Integrated Optimization System for Geotechnical Parameter Inversion Using ABAQUS, Python, and MATLAB

doi:10.3390/buildings15071108

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Integrated Optimization System for Geotechnical Parameter Inversion Using ABAQUS, Python, and MATLAB

2025-03-28

DOI：10.3390/buildings15071108

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Abstract

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Accurate inversion of geotechnical parameters is essential for assessing foundation-bearing capacity and stability, which directly impact structural safety and serviceability. Accurate prediction of load settlement behavior is crucial to prevent overdesign and underperformance, ensuring that foundations support anticipated loads without excessive deformation or failure. This paper presents an integrated optimization system combining ABAQUS (2022), Python (PyCharm21.3.3), and MATLAB (2022b) software, based on the Duncan-Chang (DC) model, for inversion of key geotechnical parameters. The ABAQUS UMAT subroutine customizes the DC model, facilitating its application in finite element simulations for soil-structure interaction analysis. To improve the optimization process, an adaptive genetic algorithm that dynamically adjusts crossover and mutation rates, thereby improving solution searches and parameter space exploration, is implemented. Key parameters of the DC model-the initial tangent stiffness (K) and nonlinear deformation characteristics (n) of soil-are inverted. The accuracy of this inversion is validated through comparisons with experimental pressure-settlement curves obtained from indoor bearing plate tests. Therefore, this optimization system effectively integrates intelligent algorithms with finite element analysis, serving as a reliable tool for precise geotechnical parameter inversion, with potential for improving foundation design accuracy, optimizing soil-structure interaction predictions, and improving the overall stability and safety of geotechnical structures.

Keywords:

adaptive genetic algorithm

Duncan-Chang model

joint optimization

geotechnical parameters

secondary development