Multi-modal Classification Using Domain Adaptation for Automated Defect Detection Based on the Hammering Test

Ushiroda, Keitaro; Kasahara, Jun Younes Louhi; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/sii52469.2022.9708607

Cited by 4 publications

(2 citation statements)

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“…The SVM method exhibited a worst-case predictive value of 72% and a best-predicted value of 99%. Another study [12] At the product level, tools like T.T.Car [5] and AI Hammering Test Checker [6] employ hammering tests. T.T.Car creates a problem area map by moving along measurement lines drawn on the road but cannot inspect wall surfaces.…”

Section: Related Workmentioning

confidence: 99%

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

Ito,

Koike,

Hibino

2024

International Journal of Electronics and Telecommunications

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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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Section: Related Workmentioning

confidence: 99%

“…In the case of SVM, the performance is 99% when it is good, but when it is bad, it is about 72%. According to the [12], in the case of multimodal, images and percussion are combined, and a decision using SVM is put into it. The F value, in this case, is shown to be about 0.73.…”

Section: Related Workmentioning

confidence: 99%