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Research on a pipeline-based stacking model for predicting subway tunnel settlement

neural-networks

Abstract

Accurate prediction of ground settlement in metro construction remains challenging due to complex geological conditions and strong non-linear variations, often leading to safety risks. To address these limitations, this study proposes a data-driven Stacking ensemble model within a pipeline framework that integrates feature selection and hyperparameter optimisation. Using monitoring data from the Hefei Metro project, the model automatically identified three key features from twelve inputs and combined random forest, gradient boosting, and support vector regression, with linear regression as the meta-learner. Compared with individual base models, the proposed approach effectively mitigated overfitting and substantially improved predictive performance, achieving an R² of 0.90, RMSE of 0.70, and MAE of 0.55 on the test set. These results demonstrate that systematic optimisation of ensemble learning enhances both accuracy and generalisation, offering a reliable and efficient tool for settlement prediction and safety management in metro construction.
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