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Lee, Young-Joo
Structural Reliability and Disaster Risk Lab.
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Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges

Author(s)
Lee, JaebeomLee, Kyoung-ChanLee, Young-Joo
Issued Date
2018-05
DOI
10.3390/s18051488
URI
https://scholarworks.unist.ac.kr/handle/201301/24046
Fulltext
http://www.mdpi.com/1424-8220/18/5/1488
Citation
SENSORS, v.18, no.5, pp.1488
Abstract
Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance.
Publisher
MDPI AG
ISSN
1424-8220
Keyword (Author)
railway bridgevertical deflectionprobabilistic predictionGaussian processtraining data
Keyword
STRUCTURAL DISPLACEMENT MEASUREMENTBOX-GIRDER BRIDGEMODELRELIABILITYVARIANCECABLESROBUSTSENSORBIAS

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