Transportation system modeling and applications in earthquake engineering

Transportation networks constitute one class of major civil infrastructure systems that is a critical backbone of modern society. Physical damage and functional loss to transportation infrastructure systems not only hinder everyday societal and commercial activities, but also impair post-disaster response and recovery, leading to substantial socio-economic consequences. Therefore, understanding and modeling the disastrous impact on the transportation infrastructures and the corresponding changes of travel patterns under extreme events are vital for stakeholders, emergency managers, and government agencies to mitigate, prepare for, respond to, and recover from the potential impact. This research is aimed at developing a systematic approach for risk modeling and disaster management of transportation systems in the context of earthquake engineering. First, by employing the performance metrics that are suited for immediate post-disaster response, this dissertation explores efficient methodologies to maximize the overall system functionality and the benefit of mitigation investment for transportation infrastructure systems. Furthermore, the regions potentially unreachable after a damaging earthquake are identified promptly by using network reachability algorithms that provide essential information for rapid emergency response decision-making. Lastly, an integrated simulation model of travel demand that accounts for damage of bridge and building structures, release of hazardous materials, and influences of emergency shelters and hospitals, is developed to approximate the “abnormal” post-earthquake travel patterns and evaluate the functional loss of the transportation systems. This study extends the understanding of disaster management of transportation infrastructure systems. The methodologies developed in this study have the following significance: (i) help leverage available mitigation resources to improve the disaster resilience and functionality of transportation infrastructure systems; (ii) enable emergency response and recovery teams to rapidly identify and evaluate the performance of optimal routes for emergency ingress and egress; (iii) accurately estimate traffic congestion under extreme events; and (iv) provide important insights necessary to make decisions on protecting these systems to meet the needs of current and future generations