Seismic reliability evaluation of bridges under spatially varying ground motions using a four-parameter distribution

Abstract

Large-span bridges are often subjected to spatially varying ground motion (SVGM) excitations. Meanwhile, bridge structures and ground motion excitations generally present a large degree of inherent uncertainties. Carrying out the seismic reliability analysis of bridges under SVGMs can provide a direct assessment for the safety probability of bridges during an earthquake event and is of paramount importance for the probabilistic performance analysis of bridges. To this end, this study proposes a new method for the seismic reliability evaluation of random nonlinear bridges subjected to SVGMs, which tackles the drawback of the current seismic reliability analysis method that can only be applied for uniform seismic excitations. To implement, the space correlated ground motions are first generated via a random function-based spectral representation method, based on which the uncertainty of ground motions is characterized by three elementary random variables. The first-passage probability of bridges under SVSGMs is then derived by the equivalent extreme value distribution (EVD), which is approximated by a flexible four-parameter distribution, i.e., shifted generalized lognormal distribution (SGLD). To estimate the parameters in the SGLD model with high efficiency and accuracy, a novel fractional moment-based method is proposed. A numerical example of a two-span continuous box-girder bridge subjected to space varying site conditions is investigated to verify and exemplify the proposed method. The results demonstrate that: 1) The proposed method is efficient and accurate for the seismic reliability of bridge under SVGM, even at low failure probability levels, and the computational burden of the proposed method is less than 2% of that of MCS; 2) The spatial variability is significantly influential on the seismic reliability of the bridge, ignoring the spatial variability of seismic excitation will significantly underestimate the failure probability of the bridge.