Prediction of shield tunnelling parameters based on temporal convolutional network

Yuchao Wang; Xiongyao Xie; Changfu Huang; Gang Niu; Zhou Shi

doi:10.1117/12.3030150

5 June 2024 Prediction of shield tunnelling parameters based on temporal convolutional network

Yuchao Wang, Xiongyao Xie, Changfu Huang, Gang Niu, Zhou Shi

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316367 (2024) https://doi.org/10.1117/12.3030150
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

This paper establishes a novel deep learning model for real-time prediction of shield cutter head torque during tunnelling. A hybrid model was developed which integrates wavelet transform (WT) and temporary convolution network (TCN). We comprehensively assess the predictive performance of the model by R² and RMSE indexes. The validity of the model is verified by Fuzhou Binhai Expressway Project. Another two excellent recurrent neural networks including LSTM and GRU were used for comparison. The results show that the WT-TCN model achieves the highest prediction accuracy, R² and RMSE are 0.995 and 17.078. And the accuracy improvement in RMSE can reach 33.7% and 57.7% respectively. The prediction of tunneling parameters based on WT-TCN algorithm can significantly improve the judgment level of shield tunneling state, which is beneficial for optimizing and adjusting construction parameters.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuchao Wang, Xiongyao Xie, Changfu Huang, Gang Niu, and Zhou Shi "Prediction of shield tunnelling parameters based on temporal convolutional network", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316367 (5 June 2024); https://doi.org/10.1117/12.3030150

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KEYWORDS

Convolution

Wavelets

Data modeling

Head

Performance modeling

Deep learning

Neural networks

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