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Title |
Vibration-Based Structural Damage Detection Using Semi-Supervised Deep Learning Model
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Authors |
김기현(Gihyeon Kim) ; 김병현(Byunghyun Kim) ; 이사현(Sahyun Lee) ; 조수진(Soojin Cho) |
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DOI |
https://doi.org/10.11112/jksmi.2024.28.6.69 |
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Keywords |
반지도학습; 딥러닝; 진동 응답; 구조물 손상 탐지; LK-Block Encoder; 1-D CNN Semi-supervised learning; Deep learning; Vibration responses; Damage detection; LK-Block Encoder; 1-D CNN |
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Abstract |
This study proposes a framework for damage detection in structures through vibration responses using semi-supervised learning of an LK-Block Encoder, and validated through experiments with a shear building. The framework utilizes an LK-Encoder, which is composed of four serially connected 1-D CNN-based hidden layer blocks, and applies semi-supervised learning to set a threshold in the feature space for distinguishing between normal and damaged vibration data, thereby enabling damage detection from vibration data. To validate this approach, the study assumed damage scenarios by varying the thickness of shear building columns, and damage detection was conducted using the proposed framework. Results showed an accuracy of 97.03% for large damage condition. When the dataset of the normal condition was increased fourfold, small damage was detected with an accuracy of 99.66%. Additionally, it was confirmed that even when the model was trained with different types of damage from the target detection, increasing the amount of normal data improved damage detection accuracy. These results indicate that the proposed framework effectively detects even minor damage, which is difficult to identify through natural frequency analysis, with high accuracy.
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