a sound basis that is data-driven for the assessment of USS in soft sensitive clays and advance the safety and stability of civil engineering projects.