Smart non-destractive test of a concrete wall using a hammer

Atsushi Ito, Masafumi Koike, Katsuhiko Hibino

Abstract


Large concrete structures such as buildings, bridges, and tunnels are aging. In Japan and many other countries, those built during economic reconstruction after World War II are about 60 to 70 years old, and flacking and other problems are becoming more noticeable. Periodic inspections were made mandatory by government and ministerial ordinance during the 2013-2014 fiscal year, and inspections based on the new standards have just begun. There are various methods to check the soundness of concrete, but the hammering test is widely used because it does not require special equipment. However, long experience is required to master the hammering test. Therefore, mechanization is desired. Although the difference between the sound of a defective part and a normal part is very small, we have shown that neural network is useful in our research. To use this technology in the actual field, it is necessary to meet the forms of concrete structures in various conditions. For example, flacking in concrete exists at various depths, and it is impossible to learn about flacking in all cases. This paper presents the results of a study of the possibility of finding flacking at different depths with a single inspection learning model and an idea to increase the accuracy of a learning model when we use a rolling hammer.


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References


”White Pater of Ministry of Land, Infrastructure, Transport and Tourism in 2013 (in Japanese)”, 2013, http://www.mlit.go.jp/hakusyo/mlit/h25/ hakusho/h26/html/n1131000.html

”Infrastructure Maintenance Information (in Japanese)”, 2023, https:// www.mlit.go.jp/sogoseisaku/maintenance/02research/index.html

”Statistics and data related to condominiums, etc. (in Japanese)”, 2023, https://www.mlit.go.jp/jutakukentiku/house/jutakukentiku house tk5 000058.html

”TERRA DRONE”, 2023, https://www.terra-drone.net/global/

”T.T.Car: Hammering Inspection System (in Japanese)”, 2023, http://

www.daon.jp/

”AI Hammering Checker (in Japanese)”, http://www.port-d.co.jp/p

pdc-100.htm#toi jmp

”Teachable Machine”, 2023, https://teachablemachine.withgoogle.com/

train/audio

”An introduction to

AI for dummies”,

an- introduction- to- teachable- machine- ai- for- dummies- 61d1f97f5cf

S. J. Pan and Q. Yang, ”A Survey on Transfer Learning”, IEEE Trans- actions on Knowledge and Data Engineering, Vol.22, No.10, pp.1345- 1359, doi: 10.1109/TKDE.2009.191

T. Fukumura, H. Aratame, A. Ito, M. Koike, K. Hibino and Y. Kawa- mura, ”Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network”, International Journal of Networking and Computing, Vol.12, No.2, ISSN 2185-2847, 2022

Kadir Gu ̈c ̧lu ̈er, Abdurrahman O ̈zbeyaz, Samet Go ̈ymen, Osman Gu ̈naydın, ”A comparative investigation using machine learning methods for concrete compressive strength estimation,” Materi- als Today Communications, Vol.27, pp.102278, ISSN 2352-4928, doi.org/10.1016/j.mtcomm.2021.102278

K.Ushiroda, J.Y.Louhi, Kasahara, A. Yamashita and H. Asama, ”Multi- modal Classification Using Domain Adaptation for Automated Defect Detection Based on the Hammering Test,” 2022 IEEE/SICE Interna- tional Symposium on System Integration (SII), pp.991-996, 2022, doi: 10.1109/SII52469.2022.9708607

S. Kharkovsky and R. Zoughi, ”Microwave and millimeter wave nonde- structive testing and evaluation - Overview and recent advances,” Vol.10, No.2, pp.26-38, doi: 10.1109/MIM.2007.364985

J, Wang and T. Ueda, ”A review study on unmanned aerial ve- hicle and mobile robot technologies on damage inspection of re- inforced concrete structures,” Structural Concrete, Vol.24, No.1, doi.org/10.1002/suco.202200846

Yandan Jiang, Lai Wang, Bo Zhang, Xiaowei Dai, Jun Ye, Bochao Sun, Nianwu Liu, Zhen Wang, Yang Zhao, ”Tunnel lining detection and retrofitting,” Automation in Construction, Vol.152, pp.104881, 2023, ISSN = 0926-5805, doi.org/10.1016/j.autcon.2023.104881

Bo Chen, Hua Zhang, Guijin Wang, Jianwen Huo, Yonglong Li, Linjing Li, ”Automatic concrete infrastructure crack semantic segmentation using deep learning”, Vol.152, pp.104950, 2023, ISSN = 0926-5805, doi.org/10.1016/j.autcon.2023.104950

G Karaiskos and A Deraemaeker and D G Aggelis and D Van Hemelrijck, ”Monitoring of concrete structures using the ultrasonic pulse velocity method”, Smart Materials and Structures, Vo.24, No.11, pp.113001, 2015, dx.doi.org/10.1088/0964-1726/24/11/113001

Stamos Katsigiannis, Saleh Seyedzadeh, Andrew Agapiou, Naeem Ramzan, ”Deep learning for crack detection on masonry fac ̧ ades using limited data and transfer learning”, Journal of Building Engineering, Vol.76, pp.107105, ISSN 2352-7102, doi.org/10.1016/j.jobe.2023.107105

Howard, Andrew and Sandler, Mark and Chen, Bo and Wang, Wei- jun and Chen, Liang-Chieh and Tan, Mingxing and Chu, Grace and Vasudevan, Vijay and Zhu, Yukun and Pang, Ruoming and Adam, Hartwig and Le, Quoc, ”Searching for MobileNetV3”, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp.1314-1324, 2019, doi.org/10.1109/ICCV.2019.00140


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