Prediction interaction responses between railway subgrade and shield tunnelling using machine learning with sparrow search algorithm

Tunnelling-induced uneven ground structure settlement is a hot research topic involving various interrelated factors. This paper employs hybrid algorithms to establish the predictive model for the interaction responses, including maximum settlements, the longitudinal settlement curve, and the shield operational parameters. We choose four machine learning (ML) models: back-propagation neural network (BPNN), long short-term memory neural network (LSTM), least squares support vector machine (LS-SVM), and deep extreme learning machine (DELM). The sparrow search algorithm (SSA) searches for optimal hyperparameter combinations to improve prediction performance. We comprehensively compare the above models' accuracy and generalization ability for different predicting objects. The database used in this study is collected from a subway project in Beijing, China, where the excavation of twin shield tunnels caused subgrade differential settlements on four national railway lines. The in-situ data from the right line of twin shield tunnels is used to train and test the models, while that from the left line is applied to verify the generalization ability of the models. The DELM-SSA model performs well in predicting maximum settlement, while the LSTM-SSA model excels at predicting shield operational parameters. The LS-SVM-SSA model accurately predicts the monitoring points' longitudinal settlement curve. According to the results, different models are recommended for predicting the interaction responses. The analysis of the Pearson correlation coefficient also reveals that shield operational parameters, such as shield driving speed (Sds) and cutterhead rotational speed (Crs), correlate relatively strongly with the settlement.