Research on Real-time Performance Prediction of TBM Based on Dual Neural Networks

doi:10.3901/JME.2024.16.043

Abstract

Abstract: With the goal of predicting tunnel boring machine(TBM) performance in real time through data that is easy to obtain during construction, relying on the geological survey data and excavation data of a water conveyance tunnel in Guangdong, the distribution of the main boring parameters and field depth index in different types of surrounding rock is analyzed. The hidden Markov model (HMM) is used to calculate the probability distribution of various surrounding rocks along the tunnel, and using it as a prediction model feature can improve the prediction accuracy but increase the risk of overfitting. A two-way neural network model is proposed, which predicts the average advance performance of the first ring in TBM construction and compares it with classical machine learning models. The results show that the proposed performance prediction model can ensure that the prediction accuracy is better than that of the classical machine learning model and has good generalization ability.

Key words: TBM, performance prediction, hidden Markov model(HMM), artificial neural networks, machine learning

CLC Number:

U455

LIU Jianqin, HU Guisong, QIAO Jinli, GUO Xiao. Research on Real-time Performance Prediction of TBM Based on Dual Neural Networks[J]. Journal of Mechanical Engineering, 2024, 60(16): 43-53.

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