Intelligent prediction method for cutterhead torque of shield tunnel based on intelligent optimization algorithm and Bi-LSTM neural network

<p indent="0mm">Intelligent prediction of reasonable cutterhead torque can effectively reduce the stratum disturbance caused by shield tunneling and reduce cutterhead wear, which is of great significance for the safe and efficient construction of shield tunnels. A time-series intelligent prediction method for shield tunnel cutterhead torque is proposed based on intelligent optimization algorithms and deep-learning neural networks. Bi-LSTM neural network is used as the deep learning prediction model. The particle swarm optimization algorithm (PSO), genetic optimization algorithm (GA), sparrow search algorithm (SSA), and hunger game search algorithm (HGS) are jointly used to intelligently determine the three hyperparameters of the Bi-LSTM neural network, respectively, namely that, learning rate, the number of neurons, and dropout rate. Furthermore, an intelligent prediction method for the cutterhead torque of the shield tunnel is obtained. The root mean square error between the predicted values of intelligent models and measured values is used as fitness functions for four optimization algorithms. Normalization method is used for data preprocessing to reduce the impact of magnitude differences between different parameters. The input of the intelligent prediction model considers the influence of 15 parameters on the cutterhead torque, including 1 tunnel geometric parameter, 6 geological mechanical parameters, and 8 shield tunneling parameters. The performances of four optimization algorithms for determining hyperparameters of Bi-LSTM neural network are evaluated from three perspectives: accuracy, efficiency, and stability, based on on-site measured data of shield tunneling. The results indicate that compared with the other three optimization algorithms, the HGS method has better stability, faster efficiency, and higher computational accuracy, which is an appropriate method to determine the hyperparameters of Bi-LSTM.