Automatic Detection of Concrete Surface Defects Using Deep Learning and Laser Ultrasonic Visualization Testing

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Published Sep 4, 2023
Takahiro Saitoh Syumpei Ohyama Tsuyoshi Kato Sohichi Hirose

Abstract

In recent years, nondestructive testing of civil engineering structures has become increasingly important. Non-destructive inspection methods for civil engineering structures generally use ultrasonic waves. 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. Therefore, this study proposes a laser ultrasonic visualization testing using AI. The proposed method will be applied to a concrete structure with surface defects to confirm the effectiveness of the proposed method.

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Keywords

Deep learning, Ultrasonic nondestructive testing, Laser ultrasonic visualization testing, Wave propagation

References
Achenbach, J. D. (2004). Reciprocity in elastodynamics. Cambridge University Press.

Chollet, F. (2017). Deep learning with python. Manning Publications.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 2672-2680. doi: https://doi.org/10.48550/arXiv.1406.2661

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

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

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.

Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G. (2022). Learning from noisy labels with deep neural networks: A survey. doi: https://doi.org/10.48550/arXiv.2007.08199

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
Section
Special Session Papers