Research on deformation prediction of subway tunnel structure based on LSTM network

Haicai Wu; Zhongmin Wang; Wenchuan Hu; Jiachen Liu; Xinyu Shi

doi:10.1117/12.2645545

30 August 2022 Research on deformation prediction of subway tunnel structure based on LSTM network

Haicai Wu, Zhongmin Wang, Wenchuan Hu, Jiachen Liu, Xinyu Shi

Proceedings Volume 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022); 123092G (2022) https://doi.org/10.1117/12.2645545
Event: International Conference on Advanced Manufacturing Technology and Manufacturing System (ICAMTMS 2022), 2022, Shijiazhuang, China

Abstract

In order to improve the accuracy of subway tunnel settlement prediction, the role of long short-term memory network (LSTM network) in subway tunnel settlement prediction was studied. Reverse neural network (BP neural network), support vector machine (SVM) and LSTM network are used to build models, combined with the measured data of Shanghai subway tunnel, the prediction accuracy of the models is compared and analyzed. The test results show that LSTM network is better than BP neural network and support vector machine, and have high prediction accuracy. Compared with the BP neural network model and the support vector machine, the average prediction error of the LSTM network model is reduced by 72% and 75%, the average relative error is reduced by 72% and 75%, and the root mean square error value is reduced by 73% and 78%, the predicted results are closer to the actual measurement results. The research shows that the LSTM network, one of the deep learning methods, is introduced into the subway tunnel settlement monitoring, which improves the prediction accuracy.

Citation Download Citation

Haicai Wu, Zhongmin Wang, Wenchuan Hu, Jiachen Liu, and Xinyu Shi "Research on deformation prediction of subway tunnel structure based on LSTM network", Proc. SPIE 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022), 123092G (30 August 2022); https://doi.org/10.1117/12.2645545

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available