Abstract
Accurate prediction of ground settlement is vital to guarantee the safety and efficiency of subway tunnel digging. In this study, to accurately predict the tunneling-induced ground settlement, the extreme gradient boosting (XGBoost) model is optimized using a combination of three optimization algorithms and the principal component analysis (PCA), based on a dataset of 288 tunnel case histories and 12 indicators representing the tunnel’s spatial morphology, stratum properties, and excavation state. PCA is utilized to pre-process the sample dataset, further eliminate the correlation between indicators and reduce the dimension. Seven linearly independent principal components are obtained, constituting the model’s input variables. Thereafter, particle swarm optimization (PSO), Bayesian optimization (BO), and sparrow search algorithm (SSA) are applied to optimize four important hyper-parameters of the XGBoost model, thus improving its performance effectively. The prediction performance of XGBoost, PSO-XGBoost, BO-XGBoost, SSA-XGBoost, PSO-support vector machine, PSO-random forest, grid search-back propagation neural network and grid search-deep neural network models are compared to determine the optimal model. The study results showed that the Pearson correlation coefficient, root mean square error, and mean absolute error of the proposed BO-XGBoost model on the training dataset are 0.9299, 1.2563, and 0.3015, whereas those on the testing dataset are 0.9154, 1.3114 and 0.9928, respectively. These results prove its reliability and superiority against other alternatives in predicting tunneling-induced ground settlement.
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Financial support from the National Natural Science Foundation of China (51934003), the China Postdoctoral Science Foundation (2021M693840), and the Yunnan innovation team (202105AE160023).
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Geng, X., Wu, S., Yan, Q. et al. An Optimized XGBoost Model for Predicting Tunneling-Induced Ground Settlement. Geotech Geol Eng 42, 1297–1311 (2024). https://doi.org/10.1007/s10706-023-02619-x
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DOI: https://doi.org/10.1007/s10706-023-02619-x