An AI-Driven Framework for State-Dependent Constitutive Modeling of Soils Integrating Particle Swarm Optimization and Machine Learning

Abstract

Accurately predicting soil stress-strain behavior remains challenging due to the nonlinear, path-dependent, and evolving nature of soil properties. This study develops an AI-driven State-Dependent modeling framework (AI-D-SD-F) that integrates Particle Swarm Optimization (PSO) and Machine Learning (ML) for real-time parameter evolution and adaptive stress-strain simulation. PSO dynamically calibrates plastic potential parameters under varying stress states, while Gaussian Process Regression (GPR) and Broad Learning System (BLS) models establish nonlinear mappings among stress paths, hardening variables, and initial conditions to enable data-driven parameter updating. An adaptive strain-step implicit algorithm further improves the stability of nonlinear stress-return computations. Validation using triaxial tests on Hangzhou clay shows that the framework improves prediction accuracy by more than 32% compared with the Tsinghua and Modified Cam-Clay models. Engineering-scale simulations of shallow foundation failure exhibit deviations within 5% of Terzaghi’s local bearing capacity, confirming strong predictive reliability. The proposed framework provides a unified, data-enhanced foundation for adaptive constitutive modeling, improving both numerical robustness and accuracy in geotechnical analyses.