Mock-up Test를 통한 AI 및 열화상 기반 콘크리트 균열 깊이 평가 기법의 적용성 검증
Application Verification of AI&Thermal Imaging-Based Concrete Crack Depth Evaluation Technique through Mock-up Test
- 주제(키워드) Machine Learning , Concrete Crack Depth , Thermal Image , Structural Safety Test
- 발행기관 한국공간구조학회
- 발행년도 2023
- 총서유형 Journal
- KCI ID ART002996669
- 본문언어 한국어
초록/요약
With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.
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