Automated quantification of longitudinal joint opening in shield tunnels using enhanced multi-modal YOLO and laser scanning

Abstract

The deformation of joints in shield tunnels poses significant risks for tunnel safety, serviceability, and durability. Compared to circumferential joints, the deformation of longitudinal joints is more important for tunnel safety assessment. However, traditional tunnel inspection methods focus only on the overall convergence and dislocation between rings, neglecting the opening of longitudinal joints. Moreover, accurate detection of longitudinal joints is difficult, especially with visual data collected from field tunnels, because non-structural elements, such as cables and pipelines, often present similar visual features to longitudinal joints and thus interfere with their detection. The proposed method employs an advanced multi-modal deep learning model (an enhanced YOLOv8n, augmented with a Spatial-to-Depth (SPD) module and a Convolutional Block Attention Module (CBAM), and optimized with a grayscale-depth fusion strategy to robustly detect longitudinal joints in images, even under interference from non-structural elements and challenging lighting conditions. Once the joints are accurately identified and precisely localized in the image domain, the spatial correspondence between the images and point cloud data is established to enable boundary point extraction and coordinate transformation. This process isolates the relevant point cloud data associated with the detected joints and facilitates accurate calculation of the joint opening. Field experiments validated the effectiveness of the method in accurately locating longitudinal joints and quantifying their openings in an operational tunnel environment. The proposed method fills a critical gap in the current tunnel structural safety evaluations by offering an accurate and effective means of evaluating the deformation of longitudinal joints. This approach improves the accuracy and reliability of tunnel deformation assessments, and provides more critical and detailed data for tunnel structural safety evaluations. Future work will focus on refining the joint detection and boundary point extraction methods to address more complex tunnel environments and defects, as well as the need for comprehensive error analysis and uncertainty quantification.

Keywords

Shield tunnel / Longitudinal joint deformation / YOLOv8n / Laser scanning / Point cloud

References

[1] R. Amjad, Y. Zhang, X. Liu, An analytical solution for shear bearing capacity of circumferential joints of precast concrete segmental tunnel linings considering dowel action, Struct. Concr. (2024), doi: 10.1002/suco.202400250.

[2] L. Du, R. Zhong, H. Sun, Y. Pang, Y. Mo, Dislocation detection of shield tunnel based on dense cross-sectional point clouds, IEEE Trans. Intell. Transp. Syst. 23 (2022) 22227-22243, doi: 10.1109/TITS.2022.3156385.

[3] A. Yu, W. Mei, M. Han, Deep learning based method of longitudinal dislocation de- tection for metro shield tunnel segment, Tunn. Undergr. Space Technol. 113 (2021) 103949, doi: 10.1016/j.tust.2021.103949.

[4] J.-L. Zhang, H.A. Mang, X. Liu, Y. Yuan, B. Pichler, On a nonlinear hybrid method for multiscale analysis of a bearing-capacity test of a real-scale segmen- tal tunnel ring, Int. J. Numer. Anal. Methods Geomech. 43 (2019) 1343-1372, doi: 10.1002/nag.2894.

[5] J.-L. Zhang, C. Vida, Y. Yuan, C. Hellmich, H.A. Mang, B. Pichler, A hybrid analysis method for displacement-monitored segmented circular tunnel rings, Eng. Struct. 148 (2017) 839-856, doi: 10.1016/j.engstruct.2017.06.049.

[6] Y. Yuan, Y. Bai, J. Liu, Assessment service state of tunnel structure, Tunn. Undergr. Space Technol. 27 (2012) 72-85, doi: 10.1016/j.tust.2011.07.002.

[7] Q. Wang, P. Geng, P. Li, D. He, H. Shen, Failure analysis of circumferential joints and preferable bolt form of shield tunnel under normal fault dislocation, Tunn. Undergr. Space Technol. 146 (2024) 105648, doi: 10.1016/j.tust.2024.105648.

[8] D. Liu, C. Tian, F. Wang, Q. Hu, J. Zuo, Longitudinal structural deformation mech- anism of shield tunnel linings considering shearing dislocation of circumferential joints, Comput. Geotech. 139 (2021) 104384, doi: 10.1016/j.compgeo.2021.104384.

[9] H.-N. Wu, S.-L. Shen, S.-M. Liao, Z.-Y. Yin, Longitudinal structural modelling of shield tunnels considering shearing dislocation between segmental rings, Tunn. Un- dergr. Space Technol. 50 (2015) 317-323, doi: 10.1016/j.tust.2015.08.001.

[10] X. Liu, Z. Dong, Y. Bai, Y. Zhu, Investigation of the structural effect induced by stagger joints in segmental tunnel linings: first results from full-scale ring tests, Tunn. Undergr. Space Technol. 66 (2017) 1-18, doi: 10.1016/j.tust.2017.03.008.

[11] D. Su, W. Chen, X. Wang, M. Huang, X. Pang, X. Chen, Numerical study on transverse deformation characteristics of shield tunnel subject to local soil loosening, Undergr. Space 7 (2022) 106-121, doi: 10.1016/j.undsp.2021.07.001.

[12] H. Huang, H. Shao, D. Zhang, F. Wang, Deformational responses of operated shield tunnel to extreme surcharge: a case study, Struct. Infrastruct. Eng. 13 (2017) 345-360, doi: 10.1080/15732479.2016.1170156.

[13] C. Gong, M. Cheng, Y. Ge, J. Song, Z. Zhou, Leakage mechanisms of an operational underwater shield tunnel and countermeasures: a case study, Tunn. Undergr. Space Technol. 152 (2024) 105892, doi: 10.1016/j.tust.2024.105892.

[14] P. Li, Y. Zhang, F. Jiang, H. Zheng, Comprehensive health assessment of shield tunnel structure based on prototype experiment, J. Cent. South Univ. 25 (2018) 681-689, doi: 10.1007/s11771-018-3771-2.

[15] H. Zhu, L. Zhou, J. Zhu, Beam-spring generalized model for segmental lining and simulation of its nonlinear rotation, Chin. J. Geotech. Eng. 41 (2019) 1581-1590, doi: 10.11779/CJGE201909001. (in Chinese)

[16] J.-Y. Han, J. Guo, Y.-S. Jiang, Monitoring tunnel profile by means of multi-epoch dispersed 3-D LiDAR point clouds, Tunn. Undergr. Space Technol. 33 (2013) 186-192, doi: 10.1016/j.tust.2012.08.008.

[17] H. Cui, X. Ren, Q. Mao, Q. Hu, W. Wang, Shield subway tunnel deformation detection based on mobile laser scanning, Autom. Constr. 106 (2019) 102889, doi: 10.1016/j.autcon.2019.102889.

[18] L. Xu, J. Gong, J. Na, Y. Yang, Z. Tan, N. Pfeifer, S. Zheng, Shield tunnel conver- gence diameter detection based on self-driven mobile laser scanning, Remote Sens. 14 (2022) 767. https://doi.org/10.3390/rs14030767.

[19] W. Wang, W. Zhao, L. Huang, V. Vimarlund, Z. Wang,Applications of terrestrial laser scanning for tunnels: a review, J. Traffic Transp. Eng. Engl. Ed. 1 (2014) 325-337, doi: 10.1016/S2095-7564(15)30279-8.

[20] B. Shi, M. Yang, J. Liu, B. Han, K. Zhao, Rail transit shield tunnel defor- mation detection method based on cloth simulation filtering with point cloud cylindrical projection, Tunn. Undergr. Space Technol. 135 (2023) 105031, doi: 10.1016/j.tust.2023.105031.

[21] X. Liu, Y. Chen, X. Liu,Laser scanning-based rapid detection of deformation of shield tunnel section, J. Traffic Transp. Eng. 21 (2021) 107-116. (in Chinese)

[22] Z. Cao, D. Chen, Y. Shi, Z. Zhang, F. Jin, T. Yun, S. Xu, Z. Kang, L. Zhang, A flexible architecture for extracting Metro tunnel cross sections from terrestrial laser scanning point clouds, Remote Sens. 11 (2019) 297. https://doi.org/10.3390/rs11030297.

[23] N. Yasuda, Y. Cui, Deformation estimation of a circular tunnel from a point cloud using elliptic fourier analysis, Tunn. Undergr. Space Technol. 125 (2022) 104523, doi: 10.1016/j.tust.2022.104523.

[24] X. Xie, X. Lu, Development of a 3D modeling algorithm for tunnel deformation monitoring based on terrestrial laser scanning, Undergr. Space 2 (2017) 16-29, doi: 10.1016/j.undsp.2017.02.001.

[25] J. Xu, L. Ding, H. Luo, E.J. Chen, L. Wei, Near real-time circular tunnel shield seg- ment assembly quality inspection using point cloud data: a case study, Tunn. Un- dergr. Space Technol. 91 (2019) 102998, doi: 10.1016/j.tust.2019.102998.

[26] Z. Yue, H. Sun, R. Zhong, L. Du, Method for tunnel displacements calcu- lation based on mobile tunnel monitoring system, Sensors 21 (2021) 4407, 10.3390/s21134407.

[27] S. Liu, J. Xu, D. Lu, A measure for image segmentation of point cloud data and tunnel deformation detection, Tunn. Constr. 41 (2021) 531. (in Chinese)

[28] H. Huang, Q. Li, D. Zhang, Deep learning based image recognition for crack and leakage defects of metro shield tunnel, Tunn. Undergr. Space Technol. 77 (2018) 166-176, doi: 10.1016/j.tust.2018.04.002.

[29] K. Yang, Y. Bao, J. Li, T. Fan, C. Tang, Deep learning-based YOLO for crack seg- mentation and measurement in metro tunnels, Autom. Constr. 168 (2024) 105818, doi: 10.1016/j.autcon.2024.105818.

[30] C. Zhang, X. Chen, P. Liu, B. He, W. Li, T. Song,Automated detection and segmenta- tion of tunnel defects and objects using YOLOv8-CM, Tunn. Undergr. Space Technol. 150 (2024) 105857, doi: 10.1016/j.tust.2024.105857.

[31] S. Duan, M. Zhang, S. Qiu, J. Xiong, H. Zhang, C. Li, Q. Jiang, Y. Kou, Tunnel lining crack detection model based on improved YOLOv5, Tunn. Undergr. Space Technol. 147 (2024) 105713, doi: 10.1016/j.tust.2024.105713.

[32] J. Zhou, Y. Gao, X. Yang, Z. Liu, Lightweight tunnel obstacle detection based on improved YOLOv5, Sensors 24 (2024) 395, 10.3390/s24020395.

[33] Y. Wang, Y. Li, J. Zhang, MFF-YOLO: an accurate model for detecting tunnel defects based on multi-feature fusion, Sensors 23 (2023) 6490, 10.3390/s23146490.

[34] Z. Wang, Z. Cai, Y. Wu, An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites, J. Comput. Des. Eng. 10 (2023) 1158-1175, doi: 10.1093/jcde/qwad042.

[35] Y. Liu, Y. Wang, Z. Li, Automated 3D defect inspection in shield tunnel lin- ings through multisource data fusion and BIM, Intell. Transp. Infrastruct. (2025), doi: 10.1007/s43503-025-00054-w.

[36] A. Yu, W. Mei, Deep-learning-based longitudinal joint opening detection method for metro shield tunnel, Tunn. Undergr. Space Technol. 154 (2024) 106108, doi: 10.1016/j.tust.2024.106108.

[37] S.J. Feng, Y. Feng, X.L. Zhang, Y.H. Chen, Deep learning with visual explanations for leakage defect segmentation of metro shield tunnel, Tunn. Undergr. Space Technol. 136 (2023) 105107, doi: 10.1016/j.tust.2023.105107.

[38] D.-Y. Duan, W.-G. Qiu, Y.-J. Cheng, Y.-C. Zheng, F. Lu, Reconstruction of shield tunnel lining using point cloud, Autom. Constr. 130 (2021) 103860, doi: 10.1016/j.autcon.2021.103860.

[39] Ministry of Transport of the People’s Republic of China, specifications for operational monitoring of urban rail transit facilities —part 3 : tunnel, (2020). (in Chinese)