Resilience Evaluation Framework for Integrated Civil Infrastructure–Community Systems under Seismic Hazard

Abstract

Seismic resilience of civil infrastructure systems is an essential property of modern communities. In this paper, an agent-based modeling framework to evaluate the seismic resilience of an integrated system consisting of the community and its civil infrastructure systems is proposed. Specifically, an agent-based model of the recovery process of civil infrastructure systems is incorporated into a previously developed compositional supply/demand seismic-resilience quantification framework. The proposed model represents the behavior of the operators of civil infrastructure systems as they strive to recover their functionality in the aftermath of an earthquake as well as their mutual interactions and interactions with the community to which they provide services. A case study of the seismic resilience of a virtual system consisting of electric power supply system, transportation system, and the community (EPSS–TS–community system) is conducted using the proposed framework. A parametric investigation is carried out to examine the effect of different earthquake magnitude scenarios as well as different behaviors of the involved agents and their interaction on the seismic resilience of the EPSS–TS–community system. It was demonstrated that the proposed agent-based modeling approach is effective in representing the interactions among different participants in the recovery process. It was also revealed that timely and well-planned intervention in the recovery process can be very effective in alleviating the postearthquake lack of resilience resulting from the insufficient supply of civil infrastructure services to meet the community’s demands. Therefore, the proposed framework could be employed to formulate the recovery trajectory of the intertwined sociotechnical system subjected to different earthquake scenarios. The interplay among different agents, as well as the interdependence among civil infrastructure systems, is found to profoundly shape the recovery path for this integrated EPSS–TS–community system.