Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges

Sensors

卷:18

Kyoung-Chan Lee¹  Young-Joo Lee²  Jaebeom Lee² 

[1] Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute, Uiwang 16105, Korea;

[2] School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea;

关键词: railway bridge;    vertical deflection;    probabilistic prediction;    Gaussian process;    training data;   

DOI  :  10.3390/s18051488

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown