Temperature gradient modeling of a steel box-girder suspension bridge using Copulas probabilistic method and field monitoring

A novel copula-based probabilistic model is proposed to establish the temperature difference analysis model for a long-span suspension bridge's steel box girder. The key idea is to express a two-dimensional function of the temperature difference in flat steel box girder by using copulas. The maximum and minimum values of daily temperature difference model was developed using long-terms structural health monitoring data. Then, the correlation between adjacent temperature differences is investigated using five types of copulas. Akaike information criterion (AIC) is used to select an optimal model from five types of copulas, and the optimal joint function (two-dimensional function) for steel box girder's temperature difference is established. Finally, the structure's temperature gradient model is extrapolated for the service life of the structure by using Monte Carlo method. Moreover, this paper discusses the temperature gradient models using five types of common copulas and four types of time-varying copulas. The result shows that the t-copula is the optimal function to build the two-dimensional functions for steel box girder's temperature difference, and the temperature model along the transverse direction can offer useful information that is not available in the design codes.

Automated summary

A novel copula-based probabilistic model was proposed for analyzing temperature difference in a long-span suspension bridge's steel box girder. By investigating correlation between adjacent temperature differences and selecting optimal model using AIC, the study established the optimal joint function for steel box girder's temperature difference. Using Monte Carlo method, the structure's temperature gradient model was extrapolated for the service life of the bridge.