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R2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R2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 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.
