Soil Parameter Inversion Considering the Influence of Temperature Effects

Significant environmental temperature variations occur during the construction of large-scale underground structures, constituting one of the major factors influencing structural deformation. Parameter inversion of soil layers based solely on the causal relationship between excavation-induced loading effects and structural displacements can lead to substantial errors. To address this issue, this study aims to improve the inversion accuracy of soil parameters by considering temperature effects. A finite element model incorporating temperature effects, combined with machine learning algorithms, was employed to improve the inversion process. Based on the measured displacements and structural temperatures of diaphragm walls of the Beijing Tongzhou Integrated Transportation Hub Project, the influence of temperature effects on structural behavior was investigated to improve the inversion accuracy of soil parameters for large underground structures. Then, a finite element model of the excavation considering temperature effects is established using measured soil parameters and temperature data. According to soil classification, a training dataset is constructed through proportional scaling of soil parameters. Three machine learning algorithms—Decision Tree, Random Forest, and Gaussian Process Regression—are compared to evaluate inversion accuracy. The results indicate that the deformation of underground structures is governed by the coupled effects of temperature and earth pressure. Among the tested methods, the Random Forest algorithm demonstrates the highest accuracy in soil parameter inversion, with an average displacement error of 4.23% in the finite element model based on the inverted parameters. These findings highlight the importance of incorporating temperature effects to enhance inversion reliability for large underground structures.