Moment-independent importance (MII) analysis is known as a global sensitivity measurement in qualifying the influence of uncertainties, which is taken as a crucial step towards seismic performance analysis. Most MII analysis is based on Monte Carlo simulation, which leads to a high computational cost since a large number of nonlinear time history analyses are required to obtain the probability density function. To address this limitation, this study presents a computational efficient MII analysis to investigate the uncertain parameters in the seismic demands of bridges. A modified four-point-estimate method is derived from Rosenblueth’s two-point-estimate method. Thus, the statistical moments of a bridge’s seismic demands can be obtained by several sampling points and their weights. Then, the shifted generalized lognormal distribution method is adopted to estimate the unconditional and conditional probability density functions of seismic demands, which are used for the MII analysis. The analysis of seismic demands based on piers and bearings in a finite element model of a continuous girder bridge is taken as a validation example. The MII measures of the uncertain parameters are estimated by just several nonlinear time history analyses at the point-estimate sampling points, and the results by the proposed method are compared with those found by Monte Carlo simulation.

Accurate quantification of the apparent chloride diffusion coefficient is pivotal for predicting the service life and assessing the durability of coastal infrastructure. While existing empirical models are informative, they fundamentally overlook the influence of reinforcement. This study establishes an integrated computational framework combining XGBoost machine learning with finite element (FE) analysis to elucidate chloride transport mechanisms in reinforced concrete (RC), explicitly accounting for the presence of reinforcement. Based on 171 experimental datasets, this study developed a prediction model to estimate the apparent chloride diffusion coefficient in reinforced concrete subjected to dry–wet cycles. The diameter of rebar was innovatively incorporated as a parameter, systematically integrating seven other key influencing factors into the model. Shapley Additive Explanations (SHAP) analysis reveals that exposure duration, sampling depth, coarse-to-total aggregate ratio, and rebar diameter constitute the dominant influencing parameters. Furthermore, FE analysis reveals that the presence of rebar redistributes aggregates, forming preferential pathways that increase chloride concentration at the steel-concrete interface. This study shows that the influence of reinforcement on chloride diffusion cannot be ignored. The proposed methodology advances durability science by data-driven modeling with physics-based modeling, providing actionable strategies for marine infrastructure optimization.