Quick post-disaster emergency response of highway bridge networks (HBNs) is vital to alleviating the impact of disasters in affected areas. Nevertheless, achieving their emergency response resilience remains challenging due to the difficulty in accurately capturing the response capacity of HBNs and rapidly evaluating the damage states of regional bridges. This study delves into the emergency response, seismic resilience, and recovery scheduling of HBNs subjected to frequent yet mostly ignored moderate earthquakes. Firstly, the feasibility of intelligent methods is explored as a substitute for nonlinear time-history analysis of regional bridges. Subsequently, for realistic modeling of post-disaster HBNs, a decision tree model is developed to determine potential traffic restrictions imposed on damaged bridges. Moreover, their emergency response functionalities are thoroughly investigated, upon which a comprehensive multi-dimensional resilience metric vector is proposed. Finally, the proposed methodologies are applied to the Sioux Falls HBN as a case study, revealing a decreasing mean value and increasing deviation values in the long term. The results are expected to provide important theoretical and practical emergency response guidance.

To cope with harsh working conditions, new corrosion-resistant coated steel wires with higher tensile strength have been developed. This study investigates the corrosion characteristics of a new zinc–aluminum alloy-coated steel wire under stress conditions. The particulate corrosion products generated by the oxidation of the coating in the initial stage of corrosion are converted into layer-structured corrosion products at the early stage of corrosion. Moreover, high-stress conditions have a significant influence on the critical conversion time from the coating corrosion stage to the iron matrix corrosion stage. Thus, the uniform corrosion depth (i.e., the mass loss rate) could be fitted with a continuous power function model rather than the previously used two-stage model owing to an ambiguous moment of conversion under stress conditions. The pitting corrosion depth could be fitted with a lognormal distribution in this study. The probability distributions for the aspect ratios of corrosion pits under different stress conditions tended to be consistent. Finally, the block’s maximum pitting factor followed a Gumbel distribution with a scale parameter that changed linearly with the stress level and a location parameter related to the square of the stress level.

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis.