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Journal of Intelligent Construction 2023, 1(4): 9180029 https://doi.org/10.26599/JIC.2023.9180029
Research Article | Open Access | Issue | Published: 27 December 2023

Intelligent recognition of voids behind tunnel linings using deep learning and percussion sound

Show Author's Information Xiaolei Zhang1,  Xin Lin1,  Wei Zhang2,  Yong Feng3 ( ),  Wei Lan4,  Yuewu Da2,  Kan Hu2
Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
Wuxi Water Group Co., Ltd., Wuxi 214031, China
Urban Mobility Institute, Tongji University, Shanghai 201804, China
Shanghai Shenyuan Geotechnical Engineering Co., Ltd., Shanghai 200040, China
Keywords:
deep learning, tunnel, void defect, percussion method, MFCCs
Cite this article:
Zhang X, Lin X, Zhang W, et al. Intelligent recognition of voids behind tunnel linings using deep learning and percussion sound. Journal of Intelligent Construction, 2023, 1(4): 9180029. https://doi.org/10.26599/JIC.2023.9180029
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Abstract Full text About this article

Voids behind tunnel linings are critical factors affecting tunnels’ safety and durability. For automatic, rapid, and accurate detection of void defects behind tunnel linings, this paper proposes an intelligent recognition method of void detection based on deep learning (DL) and percussion method. Extensive indoor percussion experiments were first conducted to obtain a total of 77,925 percussion signals. Afterward, the mel-frequency cepstrum coefficients (MFCCs) are utilized for signal feature extraction, based on which a convolutional neural network (CNN) is developed for automatic void defect diagnosis. The void automated diagnosis tests are subsequently performed, and the impact of three key factors on the recognition results is investigated. The results show that the proposed CNN can accurately identify voids ranging from 0.10 to 0.30 m, with an average accuracy of 94.96% and an F1 score of 72.29%. The exploration of the slab thickness indicates that the proposed method is capable of detecting voids with an average accuracy of 94.37% and an F1 score of 74.55%, with the slab thicknesses ranging from 0.10 to 0.30 m. Furthermore, the boundary effects of concrete slabs are analyzed. Finally, an on-site validation is carried out, and the good agreements between the developed method and ultrasonic detection method indicate that the CNN-aided percussion method is feasible in practical tunnel lining void inspection tasks.


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About this article

Intelligent recognition of voids behind tunnel linings using deep learning and percussion sound

Show Author's information Xiaolei Zhang1, Xin Lin1, Wei Zhang2, Yong Feng3 ( ), Wei Lan4, Yuewu Da2, Kan Hu2
Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China
Wuxi Water Group Co., Ltd., Wuxi 214031, China
Urban Mobility Institute, Tongji University, Shanghai 201804, China
Shanghai Shenyuan Geotechnical Engineering Co., Ltd., Shanghai 200040, China

Abstract

Voids behind tunnel linings are critical factors affecting tunnels’ safety and durability. For automatic, rapid, and accurate detection of void defects behind tunnel linings, this paper proposes an intelligent recognition method of void detection based on deep learning (DL) and percussion method. Extensive indoor percussion experiments were first conducted to obtain a total of 77,925 percussion signals. Afterward, the mel-frequency cepstrum coefficients (MFCCs) are utilized for signal feature extraction, based on which a convolutional neural network (CNN) is developed for automatic void defect diagnosis. The void automated diagnosis tests are subsequently performed, and the impact of three key factors on the recognition results is investigated. The results show that the proposed CNN can accurately identify voids ranging from 0.10 to 0.30 m, with an average accuracy of 94.96% and an F1 score of 72.29%. The exploration of the slab thickness indicates that the proposed method is capable of detecting voids with an average accuracy of 94.37% and an F1 score of 74.55%, with the slab thicknesses ranging from 0.10 to 0.30 m. Furthermore, the boundary effects of concrete slabs are analyzed. Finally, an on-site validation is carried out, and the good agreements between the developed method and ultrasonic detection method indicate that the CNN-aided percussion method is feasible in practical tunnel lining void inspection tasks.

Keywords: deep learning, tunnel, void defect, percussion method, MFCCs

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Publication history
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Publication history

Received: 24 September 2023
Revised: 02 November 2023
Accepted: 09 November 2023
Published: 27 December 2023
Issue date: December 2023

Copyright

© The Author(s) 2023. Published by Tsinghua University Press.

Acknowledgements

Much of the work described in this paper was supported by the National Key R&D Program of China (No. 2020YFC1808105), Science and Technology Commission of Shanghai Municipality (No. 21DZ1204400), and the National Natural Science Foundation of China (Nos. 42372335 and 42007250). The authors would like to greatly acknowledge all these financial supports and express their most sincere gratitude.

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