Data-driven methods for predicting the representative temperature of bridge cable based on limited measured data

Abstract

Cable-stayed bridges have been widely used in high-speed railway infrastructure. The accurate determination of cable’s representative temperatures is vital during the intricate processes of design, construction, and maintenance of cable-stayed bridges. However, the representative temperatures of stayed cables are not specified in the existing design codes. To address this issue, this study investigates the distribution of the cable temperature and determinates its representative temperature. First, an experimental investigation, spanning over a period of one year, was carried out near the bridge site to obtain the temperature data. According to the statistical analysis of the measured data, it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient. Then, based on the limited data, the Monte Carlo, the gradient boosted regression trees (GBRT), and univariate linear regression (ULR) methods are employed to predict the cable’s representative temperature throughout the service life. These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables. However, each method has its own advantages and limitations in terms of applicability and accuracy. A comprehensive evaluation of the performance of these methods is conducted, and practical recommendations are provided for their application. The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.

Keywords

cable-stayed bridges / representative temperature / gradient boosted regression trees (GBRT) method / field test / limited measured data

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