To enhance the accuracy and generalization of surface final settlement prediction models in shield tunneling,the authors establish a principal component analysis(PCA)-stacking model for surface settlement prediction,integrating the PCA method with a multi-layer Stacking ensemble algorithm.The PCA algorithm processes the extensive data generated during shield tunneling,reducing feature dimensions and extracting critical information.Simultaneously,the multi-layer Stacking algorithm integrates multiple heterogeneous models,improving predictive performance while avoiding optimization comparisons among sub-models.Based on a case study of an ultra-large diameter shield tunnel from the Shanghai Beiheng tunnel project,multi-source data from shield tunneling engineering are analyzed.The performance of the Stacking model before and after PCA processing is compared,and the PCA-Stacking model is benchmarked against random forests(RF)and extreme gradient boosting(XGBoost)models.The research findings are as follows:(1)The R2 values of the Stacking model before and after PCA processing are 0.792 and 0.831,respectively,demonstrating that PCA improves the stacking model's performance.(2)After hyperparameter optimization,the R2 values for the RF and XGBoost models are 0.748 and 0.612,respectively,showing inferior performance compared to the PCA-Stacking model without hyperparameter optimization.(3)The PCA-Stacking model exhibits robust predictive capabilities for both ground uplift and subsidence variations.(4)The PCA-Stacking algorithm,employing heterogeneous sub-models,outperforms ensemble algorithms based on homogeneous sub-models in predicting ground settlement during shield tunneling.
万方数据
维普
