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Smart Structures and Systems Volume 33, Number 2, February 2024 , pages 93-103 DOI: https://doi.org/10.12989/sss.2024.33.2.093 |
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 open accessDeep learning-based anomaly detection in acceleration data of long-span cable-stayed bridges |
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Seungjun Lee, Jaebeom Lee, Minsun Kim, Sangmok Lee and Young-Joo Lee
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| Abstract | ||
| 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. | ||
| Key Words | ||
| anomaly patterns; deep neural network (DNN); feature extraction; sensor data; structural health monitoring (SHM) | ||
| Address | ||
| (1) Seungjun Lee, Minsun Kim, Young-Joo Lee: Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, Republic of Korea; (2) Jaebeom Lee: Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), 267, Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea; (3) Sangmok Lee: Dam Safety Management Center, Korea Water Resources Corporation (K-water), 200, Sintanjin-ro, Daedeok-gu, Daejeon, Republic of Korea. | ||