Automatic Detection of Concrete Surface Defects Using PRE-TRAINED CNN and Laser Ultrasonic Visualization Testing
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Published
Oct 8, 2024
Takahiro Saitoh
Tsuyoshi Kato
Sohichi Hirose
Abstract
In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens.
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Keywords
convolutional neural network, nondestructive ultrasonic testing, defect detection, deep learning, Grad-CAM
References
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Chimenti, D. (2014). Review of air-coupled ultrasonic materials characterization. Ultrasonics, 54(7), 1804-1816. doi: https://doi.org/10.1016/j.ultras.2014.02.006
Chollet, F. (2017). Deep learning with python. Manning Publications.
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 2672-2680. doi: https://doi.org/10.48550/arXiv.1406.2661
Han, X., Yang, Y., & Liu, Y. (2022). Determining the defect locations and sizes in elastic plates by using the artificial neural network and boundary element method. Engineering Analysis with Boundary Elements, 139, 232-245. doi: 10.1016/j.enganabound.2022.03.030
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (770-778). doi: 10.1109/CVPR.2016.90
Helal, J., Sofi, M., & Mendis, P. (2015). Non-destructive testing of concrete: A review of methods. Special Issue Electron. J. Struct. Eng, 14(1), 97-105. doi: doi:10.56748/ejse.141931
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 25). Curran Associates, Inc.
Köhler, B., & Schubert, F. (2002). Optical detection of elastodynamic fields of ultrasonic transducers. Ultrasonics, 40(1), 741-745. doi: https://doi.org/10.1016/S0041-624X(02)00204-4
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Modarres, M., & Keshtgar, A. (2016). Probabilistic approach for nondestructive detection of fatigue crack initiation and sizing. International Journal of Prognostics and Health Management, 7, 1-10. doi: 10.36001/ijphm.2016.v7i2.2403
Nakajima, M., Saitoh, T., & Kato, T. (2022). A study on deep cnn structures for defect detection from laser ultrasonic
visualization testing images. Artificial Intelligence and Data Science, 3(J2), 916-924 (in Japanese). doi: 10.1006/jabr.1994.1114
Nakajima, M., Saitoh, T., & KATO, T. (2024). Simulation-aided deep learning for laser ultrasonic visualization testing with style transfer. Intelligence, Informatics and Infrastructure, 5(1), 25-33. doi: 10.11532/jsceiiai.5.1_25
Pan, S. J., & Yang, Q. Y. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.
Rao, M., Yang, X., Wei, D., Chen, Y., Meng, L., & Zuo, M. (2021). Structure fatigue crack length estimation and prediction using ultrasonic wave data based on ensemble linear regression and paris’s law. International Journal of Prognostics and Health Management, 11. doi: 10.36001/ijphm.2020.v11i2.2923
Rose, J. L. (2008). Ultrasonic waves in solid media. Cambridge University Press.
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Saitoh, T., Kato, T., & Hirose, S. (2021). Deep learning for scattered waves obtained by time-domain boundary element method and an attempt to classify defect types. Journal of JSNDI, 70(7), 272-279 (in Japanese). doi: 10.1006/jabr.1994.1114
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Skalskyi, V., Nazarchuk, Z., & Stankevych, O. (2022). Acoustic emission: Fracture detection in structural materials (foundations of engineering mechanics). Springer.
Takatsubo, J., Miyauchi, M., Tsuda, H., Toyama, N., Urabe, K., & Wang, B. (2008). Generation laser scanning method for visualizing ultrasonic waves propagating on a 3-D object. In 1st international symposium on laser ultrasonics: Science, technology and applications.
Yashiro, S., Toyama, N., Takatsubo, J., & Shiraishi, T. (2010). Laser-generation based imaging of ultrasonic wave propagation on welded steel plates and its application to defect detection. Materials Transaction, 51(11), 2069-2075. doi: https://doi.org/10.2320/matertrans.M2010204
Ye, J., Ito, S., & Toyama, N. (2018). Computerized ultrasonic imaging inspection: From shallow to deep learning. Sensors, 18(11), 3820. doi: 10.3390/s18113820
Chimenti, D. (2014). Review of air-coupled ultrasonic materials characterization. Ultrasonics, 54(7), 1804-1816. doi: https://doi.org/10.1016/j.ultras.2014.02.006
Chollet, F. (2017). Deep learning with python. Manning Publications.
Deng, J., Socher, R., Fei-Fei, L., Dong,W., Li, K., & Li, L.-J. (2009, 06). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR) (248-255). doi: 10.1109/CVPR.2009.5206848
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 2672-2680. doi: https://doi.org/10.48550/arXiv.1406.2661
Han, X., Yang, Y., & Liu, Y. (2022). Determining the defect locations and sizes in elastic plates by using the artificial neural network and boundary element method. Engineering Analysis with Boundary Elements, 139, 232-245. doi: 10.1016/j.enganabound.2022.03.030
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (770-778). doi: 10.1109/CVPR.2016.90
Helal, J., Sofi, M., & Mendis, P. (2015). Non-destructive testing of concrete: A review of methods. Special Issue Electron. J. Struct. Eng, 14(1), 97-105. doi: doi:10.56748/ejse.141931
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems (Vol. 25). Curran Associates, Inc.
Köhler, B., & Schubert, F. (2002). Optical detection of elastodynamic fields of ultrasonic transducers. Ultrasonics, 40(1), 741-745. doi: https://doi.org/10.1016/S0041-624X(02)00204-4
Meng, M., Chua, Y. J., Wouterson, E., & Ong, C. P. K. (2017). Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks. Neurocomputing, 257, 128-135. doi: https://doi.org/10.1016/j.neucom.2016.11.066
Modarres, M., & Keshtgar, A. (2016). Probabilistic approach for nondestructive detection of fatigue crack initiation and sizing. International Journal of Prognostics and Health Management, 7, 1-10. doi: 10.36001/ijphm.2016.v7i2.2403
Nakajima, M., Saitoh, T., & Kato, T. (2022). A study on deep cnn structures for defect detection from laser ultrasonic
visualization testing images. Artificial Intelligence and Data Science, 3(J2), 916-924 (in Japanese). doi: 10.1006/jabr.1994.1114
Nakajima, M., Saitoh, T., & KATO, T. (2024). Simulation-aided deep learning for laser ultrasonic visualization testing with style transfer. Intelligence, Informatics and Infrastructure, 5(1), 25-33. doi: 10.11532/jsceiiai.5.1_25
Pan, S. J., & Yang, Q. Y. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.
Rao, M., Yang, X., Wei, D., Chen, Y., Meng, L., & Zuo, M. (2021). Structure fatigue crack length estimation and prediction using ultrasonic wave data based on ensemble linear regression and paris’s law. International Journal of Prognostics and Health Management, 11. doi: 10.36001/ijphm.2020.v11i2.2923
Rose, J. L. (2008). Ultrasonic waves in solid media. Cambridge University Press.
Saitoh, T., Hirose, S., Fukui, T., & Ishida, T. (2007). Development of a time-domain fast multipole BEM based on the operational quadrature method in a wave propagation problem. Advances in Boundary Element Techniques VIII, 355-360.
Saitoh, T., Kato, T., & Hirose, S. (2021). Deep learning for scattered waves obtained by time-domain boundary element method and an attempt to classify defect types. Journal of JSNDI, 70(7), 272-279 (in Japanese). doi: 10.1006/jabr.1994.1114
Saitoh, T., Mori, A., Ooashi, K., & Nakahata, K. (2019). Development of a new dynamic elastic constant estimation method for frp and its validation using the fdtd method. Insight- Non-Destructive Testing and Condition Monitoring, 61(3), 162-165.
Schmerr, L. W. (1998). Fundamentals of ultrasonic nondestructive evaluation. Plenum Press.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) (618-626). doi: 10.1109/ICCV.2017.74
Skalskyi, V., Nazarchuk, Z., & Stankevych, O. (2022). Acoustic emission: Fracture detection in structural materials (foundations of engineering mechanics). Springer.
Takatsubo, J., Miyauchi, M., Tsuda, H., Toyama, N., Urabe, K., & Wang, B. (2008). Generation laser scanning method for visualizing ultrasonic waves propagating on a 3-D object. In 1st international symposium on laser ultrasonics: Science, technology and applications.
Yashiro, S., Toyama, N., Takatsubo, J., & Shiraishi, T. (2010). Laser-generation based imaging of ultrasonic wave propagation on welded steel plates and its application to defect detection. Materials Transaction, 51(11), 2069-2075. doi: https://doi.org/10.2320/matertrans.M2010204
Ye, J., Ito, S., & Toyama, N. (2018). Computerized ultrasonic imaging inspection: From shallow to deep learning. Sensors, 18(11), 3820. doi: 10.3390/s18113820
Section
Technical Papers