Foundation Pit Soil Parameter Inversion and Deformation Prediction Based on ESFOA and Hybrid Kernel LSSVM

During the excavation process of the foundation pit, soil parameters evolve dynamically. In order to improve the accuracy of soil parameter selection in foundation pit engineering and achieve accurate deformation prediction, this paper proposes a displacement inverse analysis method that combines the enhanced starfish optimization algorithm (ESFOA) and the hybrid kernel least squares support vector machine (LSSVM). The ESFOA improves the global search capability and convergence accuracy of the starfish optimization algorithm (SFOA) by optimizing the initial population and introducing a hunting mechanism. On this basis, the ESFOA was used to optimize the RBF kernel function width (σ), polynomial kernel coefficient (q), regularization penalty coefficient (c), and kernel function mixing weight (λ) of the hybrid kernel LSSVM model. Samples were obtained through finite element simulation and orthogonal experiments, and the optimized ESFOA-LSSVM model was used to establish the nonlinear mapping relationship between the horizontal displacement of the foundation pit excavation enclosure and the soil parameters. The horizontal displacement monitoring data of the foundation pit retaining structure is used to invert the soil parameters and predict the deformation of the retaining structure under subsequent conditions. The results show that (1) compared with other algorithms, the ESFOA has good global search capabilities and convergence accuracy; (2) the ESFOA-LSSVM model is tested through test samples, and the model has good accuracy and feasibility; (3) the parameters obtained by the inversion can effectively improve the prediction accuracy of foundation pit deformation, and the prediction results are closer to the actual monitoring values.