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.
ZHENG Yiming
,
LI Gang
,
JI Jun
,
ZHANG Mengxi
,
WU Huiming
.
Surface Settlement Prediction Method for Shield Tunneling Based on Stacking Ensemble Learning
[J]. Tunnel Construction, 2024
, 44(11)
: 2233
-2240
.
DOI: 10.3973/j.issn.2096-4498.2024.11.013
