Abstract

The temperature field of a long-span bridge is variant with an underlying statistical property due to the periodically time-varying solar radiation and the unceasing structural heat exchange with surrounding environment. A comprehensive understanding of the long-term temperature field of a steel-box girder is essential for the design and maintenance of cable-supported bridges. However, it is challenging to obtain the long-term temperature variations because of the complex geometric configuration of the main girder and the complicated heat transfer process. In this context, this paper presents a strategy to overcome this problem by simulating the long-term temperature field of a steel-box girder based on limited monitored data. In the simulation, the long-term temperature field is treated as a stochastic process with prescribed spectral and probabilistic properties. By analyzing the monitored temperature data on Sutong Bridge, the models of power spectral density (PSD), coherence function, and high-order moments are developed. A noteworthy feature captured in measurements is that the temperature is non-Gaussian with considerable kurtosis and skewness. Hence, the temperature field simulation is converted to the simulation of multivariate non-Gaussian stochastic processes. Based on the spectral representation method accompanied with Hermite transformation, the temperature field of the steel-box girder of Sutong Bridge is simulated with a two-year duration considering the non-Gaussianity. Verifications of the simulated field are made through comparisons with the developed spectral and probabilistic models. It is shown that the simulations match well with the measured temperature characteristics, specifically including the time histories and their PSD, PDF, and coherence functions. The good agreement indicates the effectiveness of the simulated long-term temperature field.

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