Abstract Thick soft muddy clay layers in coastal regions pose significant engineering challenges due to their poor mechanical properties, threatening projects’ stability and safety. Accurate determination of soil parameters is essential for reliable geotechnical design. This study utilizes 178 soil samples from multiple projects in Zhoushan, China, to analyze the statistical distributions and probabilistic characteristics of key parameters, including water content, unit weight, void ratio, degree of saturation, and Atterberg limits. Focusing on shear strength as a critical indicator, the research investigates its relationship with various physical properties. By employing readily available physical parameters as inputs, a machine learning model combining Bayesian optimization and XGBoost (BOA-XGBoost) is developed to predict shear strength parameters. The BOA-XGBoost model achieved R 2 values of 0.859 for cohesion and 0.814 for the internal friction angle, with corresponding low error metrics, demonstrating superior performance and generalization capability compared to other models. This approach enables efficient and timely prediction of mechanical properties, offering practical guidance for the design and construction in soft clay areas.
Chen et al. (Mon,) studied this question.