Seismic Reliability Analysis of Reinforced Concrete Arch Bridges Considering Component Correlation

Abstract

To more effectively account for the correlation between components in the seismic reliability analysis of reinforced concrete arch bridges, this study proposes a system seismic reliability analysis method based on the D-vine Copula function. First, based on the theories of seismic vulnerability and hazard, the seismic vulnerability curves of key components (arch ring, piers, main girder, columns) and the site hazard curves are obtained. Second, a trial algorithm is used to determine alternative combinations of Pair-Copula functions. The maximum likelihood estimation method is employed to solve for the parameter θ, and the optimal Pair-Copula function is selected based on AIC and BIC information criteria. The optimal Pair-Copula function for each layer in the D-vine structure is determined through hierarchical iteration, ultimately constructing a seismic reliability evaluation framework for arch bridge systems that incorporates component correlations. The results show that the damage probability of the arch ring is consistently the highest, followed by the piers and main girder, with the columns having the lowest probability. Compared to ignoring component correlation, the seismic reliability indices of the system under minor, moderate, severe damage, and complete failure states all decrease when correlation is considered, indicating that component correlation significantly affects system reliability. Ignoring correlation leads to an overestimation of the system’s seismic performance. The seismic reliability indices obtained by the D-vine Copula method and Monte Carlo simulation are in good agreement, with a maximum relative error not exceeding 2.26%, verifying the applicability and accuracy of the D-vine Copula method in the reliability analysis of complex structural systems. By constructing an accurate joint probability distribution model, this study effectively accounts for the nonlinear correlation characteristics between components. Compared to the traditional Monte Carlo simulation, which relies on large-scale repeated sampling, the D-vine Copula method significantly reduces computational complexity through analytical derivation, improving computational efficiency by over 80%.