Toward automated formulation, calibration, and implementation of soil models: A generative computational AI framework for SANISAND plasticity
Javad Ghorbani, Majidreza Nazem
Abstract
Soil constitutive models are essential for predicting geomaterial behavior under various loading conditions, serving as a foundation for reliable and efficient construction and infrastructure design. Advanced plasticity models can enhance predictive accuracy in geotechnical applications involving complex environmental and mechanical loads. However, the development, calibration, and numerical implementation of these models are often challenging, time-intensive, and susceptible to subjectivity. This study presents a novel framework called ’generative computational artificial intelligence,’ designed to automate the formulation, calibration, and numerical implementation of constitutive models. Focusing specifically on SANISAND plasticity, the framework features a collection of cooperative agents that autonomously handle experimental data as inputs, develop constitutive models, calibrate parameters, and generate implementation code, all with minimal human intervention. The methodology involves several core stages: (1) feature extraction to identify critical behaviors, such as elasticity and critical state, from data; (2) parameter identification to optimize parameters aligned with these behaviors; (3) model ranking to assess the performance of candidate models; (4) construction and implementation of the final constitutive model based on ranked candidates ; and (5) Final optimization using particle swarm optimization . Results demonstrate that the proposed framework can efficiently build, code, and calibrate a complete constitutive model based on experimental data, enhancing both the efficiency and objectivity of geotechnical simulations. With its potential for broad application to other soil model families, this framework provides a pathway toward more automated, objective, and reliable modeling solutions in geomechanics .