Intelligent real-time prediction of multi-region thrust of EPB shield machine based on SSA-LSTM

Abstract

During the tunneling process of shield machine, the accurate determination of tunneling parameters is the guarantee of safe operation of shield machine. Through the real-time construction data analysis and mining of shield machine, the tunneling parameters at the next moment are obtained in real-time. Based on this, this paper proposes an intelligent real-time prediction method for multi-region thrust of EPB shield machine based on Sparrow Search algorithm-Long and Short-term Memory (SSA-LSTM). By correlation analysis of the construction big data, the data features with a great correlation of shield machine thrust are obtained. And it is used as the input of LSTM prediction model to explore the nonlinear relationship between inputs and output. SSA is used to optimize LSTM prediction model to establish a more accurate nonlinear relationship, and then the multi-region thrust of shield machine at the next moment is accurately obtained. The simulation results show that SSA-LSTM model can accurately predict the thrust of each region of shield machine at the next moment, and the prediction performance is better than other models. The method provides a reference basis for shield machine to implement accurate thrust regulation and provides a guarantee for effective control of Earth pressure balance (EPB) in sealed cabin to ensure construction safety, which has engineering application value.