Inversion of Physical and Mechanical Parameters of Surrounding Rock Mass in Foundation Pits Using a PSO-BP Neural Network

In foundation pit engineering, precise determination of the physical–mechanical parameters of the surrounding rock is essential for reliable simulation of rock deformation and anchor cable forces. A foundation pit engineering project in Shapingba District, Chongqing, was selected as a case study. A numerical model was developed using FLAC3D, and 64 working conditions were designed via orthogonal experiments to serve as training samples. Global optimization inversion of the samples was performed using a BP neural network enhanced by particle swarm optimization. Using selected monitoring data of surrounding rock displacement and anchor cable forces, inversion was conducted to determine the physical–mechanical parameters of the foundation pit surrounding rock, and the FLAC3D model inputs were subsequently updated. Finally, simulated results were validated against field measurements. The maximum relative error of surrounding rock displacement reached 8%, with only 3% at the pit center. The largest settlement occurred in the eastern section, where the relative error was 5%. For anchor cable forces, the maximum relative error was 7.9%. This study employed a PSO-BP neural network to invert the physical–mechanical parameters of the foundation pit surrounding rock and introduced a two-stage validation using measured displacements and anchor cable forces. The approach enhances inversion accuracy and provides a practical reference for similar foundation pit engineering applications.