基于Stacking集成学习的盾构掘进地表沉降预测方法

Surface Settlement Prediction Method for Shield Tunneling Based on Stacking Ensemble Learning

为提高盾构施工中地表最终沉降预测模型的准确性和泛化性,结合主成分分析(PCA)和多层堆叠集成算法(Multi-layer Stacking)提出PCA-Stacking盾构掘进地表沉降预测方法。该方法利用PCA算法对盾构掘进过程中产生的大量数据进行处理,以减少特征维度并提取关键信息;此外,通过多层Stacking算法将多个异质模型进行融合,在提高模型预测性能的同时避免子模型间的优化比选。依托上海市北横通道超大直径盾构隧道工程,对盾构工程中的多源数据进行处理,对比PCA处理前后Stacking模型的性能,并将PCA-Stacking模型与RF、XGBoost模型进行对比。研究结果表明:1)PCA处理前后,Stacking模型的R 2分别为0.792和0.831,PCA对Stacking模型性能有一定提高;2)超参数优化后,RF和XGBoost的R 2分别为0.748和0.612,其性能弱于未进行超参数优化的PCA-Stacking;3)PCA-Stacking模型对地表隆起、沉降变化高度都具有良好的预测能力;4)在盾构掘进地表沉降预测方面,异质子模型的PCA-Stacking算法优于同质子模型的集成算法。

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 R 2 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 R 2 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.