Smart Structures and Systems

  • 제32권2호
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  • Pages.123-133
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  • 2023
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Thermography-based coating thickness estimation for steel structures using model-agnostic meta-learning

  • Jun Lee (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Soonkyu Hwang (Yield Enhancement Team, Global Infra Technology, Samsung Electronics) ;
  • Kiyoung Kim (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Hoon Sohn (Department of Civil Engineering, Korean Advanced Institute for Science and Technology)
  • 투고 : 2022.11.24
  • 심사 : 2023.07.20
  • 발행 : 2023.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

키워드

과제정보

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [Grant Number 2019R1A3B3067987].

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