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The presented methodology results in an optimal portfolio of resilience‐oriented resource allocation under weather‐related risks. The pre‐event mitigations improve the capacity of the transportation system to absorb shocks from future natural hazards, contributing to risk reduction. The post‐event recovery planning results in enhancing the system's ability to bounce back rapidly, promoting network resilience. Considering the complex nature of the problem due to uncertainty of hazards, and the impact of the pre‐event decisions on post‐event planning, this study formulates a nonlinear two‐stage stochastic programming (NTSSP) model, with the objective of minimizing the direct construction investment and indirect costs in both pre‐event mitigation and post‐event recovery stages. In the model, the first stage prioritizes a bridge group that will be retrofitted or repaired to improve the system's robustness and redundancy. The second stage elaborates the uncertain occurrence of a type of natural hazard with any potential intensity at any possible network location. The damaged state of the network is dependent on decisions made on first‐stage mitigation efforts. While there has been research addressing the optimization of pre‐event or post‐event efforts, the number of studies addressing two stages in the same framework is limited. Even such studies are limited in their application due to the consideration of small networks with a limited number of assets. The NTSSP model addresses this gap and builds a large‐scale data‐driven simulation environment. To effectively solve the NTSSP model, a hybrid heuristic method of evolution strategy with high‐performance parallel computing is applied, through which the evolutionary process is accelerated, and the computing time is reduced as a result. The NTSSP model is implemented in a test‐bed transportation network in Iowa under flood hazards. The results show that the NTSSP model balances the economy and efficiency on risk mitigation within the budgetary investment while constantly providing a resilient system during the full two‐stage course.

 

This paper presents a quasi-steady technique that combines aerodynamic force coefficients from straight-line wind tunnel tests with empirically developed tornado wind speed profiles to estimate the time history of aerodynamic loads on lattice structures. The methodology is specifically useful for large and geometrically complex structures that could not be modeled with reasonable scales in the limited number of tornado simulation facilities across the world. For this purpose, the experimentally developed tornado wind speed profiles were extracted from a laboratory tornado wind field and aerodynamic force coefficients of different segments of a lattice tower structure were assessed for various wind directions in a wind tunnel. The proposed method was then used to calculate the wind forces in the time domain on the model lattice tower for different orientation angles with respect to the tornado’s mean path based on the empirical tornado wind speed profiles and the measured aerodynamic force coefficient of each tower segment, where the wind field at the tower location was updated at each time step as the tornado went past the tower. A tornado laboratory simulation test was conducted to measure the wind loads on a scaled model of a lattice tower subject to a translating tornado for the purpose of validation of the proposed method. The moving average of the horizontal wind force on the lattice tower model that was calculated with this method compared very well with that measured in a laboratory tornado simulator, which paves the way for the use of straight-line wind tunnels to assess tornado-induced loads on a lattice structure and possibly other similar structures.