Data-driven decision-making for post-earthquake recovery under constrained resources

Post-earthquake recovery and reconstruction can take years or decades after the earthquake itself. This long recovery time is often caused by the shortage of reconstruction resources, such as skilled labor or financing, which impedes the recovery process. Thus, decision-makers and stakeholders, such as local governments and private entities, must decide how the recovery process should proceed while accounting for the limited resources available. Computer simulations and models have been useful for better understanding the recovery process after earthquakes. However, few models have accounted for limited available resources. Therefore, new recovery models and algorithms that decision-makers can use to develop effective post-earthquake recovery strategies are presented here. First, this dissertation introduces a recovery model utilizing queuing theory to simulate the reconstruction time of residential buildings. This stochastic queuing model explicitly accounts for the total number of damaged buildings, the damage distribution, the limited number of reconstruction resources, and the prioritization strategy implemented by the decision-makers. As a result, the model better captures the delays in the post-earthquake recovery process. Long-term data collected after the 2018 Lombok, Indonesia earthquakes were used to calibrate and validate the model, and the results show how the stochastic queuing approach achieves higher accuracy than commonly used recovery frameworks, such as FEMA's HAZUS. Second, this dissertation presents an agent-based model for post-earthquake financing, another critical resource needed for post-earthquake reconstruction. The model incorporates funding from seven different US sources for single-family, owner-occupied homes and demonstrates how different income groups have differing access to these financing sources. A case study of the city of San Jose, California, USA, following a hypothetical M7.0 earthquake, shows that current post-earthquake financing and reconstruction policies in place can result in extreme disparities in recovery outcomes between different income groups. Based on these results, interventions that includes federal funding reforms and a redistribution of labor is presented and evaluated here. The results show how these interventions are necessary to reduce inequalities in recovery outcomes. Last, this dissertation proposes a greedy algorithm to determine the optimal reconstruction of critical buildings, particularly school buildings and healthcare facilities. The algorithm chooses the reconstruction order of damaged buildings that results in the best improvement of a chosen metric. For school buildings, the greedy algorithm minimizes the distances the students would have to travel to the nearest functional school, while also considering the number of times the students would have to transfer and the student-to-classroom ratio. In the case of healthcare facilities, the greedy algorithm minimizes the total time it takes for patients to receive medical care. Case studies of Lombok, Indonesia, and Lima, Peru for, respectively, school buildings and healthcare facilities, demonstrate that the greedy algorithm can achieve better recovery outcomes than currently used reconstruction strategies, such as the common practice of first reconstructing buildings with minor damage. The models and algorithms developed in this dissertation can be leveraged by researchers and decision-makers alike. In addition to providing a better understanding of the long-term effects of earthquakes, both physical and social, the work presented here contributes to the development of effective post-earthquake strategies and policies to reduce post-earthquake inequalities, reduce long-term effects on the community, and ultimately reduce the loss of lives after an earthquake

Advisor: Kiremidjian Anne S. (Anne Setian)
Baker Jack W. 
Noh Hae Young