To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges.
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