Machine Learning Prediction of Soil‐Water Characteristic Curve and Its Application in Lateral Earth Pressure Calculation of Retaining Walls

Abstract

The soil‐water characteristic curve (SWCC) is a fundamental parameter that governs the hydro‐mechanical behavior of unsaturated soils. Conventional laboratory measurement of SWCC is time‐consuming and labor‐intensive, while traditional lateral earth pressure design for retaining walls frequently relies on the saturated soil assumption, neglecting the effects of SWCC and resulting in significant systematic deviations in calculations. This study develops a statistically rigorous machine learning (ML) framework for efficient SWCC prediction and its application to lateral earth pressure calculations for pile‐supported box counterfort retaining walls. Four ML algorithms with distinct methodological frameworks were employed: extreme learning machine (ELM), least squares support vector machine (LSSVM), projection pursuit regression (PPR), and Bayesian ridge regression (BRR). These algorithms were utilized to construct SWCC prediction models using the cleaned UNSODA database. Model performance was assessed through multi‐metric evaluation, paired t ‐tests for statistical significance, and robustness analysis involving 30 independent runs, with validation conducted on measured silty clay data across 12 suction levels. Results indicate that the ELM model achieves the highest prediction accuracy, demonstrating statistically significant superiority over LSSVM, PPR, and BRR and excellent robustness. Independent validation reveals an average relative error of only 2.25% for ELM‐predicted SWCC. The SWCC‐based earth pressure calculation rectifies the bidirectional deviations of the traditional saturated method and identifies a neutral point at a depth of 17.5 m for a 25 m‐high retaining wall. This study offers a reliable technical approach for rapid SWCC acquisition and refined lateral earth pressure design for retaining structures.