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Probabilistic Prediction of Vertical Deflection for High-speed Railway Bridges Using a Gaussian Process

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Authors
Lee, Jaebeom; Lee, Kyoung-Chan; Lee, Young-Joo
Issue Date
2019-05-26
Citation
13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019
Abstract
Vertical deflection of a high-speed railway bridge is one of the important indicators for managing the safety and running stability of a vehicle. Therefore, efforts have been made to develop sensors for measuring the deflection and predicting its short- and long-term future values. However, the vertical deflection of a railway bridge is stochastic because it involves various sources of uncertainty, which may cause errors in physics-based prediction models. This study proposes a Bayesian approach to build a probabilistic prediction model for the vertical deflection of a railway bridge. For this task, a Gaussian process is introduced to construct a covariance matrix with multiple kernels. Thereafter, actual vision-based measurements, measuring time, and temperature data are used to optimize the hyperparameters of the kernels. As a result, the proposed approach provides a probabilistic prediction interval as well as a predictive mean of the vertical deflections of the bridge. This approach is applied to an actual high-speed railway bridge in the Republic of Korea, and the corresponding analysis results and their performance are discussed.
Language
English
URI
https://hdl.handle.net/10371/153561
DOI
https://doi.org/10.22725/ICASP13.494
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)ICASP13
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