Development of an automated surface crack detection and BIM-integrated management system for concrete bridges
DOI: https://doi.org/10.3846/jcem.2025.24094Abstract
Bridge inspection work typically requires inspectors to capture hundreds to thousands of images, consuming substantial time for review. This research developed an “Automated Crack Image Cloud Detection System” and the “Auto Predictor” application, enabling automatic crack identification and deterioration image recognition through uploads. This platform integrates with the “Bridge BIM Cloud Management System”, connecting crack information with three-dimensional models. Engineers can create BIM models based on structural design drawings, while inspectors can photograph cracks and integrate relevant information. The study utilized deterioration images from long-term bridge inspections in Taiwan, covering various real-world environmental conditions. Through effective deterioration labeling strategies and comparing YOLOv4 and YOLOv7 algorithms with recommended parameters, an optimal model was obtained for system implementation. Research results demonstrate that the “Automated Crack Image Cloud Detection System” successfully identified cracks in bridge inspections and short beam shear test specimens. The deep integration with the “Bridge BIM Cloud Management System” enables automatic component crack identification and generates location charts, providing decision-makers with intuitive visual data. The YOLOv7-based model achieved a mean Average Precision (mAP) of 87.64%, significantly improving bridge inspection efficiency and demonstrating exceptional application potential.
Keywords:
bridge inspection, Deep Learning, Artificial Intelligence, crack detection, automation, BIM, managementHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
ASCII Table. (2022). ASCII table mapping. https://www.asciitable.com
Azhar, S., Brown, J., & Farooq, B. (2012). Building Information Modeling (BIM): A new paradigm for the design and management of construction projects. Journal of Construction Engineering and Management, 138(5), 575–585. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000527
Berners-Lee, T., Fielding, R., & Frystyk, H. (1996). Hypertext transfer protocol--HTTP/1.0 (RFC 1945). https://doi.org/10.17487/rfc1945
Beskopylny, A. N., Shcherban’, E. M., Stel’makh, S. A., Mailyan, L. R., Meskhi, B., Razveeva, I., Kozhakin, A., El’shaeva, D., Beskopylny, N., & Onore, G. (2023). Detecting cracks in aerated concrete samples using a convolutional neural network. Applied Sciences, 13(3), Article 1904. https://doi.org/10.3390/app13031904
Bevc, L., Mahut, B., & Grefstad, K. (1999). Review of current practice for assessment of structural condition and classification of defects (Technical Report). BRIME Project (Bridge Management in Europe), Deliverable D2.
Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv. https://doi.org/10.48550/arXiv.2004.10934
Byun, N., Han, W. S., Kwon, Y. W., & Kang, Y. J. (2021). Development of BIM-based bridge maintenance system considering maintenance data schema and information system. Sustainability, 13(9), Article 4858. https://doi.org/10.3390/su13094858
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Lecture notes in computer science. Vol. 12346: Computer vision – ECCV 2020 (pp. 213–229). https://doi.org/10.1007/978-3-030-58452-8_13
Chan, B. (2019). Current status and prospects of image recognition and deep learning in intelligent transportation systems. Chinese Technology, 123, 342–353.
Chang, C. Y., Chen, L. C., & Ma, C. C. (2017). Three-dimensional measurement of dynamic full-field displacement by stereo DIC using one high-speed camera. In 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT2017). Singapore.
Dang, J., Chun, P. J., Mizumoto, T., Liu, J., & Fujishima, T. (2021). Multi-type bridge damage detection method based on YOLO. Intelligence, Informatics and Infrastructure, 2, 447–456. https://doi.org/10.11532/jsceiii.2.J2_447
Davila Delgado, J. M., Butler, L. J., Gibbons, N., Brilakis, I., Elshafie, M. Z. E. B., & Middleton, C. (2017). Management of structural monitoring data of bridges using BIM. Bridge Engineering, 170(3), 204–218. https://doi.org/10.1680/jbren.16.00013
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In The Ninth International Conference on Learning Representations.
Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., & Berners-Lee, T. (1999). Hypertext Transfer Protocol--HTTP/1.1 (RFC 2616). https://doi.org/10.17487/rfc2616
Google. (2021). A colaboratory editing environment allows you to combine executable code [Web-based notebook]. https://colab.research.google.com/notebooks/intro.ipynb
Guo, C., Lv, X., Zhang, Y., & Zhang, M. (2021). Improved YOLOv4-tiny network for real-time electronic component detection. Scientific Reports, 11(1), Article 22744. https://doi.org/10.1038/s41598-021-02225-y
Hsieh, C. Y. (2018). Integration of image processing, computer vision, and artificial intelligence to identify concrete surface cracks [Master’s thesis]. Department of Civil Engineering, National Taiwan University.
Hsu, S. H., Chang, T. W., & Chang, C. M. (2021). Concrete surface crack segmentation based on deep learning. In P. Rizzo, & A. Milazzo (Eds.). Lecture notes in civil engineering. Vol 128: European workshop on structural health monitoring (EWSHM 2020) (pp. 24–34). Springer, Cham. https://doi.org/10.1007/978-3-030-64908-1_3
Jailia, M., Kumar, A., Agarwal, M., & Sinha, I. (2016). Behavior of MVC (Model View Controller) based web application developed in PHP and .NET framework. In 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India. IEEE. https://doi.org/10.1109/ICTBIG.2016.7892651
Kassem, M., & Succar, B. (2015). A conceptual framework for BIM-enabled asset management. Journal of Facilities Management, 13(2), 162–179.
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022). Transformers in vision: A survey. ACM Computing Surveys, 54(10), 1–41. https://doi.org/10.1145/3505244
Kivimäki, T., & Heikkilä, R. (2010). Bridge information modelling (BrIM) and model utilization at worksites in Finland. In Proceedings of the 27th International Symposium on Automation and Robotics in Construction (ISARC) (pp. 505–513), Bratislava, Slovakia. https://doi.org/10.22260/ISARC2010/0054
Krishnamurthy, B., Mogul, J. C., & Kristol, D. M. (1999). Key differences between HTTP/1.0 and HTTP/1.1. Computer Networks, 31(11–16), 1737–1751. https://doi.org/10.1016/S1389-1286(99)00008-0
Kruachottikul, P., Cooharojananone, N., Phanomchoeng, G., Muangsiri, W., & Silapachote, P. (2021). Deep learning-based visual defect-inspection system for reinforced concrete bridge substructure: A case of Thailand’s department of highways. Journal of Civil Structural Health Monitoring, 11, 949–965. https://doi.org/10.1007/s13349-021-00490-z
Laravel. (2022). The PHP framework for web artisans. https://laravel.com/
Lauridsen, J., & Lassen, B. (1999). The Danish bridge management system DANBRO. In P. C. Das (Ed.), Management of highway structures (pp. 5–10). Thomas Telford.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Journal of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Li, H., & Zhang, Y. (2022). Enhanced bridge maintenance management using BIM technology. Journal of Infrastructure Systems, 28(1), Article 04022001. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000575
Li, R., Yu, J., Li, F., Yang, R., Wang, Y., & Peng, Z. (2023). Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN. Construction and Building Materials, 362, Article 129659. https://doi.org/10.1016/j.conbuildmat.2022.129659
Lin, P. T., Yao, Y. T., Chen, Y. H., Lin, S. S., Chang, C. Y., Liu, K. Y., Lin, Y. H., & Lu, L. H. (2019). Stereovision-based automatic crack detection for 3D bridge inspection. In 2nd World Congress on Condition Monitoring (WCCM). Singapore.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada. https://doi.org/10.1109/ICCV48922.2021.00986
Lu, W. H., Lin, S. P., Lin, P. T., & Wu, Y. W. (2018). Development and applications of artificial intelligence image recognition system. In Proceedings of the 19th Conference on Nondestructive Testing Technology (CNDT). Taiwan.
Maqsood, T., & Memon, A. (2021). Integration of UAVs and BIM for bridge inspection and maintenance. Automation in Construction, 122, Article 103461. https://doi.org/10.1016/j.autcon.2020.103461
Miyamoto, A., & Motoshita, M. (2015). Development and practical application of a bridge management system (J-BMS) in Japan. Civil Engineering Infrastructures Journal, 48(1), 189–216.
Opara, J. N., Thein, A. B. B., Izumi, S., Yasuhara, H., & Chun, P. J. (2021). Defect detection on asphalt pavement by deep learning. International Journal of GEOMATE, 21(83), 87–94. https://doi.org/10.21660/2021.83.6153
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Python Software Foundation. (2023). Documentation for Python’s standard library, along with tutorials and guides. https://www.python.org/
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788), Las Vegas, NV, USA. IEEE. https://doi.org/10.1109/CVPR.2016.91
Rezaie, A., Achanta, R., Godio, M., & Beyer, K. (2020). Comparison of crack segmentation using digital image correlation measurements and deep learning. Construction and Building Materials, 261, Article 120474. https://doi.org/10.1016/j.conbuildmat.2020.120474
Song, Y. C., Chen, C. C., Lai, M. C., Hsu, C. C., Hung, H. H., & Liu, K. Y. (2014). Development of bridge lifecycle disaster prevention management system (Report No. NCREE-2014-027). National Center for Research on Earthquake Engineering.
Su, C. W., Zhang, S. Y., Yang, Y. W., Huang, C. H., Jiang, M. Y., Yao, N. J., Huang, R. Y., Yang, Z. B., Tsai, M. G., Yeh, C. C., Hsu, W. K., Jen, Y. Y., Liao, H. G., Chuang, Y. H., & Liao, A. Z. (2018). Planning for the establishment of the second-generation bridge management information system in Taiwan (Part three). Institute of Transportation, Ministry of Transportation and Communications.
Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2022). Efficient transformers: A survey. ACM Computing Surveys, 55(6), Article 109. https://doi.org/10.1145/3530811
Thompson, P. D., Small, E. P., Johnson, M., & Marshall, A. R. (1998). The Pontis bridge management system. Structural Engineering International, 8(4), 303–308. https://doi.org/10.2749/101686698780488758
Tong, Z., Gao, J., & Zhang, H. (2018). Innovative method for recognizing subgrade defects based on a convolutional neural network. Construction and Building Materials, 169, 69–82. https://doi.org/10.1016/j.conbuildmat.2018.02.081
Tzutalin. (2021). labelImg [Computer software]. GitHub. https://github.com/tzutalin/labelImg
Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 7464–7475), Vancouver, BC, Canada. IEEE. https://doi.org/10.1109/CVPR52729.2023.00721
WongKinYiu. (2023). Open source YOLOv7 [Code repository]. GitHub. https://github.com/WongKinYiu/yolov7
World Wide Web Consortium. (2022). Hypertext Transfer Protocol – HTTP/1.1. (Technical standard). https://www.w3.org/Protocols/rfc2616/rfc2616.html
Zhang, Q., Barri, K., Babanajad, S. K., & Alavi, A. H. (2021). Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain. Engineering, 7(12), 1786–1796. https://doi.org/10.1016/j.eng.2020.07.026
Zhang, Y., Zuo, Z., Xu, X., Wu, J., Zhu, J., Zhang, H., Wang, J., & Tian, Y. (2022). Road damage detection using UAV images based on multi-level attention mechanism. Automation in Construction, 144, Article 104613. https://doi.org/10.1016/j.autcon.2022.104613
Zhu, M. (2004). Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo.
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.