Probabilistic seismic restoration cost estimation for transportation infrastructure portfolios with an emphasis on curved steel I-girder bridges. (March 2017) - Seo, Junwon; Park, Heedae
Highlights: A probabilistic seismic restoration cost estimate framework for transportation infrastructure portfolios is developed. Metamodel-based fragility curves are generated via Monte Carlo Simulation (MCS). The fragility curves are analyzed with MCS to treat uncertainty in restoration costs and bridge features. The restoration costs become more sensitive to increase in peak ground motions. Abstract: This paper develops a rapid probabilistic seismic restoration cost estimate framework for transportation infrastructures (i.e., bridge portfolios) by integrating Response Surface Metamodels (RSMs) coupled with Monte Carlo Simulation (MCS) into seismic restoration cost curve generation. A portfolio of curved steel I-girder bridges located in the Eastern United States is used for this study. As part of the restoration curve generation, joint RSM-MCS models are created and utilized to assess seismic vulnerabilities of the target bridge portfolio and estimate their damage ratios. Probabilistic damage factor models in conjunction with MCS to treat uncertainties in damage ratios, curved bridge characteristics, and volatile construction conditions are simulated with the joint RSM-MCS-based vulnerability functions. Relevant restoration cost curves are then generated based upon average construction cost data of the United States. Findings reveal that characteristics for the restoration curves vary relying on variability in damage ratios and vulnerabilities, emphasizing that an increase in the difference between lower and upper cost bounds occurs as the seismic intensity increases. This evidence highlights the significance of considering variability in both damage ratios and vulnerabilities for more reliable decision-making for seismic restoration on such bridge portfolios.