An Intelligent Multi-Ring Shield Movement Performance Prediction and Control Method

Abstract

Accurate control of the shield attitude can ensure precise tunnel excavation and minimize impact on the surrounding areas. However, neglecting the total thrust force may cause excessive disturbance to the strata, leading to collapse. This study proposes a Bayesian optimization-based temporal attention long short-term memory model (BOTA-LSTM) for multi-objective prediction and control of shield tunneling, including shield attitude and total thrust. The model can achieve multi-ring predictions of shield attitude and total thrust by allocating larger weights to significant moments through a temporal attention mechanism. The hyperparameters of the proposed model are automatically selected through Bayesian hyperparameter optimization, which can effectively address the issue of complex parameter selection and optimization difficulties in multi-ring, multi-objective tasks. Based on the predictive results of the optimal model, an intelligent control method that considers both shield attitude and total thrust is proposed. Compared to a method that solely predicts and corrects for the next ring, the proposed multi-ring correction method provides the opportunity for further adjustments, if the initial correction falls short of expectations. A shield tunneling project in Hangzhou is used to demonstrate the effectiveness of the proposed model. The results show that the BOTA-LSTM model outperforms the models without the integration of a temporal attention mechanism and Bayesian hyperparameter optimization. The proposed multi-ring intelligent correction method can adjust the shield attitude and total thrust to a reasonable range, providing references for practical engineering applications.

Keywords

• shield tunneling
• temporal attention mechanism
• Bayesian optimization
• multi-objective control
• deep learning