Prediction of TBM tunneling parameters and rockburst grade based on CNN-LSTM model

In order to improve the intelligent construction and disaster prediction capabilities of TBM in traffic water conservancy and deep coal mine engineering, the CNN-LSTM model combining the advantages of convolutional neural network (CNN) and long short-term memory neural network (LSTM) was proposed, and the tunnelling parameters in the stabilization stage of TBM tunnel and rockburst grade were predicted based on the Hanjiang River-Weihe River Water Conveyance Project. In addition, the TBM data was cleaned and preprocessed, the TBM data, geological parameters and rockburst grade were matched according to the station number, reasonable prediction indicators were screened out based on grey relation analysis. And the hyperparameters of the CNN-LSTM model were reasonably selected to obtain better prediction results, and the prediction results of other models were compared and analyzed. The research findings indicate that for the tunneling parameters of thrust (F), penetration (P), and torque (M) during the stable phase of TBM tunneling, the CNN-LSTM model predicts significantly lower and more stable mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) compared to other models. Among them, the torque (M) prediction effect is the best, the thrust (F) prediction effect is second, and the penetration (P) prediction effect is the worst. With the increase of rockburst grade, the cutterhead and cutter are severely impacted, and the prediction effect of different models for the above tunnelling parameters become worse, but the prediction results of CNN-LSTM model are within the effective range and have high robustness. The CNN-LSTM model's accuracy (ACC), precision (PRE), and recall (REC) in predicting the rockburst grade of TBM tunnels are significantly higher than those of other models, with accuracy (ACC), macro-precision (MPRE), and macro-recall (MREC) reaching 98.17%, 97.73%, and 98.58% respectively. According to the random sampling analysis of the model, the CNN-LSTM model has high robustness for different rockburst sample of the same capacity, which is significantly better than other models. In conclusion, the CNN-LSTM model is feasible and effective for predicting the TBM tunnelling parameters and rockburst grade.