Probabilistic analysis of active earth pressures in spatially variable soils using machine learning and confidence intervals.

ABSTRACT: This study introduces a probabilistic framework for assessing active earth pressures in soils exhibiting spatial variability in friction angles and unit weight. These properties are modeled using random fields with log-normal distributions and spatial correlation lengths. Monte Carlo simulations (MCS) are integrated with finite element limit analysis (FELA) to evaluate the failure probability under different design safety factors. To improve computational efficiency and prediction accuracy, machine learning models, such as Multivariate Adaptive Regression Splines (MARS), are utilized to predict failure probabilities based on key spatial variability parameters. A two-phase optimization approach, combining Random Search and Adaptive Sampling, is employed to refine the hyperparameters of the

SUBMITTER: Vu-Hoang T 

PROVIDER: S-EPMC11909125 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature