Natural disasters result in loss of lives, damage to built facilities, and interruption of businesses. The losses are not instantaneous rather they continue to occur until the community is restored to a functional socio-economic entity. Hence, it is essential that policy makers recognize this dynamic aspect of the incurring losses and make realistic plans to enhance the recovery. However, this cannot take place without understanding how homeowners react to recovery signals. These signals can come in different ways: from policy makers showing their strong commitment to restore the community by providing financial support and/or restoration of lifeline infrastructure; or from the neighbors showing their willingness to reconstruct. The goal of this research is to develop a model that can account for homeowners’ dynamic interactions in both organizational and spatial domains. Spatial domain of interactions focuses on how homeowners process signals from the environment such as neighbors reconstructing and local agencies restoring infrastructure, while organizational domain of interactions focuses on how agents process signals from other stakeholders that do not directly affect the environment like insurers. The hypothesis of this study is that these interactions significantly influence decisions to reconstruct and stay, or sell and leave. A multi-agent framework is used to capture emergent behavior such as spatial patterns and formation of clusters. The developed framework is illustrated and validated using experimental data sets.

Residents in urban areas depend on infrastructure systems to return to functionality quickly after disruptions from natural and man-made disasters to support their livelihood and well-being. This work seeks to improve the accuracy of infrastructure recovery time estimates by introducing mutually exclusive damage state modeling into the Graph Model for Operational Resilience (GMOR) and utilizing this capability for road recovery assessment in two case studies in the District of North Vancouver, British Columbia. The first case study also explores the recovery of water, wastewater, and power networks in the District, and demonstrates that power and road systems recover more slowly and are more variable in their recovery time than water distribution and wastewater collection systems. The second study specifically addresses important sections of road within the District and shows that intelligent prioritization of resource allocation for road repairs can improve recovery times by up to 37% compared to random ordering. / Graduate