A Defect Identification Method for Bonding Layers of Adhesive Steel Members Based on Machine Learning

YAO Hao; XIA Guiran; LIU Zejia; ZHOU Licheng

doi:10.21656/1000-0887.440365

Volume 45 Issue 4

Apr. 2024

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Article Navigation > Applied Mathematics and Mechanics > 2024 > 45(4): 429-442

YAO Hao, XIA Guiran, LIU Zejia, ZHOU Licheng. A Defect Identification Method for Bonding Layers of Adhesive Steel Members Based on Machine Learning[J]. Applied Mathematics and Mechanics, 2024, 45(4): 429-442. doi: 10.21656/1000-0887.440365

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A Defect Identification Method for Bonding Layers of Adhesive Steel Members Based on Machine Learning

doi: 10.21656/1000-0887.440365

1.
Guangzhou Communications Investment Group Co., Ltd., Guangzhou 510330, P.R.China
2.
School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510641, P.R.China

Received Date: 2023-12-25
Rev Recd Date: 2024-01-24
Publish Date: 2024-04-01

Abstract

Abstract

The effects of bonding layer defects on ultrasonic detection signals of bonded steel reinforced structures were deeply studied and a new method for the bonding layer defect identification based on machine learning was proposed. Firstly, based on the direct contact pulse-echo reflection method, the finite element simulation of the viscous steel member was carried out, and the propagation law of ultrasonic waves in the viscous steel member was expounded. Secondly, the characteristics of local ultrasonic echo signals and related signals were analyzed, and the effects of different defect variables on ultrasonic echo signals were discussed. Finally, the ultrasonic time-history response data set of the adhesive steel member was established, and the classification and recognition performances of different machine learning models for the size and location of defects were compared, and the defect identification method for the adhesive layer of the bonded steel member was built. The results show that, the local ultrasonic echo signal and its characteristics change regularly with the defect size and location, which can help preliminarily distinguish the defect information. Meanwhile, the proposed RF model-based defect identification method can effectively identify the defects of the adhesive layer in the bonded steel member, and has a broad engineering application prospect.
- ultrasonic testing,
- machine learning,
- bonded steel component,
- bonding layer defect

(Contributed by ZHOU Licheng, M.AMM Youth Editorial Board)

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References(21)

References

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