Machine Learning-Based Prediction of Undrained Shear Strength in Marine Alluvial Clays: A Case Study of Bangkok

Accurate evaluation of undrained shear strength (Su) is crucial for the safe design of foundations and slopes in marine alluvial clays, including those commonly found in Bangkok. In this study, we assembled an automated machine learning (AutoML) workflow using open-source Python libraries to explore suitable predictive models for Su based on 152 undisturbed clay samples. The input variables considered include depth, moisture content, liquid limit, plastic limit, vane shear strength (PP), and total unit weight. Across the models evaluated, ridge regression offered a stable balance between accuracy and computational efficiency, with a mean absolute error of 0.550 t/m², a root mean square error of 0.710 t/m^2,, and an R² of 0.809, while requiring less than 0.05s of training time. The AutoML process facilitated a more transparent comparison of candidate algorithms, providing insight into variable relevance. Specifically, PP, depth, and unit weight emerged as the most influential predictors. Traditional index properties showed comparatively lower contributions. Five-fold cross-validation suggested that the selected model maintained consistent performance (mean R² = 0.810; standard deviation = 0.025). These results suggest that a streamlined AutoML workflow can aid in identifying reliable and easy-to-interpret models for Su estimation in Bangkok clays. Such an approach may complement laboratory testing and help reduce some of the uncertainty associated with empirical correlations, especially in preliminary design stages.