Abstract
To maintain structural integrity and avoid structural failures that could harm neighboring infrastructure, pollute the environment, and even result in fatalities, routine inspection and repair of concrete infrastructure are required. Throughout the structure’s operational life, routine visual inspections are typically undertaken to detect various problems caused by environmental exposure (such as cracks, loss of material, rusting of metal bindings, etc.). Visual examination can yield a variety of data that may enable the cause of distress to be positively identified. Its effectiveness is subject to human error and depends on the investigator’s skill and experience and because of their size and difficult-to-reach features, huge structures like dams, bridges, and tall skyscrapers can be prohibitively dangerous. The approach presented here uses deep learning techniques to identify the structural cracks on concrete surfaces to achieve easy detection of the cracks and high accuracy. Here, we propose an integrated Tensrflow CNN and image processing-based crack-finding method to detect cracks with high precision. Thousands of photos of cracked and non-cracked structure surface datasets are considered while developing the model. Image features are extracted during pre-processing to increase training effectiveness. The developed model has a 97.11% accuracy rate and an F1-score of 97%. The results show that the designed model is highly precise and effective in identifying cracks in structures and more accurate than many implemented techniques.
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References
Arun, M., & Poobal, S. (2018). Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal., 57, 787–798. https://doi.org/10.1016/j.aej.2017.01.020
Beck, A., & Teboulle, M. (2009). ‘‘Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems.’ IEEE Transactions on Image Processing, 18(11), 2419–2434. https://doi.org/10.1109/TIP.2009.2028250
Cao, W., Liu, Q., & He, Z. (2020). Review of pavement defect detection methods. IEEE Access, 8, 14531–14544. https://doi.org/10.1109/ACCESS.2020.2966881
Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). ‘Deep learning-based crack damage detection using convolutional neural networks.’ Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263
Cho, H., Yoon, H.-J., & Jung, J.-Y. (2018). Image-based crack detection using crack width transform (CWT) algorithm. IEEE Access, 6, 60100–60114. https://doi.org/10.1109/ACCESS.2018.2875889
Danajitha, K.K., Sheeba, A., Sophiya, P., & Maha Vishnu, V.C. (2022). "Detection of Cracks in High Rise Buildings using Drones," International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 318–324, https://doi.org/10.1109/ICESC54411.2022.9885251
Dong, Y., Wag, J., Wang, Z., Zhang, X., Gao, Y., Sui, Q., & Jiang, P. (2019). A deep-learning-based multiple defect detection method for tunnel lining damages. IEEE Access, 7, 182643–182657. https://doi.org/10.1109/ACCESS.2019.2931074
Fan, R., Bocus, M. J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., & Liu, M. (2019). Road crack detection using deep convolutional neural network and adaptive thresholding. IEEE Intelligent Vehicles Symposium. https://doi.org/10.48550/arXiv.1904.08582
Gui, Z., & Li, H. (2020). Automated defect detection and visualization for the robotic airport runway inspection. IEEE Access, 8, 76100–76107. https://doi.org/10.1109/ACCESS.2020.2986483
Han, D. J., Zhao, Qi., Wang, L., & Yin, K. (2018). Comparison of random forest, artificial neural networks, and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control., 40, 2681–2693. https://doi.org/10.1177/0142331217708242
Hassene, H., Alavi, A. H., Pengcheng, J., & Nizar, L. (2017). Detection of fatigue cracking in steel bridge girders: A support vector machine approach. Archives of Civil and Mechanical Engineering, 17, 609–622. https://doi.org/10.1016/j.acme.2016.11.005
He, K., Zhang, X., Ren, S., & Sun, J. (2015). ‘Spatial pyramid pooling in deep convolutional networks for visual recognition.’ IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904–1916. https://doi.org/10.1007/978-3-319-10578-9_23
Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering. Springer. ISBN: 978-3-319-48012-1.
Kaveh, A., & Dadras, A. (2018). Structural damage identification using enhanced thermal exchange optimization algorithm. Engineering Optimization, 50(3), 430–451. https://doi.org/10.1080/0305215X.2017.1318872
Kaveh, A., & Maniat, M. (2014). Damage detection in skeletal structures based on charged system search optimization using incomplete modal data. International Journal of Civil Engineering IUST, 12(2), 291–298.
Kaveh, A., & Maniat, M. (2015). Damage detection based on MCSS and PSO using modal data. Smart Structures and Systems, 15(5), 1253–1270. https://doi.org/10.12989/SSS.2015.15.5.1253
Kaveh, A., & Zolghadr, A. (2012). An improved charged system search for structural damage identification in beams and frames using changes in natural frequencies. International Journal of Optimization in Civil Engineering, 2(3), 321–340.
Kaveh, A., & Zolghadr, A. (2017a). Cyclical parthenogenesis algorithm for guided modal strain energy-based structural damage detection. Applied Soft Computing, 57, 250–264. https://doi.org/10.1016/j.asoc.2017.04.010
Kaveh, A., & Zolghadr, A. (2017). Guided modal strain energy-based approach for structural damage identification using tug-of-war optimization algorithm. Journal of Computing in Civil Engineering. https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000665
Lionnie, R., Ramadhan, R. C., Rosyadi, A. S., Jusoh, M., & Alaydrus, M. (2022). Performance analysis of various types of surface crack detection based on image processing. SINERGI. https://doi.org/10.22441/sinergi.2022.1.001
Mao, Y., Chen, J., Ping, P., & Chen, H. (2020). Crack detection with multi-task enhanced faster R-CNN model. IEEE Sixth International Conference on Big Data Computing Service and Applications. https://doi.org/10.1109/BigDataService49289.2020.00038
Oh, J. K., Jang, G., Oh, S., Lee, J. H., Yi, B. J., Moon, Y. S., Lee, J. S., & Choi, Y. (2009). ‘Bridge inspection robot system with machine vision.’ Automation in Construction, 18(7), 929–941. https://doi.org/10.1016/j.autcon.2009.04.003
Qu, Z., Mei, J., Liu, L., & Zhou, D. Y. (2020). ‘Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model.’ IEEE Access, 8, 54564–54573. https://doi.org/10.1109/ACCESS.2020.2981561
Ren, S., He, K., Girshick, R., & Sun, J. (2017). ‘Faster R-CNN: Towards real-time object detection with region proposal networks.’ IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Salman, M., Mathavan, S., Kamal, K., & Rahman, M. (2013). ‘‘Pavement crack detection using the Gabor filter,’’ in Proceeding 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, pp. 2039–2044, https://doi.org/10.1109/ITSC.2013.6728529
Shan, Q., & Dewhurst, R. J. (1998). Surface—breaking fatigue crack detection using laser ultrasound. Applied Physics Letters, 62, 2649–2651. https://doi.org/10.1063/1.109274
Sizyakin, R., Cornelis, B., Meeus, L., Dubois, H., Martens, M., Voronin, V., & Pižurica, A. (2020). Crack detection in paintings using convolutional neural networks. IEEE Access, 8, 74535–74552. https://doi.org/10.1109/ACCESS.2020.2988856
Song, W., Jia, G., Jia, D., & Zhu, H. (2019). ‘Automatic pavement crack detection and classification using multiscale feature attention network.’ IEEE Access, 7, 171001–171012. https://doi.org/10.1109/ACCESS.2019.2956191
Sun, Y., Yang, Y., Yao, G., Wei, F., & Wong, M. (2021). Autonomous crack and bughole detection for concrete surface image based on deep learning. IEEE Access, 9, 85709–85720.
Yang, J., Wang, W., Lin, G., Li, Q., Sun, Y., & Sun, Y. (2019). Infrared thermal imaging-based crack detection using deep learning. IEEE Access, 7, 182060–182077. https://doi.org/10.1109/ACCESS.2019.2958264
Yao, G., Wei, F., Yang, Y., & Sun, Y. (2019). ‘Deep-learning-based bug hole detection for concrete surface image.’ Advances in Civil Engineering, 2019, 1–12. https://doi.org/10.1155/2019/8582963
Yeum, C. M., & Dyke, S. J. (2015). ‘Vision-based automated crack detection for bridge inspection.’ Computer-Aided Civil and Infrastructure Engineering, 30(10), 759–770. https://doi.org/10.1111/mice.12141
Zhang, L., Zhou, G., Han, Y., Lin, H., & Wu, Y. (2018). ‘Application of internet of things technology and convolutional neural network model in bridge crack detection.’ IEEE Access, 6, 39442–39451. https://doi.org/10.1109/ACCESS.2018.2855144
Zhou, Q., Zhong, Qu., & Cao, C. (2021). Mixed pooling and richer attention feature fusion for crack detection. Pattern Recognition Letters, 145, 96–102. https://doi.org/10.1016/j.patrec.2021.02.005
Zinno, R., Haghshenas, S. S., Guido, G., & Vitale, A. (2022). A27 “artificial intelligence and structural health monitoring of bridges: A review of the state-of-the-art,.” IEEE Access, 10, 88058–88078. https://doi.org/10.1109/ACCESS.2022.3199443
Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. (2012). CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letter, 33, 227–238. https://doi.org/10.1016/j.patrec.2011.11.004
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Shashidhar, R., Manjunath, D. & Shanmukha, S.M. CrackSpot: deep learning for automated detection of structural cracks in concrete infrastructure. Asian J Civ Eng 25, 1079–1090 (2024). https://doi.org/10.1007/s42107-023-00754-7
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DOI: https://doi.org/10.1007/s42107-023-00754-7