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Title |
Damage Detection of Non-Ballasted Plate-Girder Railroad Bridge through Machine Learning based on Static Strain Data
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Authors |
문태욱(Taeuk Moon) ; 신수봉(Soobong Shin) |
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DOI |
https://doi.org/10.11112/jksmi.2020.24.6.206 |
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Keywords |
무도상 철도 판형교; 머신러닝; 손상 탐지; 정적 변형률; Local 손상지수 Non-ballasted railway bridge; Machine Learning; Damage detection; static strain; Local damage index |
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Abstract |
As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.
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