The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels.

The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complementary tunnel information encoding method. The FDNN model utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to extract features related to tunnel structural deformation. FDNN model is used to predict deformations in the Capital Ring Expressway, and the predictions align well with monitoring results. To demonstrate the superiority of the proposed model, we use four different performance evaluation metrics to analyze the predictive performance of FDNN, DNN, XGBoost, Decision Tree Regression (DTR), and Random Forest Regression (RFR) methods. The results indicate that FDNN exhibits high precision and robustness. To assess the impact of different data types on the predictive results, we use tunnel geometry data as the base and combine geological, support, and construction data. The analysis reveals that models trained on datasets comprising all four data types perform the best. Geological parameters have the most significant impact on the predictive performance of all models. The findings of this research guide predicting tunnel construction parameters, particularly in the dynamic design of support parameters. [ABSTRACT FROM AUTHOR]