Seismic risk management of complex road networks : optimization methods and integration of community impact metrics

Road networks are critical infrastructure systems that allow individuals of a community to fulfill their essential needs by providing access to services and goods. While these systems are important to communities, they are also vulnerable to being disrupted by natural hazards as they are comprised by elements that may experience damage or collapse during these events. Of these natural hazards, earthquakes are among the ones that have most profoundly damaged road networks. The Northridge earthquake of 1994 generated the collapse of bridges along Interstate 5, and the 1989 Loma Prieta caused the partial collapse of the Bay Bridge and the total collapse of the Cypress Viaduct, damages that severely disrupted commuters in the respective regions. Given the importance of road networks, decision-makers have explored strategies to mitigate the effects of earthquakes on them. Retrofitting bridges has proven to be effective at decreasing the probability of these bridges not being available after an earthquake. However, proposing effective seismic retrofitting strategies imposes several challenges as the computational costs involved in this process can be significant, and road networks are complex and distributed along large regions. Motivated by these challenges, this dissertation presents effective and computationally efficient retrofitting policies that can assist decision-makers in mitigating the impacts of road network disruption. First, this work introduces the concept of Corridors to support optimal bridge retrofitting strategies for seismic risk management of road networks. A Corridor is defined as a set of bridges that works jointly to ensure connectivity and traffic flow between different areas of a region. After Corridors have been detected, a two-stage stochastic optimization is implemented to detect which bridges should be retrofitted to ensure an acceptable performance under uncertain conditions. Second, this dissertation proposes using deep neural networks to rapidly and accurately estimate the seismic risk performance of road networks. In addition, this work introduces a modified version of the Local Interpretable Model-Agnostic Explanation (LIME) to identify retrofits that minimize earthquakes' impact on the system. The proposed neural network accurately predicts the system's performance, taking five orders of magnitude less time to compute traffic metrics than using the original traffic model. Moreover, the proposed LIME-TI metric is superior to others in identifying bridges whose retrofit effectively improves network performance. Third, this work introduces genetic algorithms as an optimization method that directly minimizes the expected impacts of road network disruption triggered by seismic events. This minimization is achieved by selecting an optimal set of bridges that managing agencies need to retrofit to decrease their probability of being unavailable after an earthquake. The genetic algorithm outstrips other bridge retrofitting selection techniques by identifying structurally vulnerable bridges and acting as bridge corridors in the network. Fourth, this dissertation presents a comparative study of three retrofitting strategies for seismic risk management of road networks: (1) Corridors-supported optimization, (2) LIME-TI retrofitting ranking, and (3) Optimization using genetic algorithms. In the comparison, three aspects of the retrofitting strategies are analyzed, (i) the ability of the strategy to minimize expected annual traffic disruption, (ii) computational costs involved in implementing the strategies, and (iii) the interpretability of bridges selected to retrofit from the perspective of policymaking. Fifth, this study integrates Welfare, a measure of the impact of road network disruption on individual commuters' well-being, with a probabilistic seismic risk assessment framework. Welfare is a network performance measure that reflects the differential impacts of changes in commute time on various income groups. While commuters from all income groups have a similar risk of delay due to road network disruption, commuters with low incomes have a substantially higher risk of Welfare loss than those with high incomes. Lastly, this work introduces a multiobjective optimization where an aggregated travel time metric and a socioeconomic equity metric are minimized in parallel. Using surrogate models for these two metrics, the proposed methodology uses a Non-dominated Sorted Genetic Algorithm (NSGA-II) to explore the implications of retrofitting bridges, focusing on socioeconomic or an aggregated metric. Using the San Francisco Bay Area as a testbed, the proposed methodology obtains retrofitting strategies that minimize societal aggregated impacts of earthquakes while ensuring that the impacts of network disruption are smaller for low-income groups. The main contribution of this dissertation work is the proposal, comparison, and explanation of optimization methods to assist decision-makers in mitigating the effects of earthquakes on road networks. In addition, this work introduces metrics focused on quantifying the socioeconomic consequences of road network disruption, enabling equitable seismic risk management of these systems

Advisor: Baker Jack W. 
Deierlein Gregory G. (Gregory Gerard)
Kiremidjian Anne S. (Anne Setian)