<p dir="ltr">Restoration of civil infrastructure is <b>not</b> equivalent to the full recovery of a community from natural hazards. Considering the recovery of only civil infrastructure in quantifying the disaster recovery of a community does not allow for capturing the long-term socio-economic impacts of natural hazards (e.g., stress, anxiety, unemployment, etc.). The role of having a robust social infrastructure in facilitating disaster recovery and addressing both short-term and long-term impacts of natural hazards needs to be explored. Social infrastructure is defined as formal entities (e.g., governmental organizations, community centers, NGOs, religious centers, etc.) as well as informal social ties such as individuals and households that assist in post-disaster recovery and alleviate the distress caused by natural hazards. Social infrastructure not only addresses post-disaster tangible needs such as shelter, food, and water but also helps alleviate disaster-induced socio-economic distress in communities.</p><p dir="ltr">This research focuses on identifying the capacity needs of the social infrastructure to facilitate disaster recovery (measured using community well-being as the recovery metric), while integrating the cascading impacts from other affected inter-dependent infrastructure systems (i.e., civil, civic, cyber, financial, environmental, and educational). Using community well-being, which is defined as the state in which the needs of a community are fulfilled, allows for incorporating both short-term and long-term impacts of natural hazards.</p><p dir="ltr">The research starts with modeling post-disaster community well-being using the indicators selected from existing community well-being models. After the selection of indicators, several data sources such as phone call, survey, and FEMA support programs data were used to 1) verify the structure of the community well-being model, and 2) quantify post-disaster community well-being. Chapter 3 elaborates on this process and its outcome, which is a framework for quantifying post-disaster community well-being based on disaster helpline and survey data.</p><p dir="ltr">Chapter 4 introduces a Bayesian Network<b> </b>modeling framework for quantifying the role of social infrastructure services in the form tangible, emotional, and informational support in enhancing post-disaster community well-being. The Bayesian model was then used to propose capacity building strategies for increasing the robustness of social infrastructure and its supporting infrastructure to foster post-disaster community well-being in the face of future hurricanes.</p><p dir="ltr"><b>Intellectual Merit</b>: the proposed research is unique in its kind as it leverages social and psychological well-being models and theories to characterize the role of social infrastructure in the recovery of communities from natural disasters. The research contributes to infrastructure and urban resilience models by considering the role of social infrastructure services using community well-being as the recovery metric. It also contributes to social sciences by introducing 2-1-1 disaster helpline data as an inexpensive and timely replacement for multiple rounds of survey questionnaires for quantifying community well-being.</p><p dir="ltr"><b>Broader Impacts</b>: the proposed model and the obtained results can serve as an Ex-Ante Capacity building tool for decision-makers to predict the status of communities in the face of future natural hazards and propose capacity building strategies to have higher post-disaster support, and thereby, community well-being.<br></p><p dir="ltr"><br></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25670910 |
| Date | 27 April 2024 |
| Creators | Mohamadali Morshedi Shahrebabaki (18426579) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_Ex-Ante_Capacity_Building_in_Social_Infrastructure_to_Improve_Post-Disaster_Recovery_and_Community_Well-being_b_/25670910 |
Page generated in 0.0077 seconds