Efficient sample-based approach for effective seismic risk mitigation of transportation networks

ABSTRACT

Identifying optimal seismic mitigation strategies (i.e., groups of bridges to retrofit) for effective risk mitigation of transportation networks entails significant computational challenges due to the large number of bridge combinations and the seismic risk evaluation of network for each mitigation strategy, especially for large-scale network. An efficient sample-based approach is proposed to address these challenges. It uses only one set of simulations of the network model to generate samples to estimate a probabilistic sensitivity measure that provides importance ranking of bridges associated with the seismic risk. Three selection approaches using the importance ranking are proposed to guide an effective search of mitigation strategies. The same set of simulations are also used to efficiently evaluate the updated seismic risk for any given mitigation strategy. The efficiency and effectiveness of the proposed approach are validated through its application to seismic risk mitigation of the transportation network of Los Angeles and Orange counties.

KEYWORDS:

• Sample-based approach
• seismic risk mitigation
• seismic retrofit priorities
• transportation network
• probabilistic sensitivity analysis
• importance ranking of bridges

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Zhenqiang Wang

Zhenqiang Wang is a graduate research assistant at Risk Assessment and Mitigation Lab in Department of Civil and Environmental Engineering at Colorado State University. His research topic is simulation-based assessment of infrastructure network resilience under multiple hazards. 

Gaofeng Jia

Gaofeng Jia is an Assistant Professor of Civil and Environmental Engineering at Colorado State University. His primary research interests are in the areas of natural hazard risk assessment and mitigation, robust analysis/design of complex engineering systems and high performance structures, risk-informed decision making, aging and deterioration of civil infrastructure with the ultimate goal of enhancing infrastructure resilience.