Time-history deflection prediction using Gaussian process regression for railway bridges

Abstract

This study proposes a probabilistic method to predict the time-history deflections of railway bridges subject. The Gaussian process regression, a non-parametric regression method, is introduced to construct the probabilistic prediction model where the inherent uncertainty of time-history deflection data is considered. The applicability of the proposed method is discussed with the actual measurement data of a railway bridge in South Korea.