Soil Parameter Inversion in Dredger Fill Strata Using GWO-MLSSVR for Deep Foundation Pit Engineering - Changrui Chen; Sifan Li; Jinbi Ye; Fangjian Chen; Yibin Wu; Jin Yu; Yanyan Cai; Jinna Lin; Xianqi Zhou (Buildings)
Accurate determination of constitutive model parameters is crucial for reliable numerical simulation in deep foundation pit engineering. This study presents an inverse analysis method using Multioutput Least-Squares Support Vector Regression (MLSSVR) optimized by the Gray Wolf Optimization (GWO) algorithm to invert key parameters of the Hardening Soil (HS) model. A case study on a foundation pit in the dredger fill stratum of Xiamen Railway integrates finite element simulation with machine learning. The proposed GWO-MLSSVR model demonstrates high predictive accuracy, with lateral displacement predictions closely matching field monitoring data and relative errors within 5% at various depths of measurement point. Compared to traditional inversion methods and MLSSVR models optimized by other algorithms, this approach significantly reduces prediction errors. Additionally, the influence of construction stages, input layer nodes, and training sample size on inversion performance is investigated. This method provides a practical and efficient solution for accurately obtaining soil parameters under complex soil conditions, thereby enhancing the reliability of geotechnical numerical simulations and offering valuable guidance for foundation pit design and safety assessment.