Modelling of extreme uniform temperature for high-speed railway bridge piers using maximum entropy and field monitoring

Abstract

Due to the atmospheric temperature and solar radiation, the effects of temperature variation in bridge structures should be considered. Such variation induces notable deformations and movements, jeopardizing the safety of bridges and high-speed trains operations. However, the temperature action is a random process and its distribution is difficult to determine. The existing methods for analyzing structural temperatures are insufficient to meet the precision requirement. Therefore, the accurate prediction of extreme structural temperatures relates to the accurate evaluation on the bridge safety. This paper proposes a robust and accurate model for predicting the extreme structural temperature of a bridge. A field experiment, spanning over 2 years, was carried out on a high-speed railway bridge; and the long-term (56-years) atmospheric temperature data was adopted. Probabilistic models for the structural temperature were established using the Maximum Entropy (MaxEnt) model and the Generalized Pareto distribution (GPD) model; and the predictions on the uniform structural temperature ( T u) with 50 and 100 years return periods are presented. Additionally, the performance between the MaxEnt model and the GPD model is compared, based on the estimates with different return levels. The results show that the MaxEnt model is more stable and is significantly robust to the variation of sample sizes; and indicates that the MaxEnt model reduces the uncertainty of outcomes and avoids the high risk of bias. The MaxEnt model has a great potential to the applications of the extreme value analysis with small sample size. It offers a wider applicability and helps solve practical problems.