Analysis of machine learning-based applications for intelligent construction of shield tunnels

Research Article
Open access

Analysis of machine learning-based applications for intelligent construction of shield tunnels

Maolin Cheng 1*
  • 1 Guangzhou University    
  • *corresponding author chengmaolin@gzdx.wecom.work
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/20/20231087
ACE Vol.20
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-031-8
ISBN (Online): 978-1-83558-032-5

Abstract

With the quick advancement of the level of information science in shield tunnel construction, the monitoring methods of shield equipment during tunnel boring work are increasingly improved and the recorded construction data includes not only information on the internal workings of the shield equipment, but also on its interaction with the external strata. Machine learning data analysis is powerful and has a wider range of applications and scope than traditional data analysis methods in the civil construction industry. Through the use of machine learning methods, the data and information collected can be mined and analysed in depth to find the intrinsic connections and linkages that can help improve the safety and efficiency ragarding shield tunnel construction. This work presents a literature analysis of current situations of machine learning for shield tunnel construction at home and abroad, briefly describes the basic principles of machine learning methods, summarises and analyses the research situation in shield tunnel construction, reviews the progress of machine learning-based shield equipment condition analysis, intelligent prediction and control methods for shield tunneling parameters and shield tunneling surface deformation prediction, and summarises the current research The study also summarises the shortcomings of current research. Finally, an outlook on the development of shield tunneling towards intelligence is presented.

Keywords:

machine learning, shield tunnel, intelligent construction

Cheng,M. (2023). Analysis of machine learning-based applications for intelligent construction of shield tunnels. Applied and Computational Engineering,20,145-150.
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References

[1]. Xiayun, G. (2021). Development and Expectation of Tunnel and Underground Engineering Technology in China, Construction Technology, 47(6), 45-52.

[2]. Soares, S. G., & Araújo, R. (2015). An on-line weighted ensemble of regressor models to handle concept drifts. Engineering Applications of Artificial Intelligence, 37, 392-406.

[3]. Zhenchuan, S, Tutu, Q, Yingying, Ren, et, al. (2020). Study on Key Technologies and Application of Engineering Big Data Management Platform of Tunnel Boring Machine, Tunnel Constriction, 40(06), 783-792.

[4]. Jin, Y., Qin, C., Tao, J., & Liu, C. (2022). An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mechanical Systems and Signal Processing, 165, 108312.

[5]. Elbaz, K., Shen, S. L., Zhou, A., Yin, Z. Y., & Lyu, H. M. (2021). Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering, 7(2), 238-251.

[6]. Zou, L., & Liang, L. (2018). Fault diagnosis of shield machine based on SOM-BP neural network fusion. In 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, 232-237.

[7]. Gao, X., Shi, M., Song, X., Zhang, C., & Zhang, H. (2019). Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 98, 225-235.

[8]. Qin, C., Shi, G., Tao, J., Yu, H., Jin, Y., Lei, J., & Liu, C. (2021). Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 151, 107386.

[9]. Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2019). Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)—Case study: Beheshtabad water conveyance tunnel in iran. Asian Journal of Water, Environment and Pollution, 16(1), 49-57.

[10]. Stypulkowski, J. B., Bernardeau, F. G., & Jakubowski, J. (2018). Descriptive statistical analysis of TBM performance at Abu Hamour Tunnel Phase I. Arabian Journal of Geosciences, 11, 1-11.

[11]. Sun, W., Shi, M., Zhang, C., Zhao, J., & Song, X. (2018). Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92, 23-34.

[12]. Zhou, C., Xu, H., Ding, L., Wei, L., & Zhou, Y. (2019). Dynamic prediction for attitude and position in shield tunneling: A deep learning method. Automation in Construction, 105, 102840.

[13]. Hongzhou, L, Jun, S. (2001). Influence of ground settlement during shield tunneling in soft soil research on numerical methods of factors, Modern Tunnelling Technology, 38 (6), 24-28.

[14]. Chen, R., Zhang, P., Wu, H., Wang, Z., & Zhong, Z. (2019). Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 13, 1363-1378.

[15]. Kohestani, V. R., Bazarganlari, M. R., & Asgari Marnani, J. (2017). Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest. Journal of AI and Data Mining, 5(1), 127-135.

[16]. Zhang, P., Chen, R. P., & Wu, H. N. (2019). Real-time analysis and regulation of EPB shield steering using Random Forest. Automation in Construction, 106, 102860.

[17]. Pourtaghi, A., & Lotfollahi-Yaghin, M. A. (2012). Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunnelling and Underground Space Technology, 28, 257-271.

Cite this article

Cheng,M. (2023). Analysis of machine learning-based applications for intelligent construction of shield tunnels. Applied and Computational Engineering,20,145-150.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.20
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Xiayun, G. (2021). Development and Expectation of Tunnel and Underground Engineering Technology in China, Construction Technology, 47(6), 45-52.

[2]. Soares, S. G., & Araújo, R. (2015). An on-line weighted ensemble of regressor models to handle concept drifts. Engineering Applications of Artificial Intelligence, 37, 392-406.

[3]. Zhenchuan, S, Tutu, Q, Yingying, Ren, et, al. (2020). Study on Key Technologies and Application of Engineering Big Data Management Platform of Tunnel Boring Machine, Tunnel Constriction, 40(06), 783-792.

[4]. Jin, Y., Qin, C., Tao, J., & Liu, C. (2022). An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mechanical Systems and Signal Processing, 165, 108312.

[5]. Elbaz, K., Shen, S. L., Zhou, A., Yin, Z. Y., & Lyu, H. M. (2021). Prediction of disc cutter life during shield tunneling with AI via the incorporation of a genetic algorithm into a GMDH-type neural network. Engineering, 7(2), 238-251.

[6]. Zou, L., & Liang, L. (2018). Fault diagnosis of shield machine based on SOM-BP neural network fusion. In 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, 232-237.

[7]. Gao, X., Shi, M., Song, X., Zhang, C., & Zhang, H. (2019). Recurrent neural networks for real-time prediction of TBM operating parameters. Automation in Construction, 98, 225-235.

[8]. Qin, C., Shi, G., Tao, J., Yu, H., Jin, Y., Lei, J., & Liu, C. (2021). Precise cutterhead torque prediction for shield tunneling machines using a novel hybrid deep neural network. Mechanical Systems and Signal Processing, 151, 107386.

[9]. Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2019). Prediction of the penetration rate and number of consumed disc cutters of tunnel boring machines (TBMs) using artificial neural network (ANN) and support vector machine (SVM)—Case study: Beheshtabad water conveyance tunnel in iran. Asian Journal of Water, Environment and Pollution, 16(1), 49-57.

[10]. Stypulkowski, J. B., Bernardeau, F. G., & Jakubowski, J. (2018). Descriptive statistical analysis of TBM performance at Abu Hamour Tunnel Phase I. Arabian Journal of Geosciences, 11, 1-11.

[11]. Sun, W., Shi, M., Zhang, C., Zhao, J., & Song, X. (2018). Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data. Automation in Construction, 92, 23-34.

[12]. Zhou, C., Xu, H., Ding, L., Wei, L., & Zhou, Y. (2019). Dynamic prediction for attitude and position in shield tunneling: A deep learning method. Automation in Construction, 105, 102840.

[13]. Hongzhou, L, Jun, S. (2001). Influence of ground settlement during shield tunneling in soft soil research on numerical methods of factors, Modern Tunnelling Technology, 38 (6), 24-28.

[14]. Chen, R., Zhang, P., Wu, H., Wang, Z., & Zhong, Z. (2019). Prediction of shield tunneling-induced ground settlement using machine learning techniques. Frontiers of Structural and Civil Engineering, 13, 1363-1378.

[15]. Kohestani, V. R., Bazarganlari, M. R., & Asgari Marnani, J. (2017). Prediction of maximum surface settlement caused by earth pressure balance shield tunneling using random forest. Journal of AI and Data Mining, 5(1), 127-135.

[16]. Zhang, P., Chen, R. P., & Wu, H. N. (2019). Real-time analysis and regulation of EPB shield steering using Random Forest. Automation in Construction, 106, 102860.

[17]. Pourtaghi, A., & Lotfollahi-Yaghin, M. A. (2012). Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunnelling and Underground Space Technology, 28, 257-271.

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