Smart Structures and Systems

  • 제33권2호
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  • Pages.93-103
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  • 2024
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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

Deep learning-based anomaly detection in acceleration data of long-span cable-stayed bridges

  • Seungjun Lee (Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Jaebeom Lee (Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS)) ;
  • Minsun Kim (Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Sangmok Lee (Dam Safety Management Center, Korea Water Resources Corporation (K-water)) ;
  • Young-Joo Lee (Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
  • 투고 : 2023.03.02
  • 심사 : 2023.12.28
  • 발행 : 2024.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1C1C2008770), and the authors would like to thank the organizers of the 1st International Project Competition for SHM (IPC-SHM, 2020) for providing the invaluable data used in this paper.

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