Earth Chamber Pressure Prediction Method for Earth Pressure Balance Shield Based on LSTM-DNN Fusion Model

Abstract

［Objective］Earth chamber pressure is a key parameter for EPB (earth pressure balance) shield construction assessment. Accurate prediction of earth chamber pressure helps construction technicians take timely control measures to ensure subway tunnel construction safety. Therefore, it is necessary to study the earth chamber pressure prediction method of EPB shield. ［Method］A multi-branch LSTM (long and short term memory)-DNN (deep neural network) fusion model is proposed. LSTM branch extracts its time series evolution characteristics by backtracking historical data, while DNN branch extracts excavation state characteristics. The two branches are combined and then integrated through a fully connected layer to realize the prediction of earth chamber pressure. This multi-branch model is verified based on the actual shield tunnel data of Jinan Rail Transit Line 1, and compared with LSTM and DNN models respectively. ［Result & Conclusion］The prediction model of earth chamber pressure based on LSTM-DNN fusion algorithm can converge efficiently, and has good prediction effects on the training set and the verification set. In the subsequent 100-step test, the predicted value of earth chamber pressure obtained by the LSTM-DNN fusion model better reflects the change trend of the actual value, with an average deviation of 7.65 kPa and a relative error of 6.09%, indicating a higher prediction accuracy.

Keywords

• urban rail transit
• earth chamber pressure prediction
• lstm
• dnn