Download or read book Seismic Risk Mitigation Strategies for Complex Regional Transport Networks written by Gitanjali Bhattacharjee and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Like the systems that supply residents of an area with power, water, sanitation, and communication services, road networks, which provide transport, are lifelines (Chang, 2016). Earthquakes can result in extensive damage to road networks and, in California, have historically caused significant damage to bridges (Mitchell et al., 1995). The immediate goal of seismically retrofitting a bridge is to mitigate the risk of the bridge experiencing structural damage during an earthquake (e.g., Buckle et al., 2006). Seismically retrofitting a bridge reduces the probability that it will be damaged by ground shaking in an earthquake -- and, consequently, the probability that it will incur repair costs or contribute to the indirect costs associated with road network disruptions. Retrofitting bridges has been shown to be a cost-effective method of mitigating the risk of bridge damage (e.g., Giovinazzi et al., 2011). Given budget constraints, retrofitting every bridge in a regional road network subject to seismic hazard is infeasible. How to decide which bridges within such a network to retrofit has therefore proven to be a problem of enduring interest. Complicating factors include the scale of the real-world problem, which precludes exhaustive searches, uncertainty in the seismic hazard and associated bridge damage, the link between bridges' states and the performance of the road network, and the computational cost of simulating road network performance. This dissertation proposes probabilistic and computationally tractable methods for performance-based seismic risk mitigation of complex regional road networks. First, this dissertation proposes a method for prioritizing bridge retrofits within a regional road network subject to uncertain seismic hazard, using a technique that accounts for network performance while avoiding the combinatoric costs of exhaustive searches. Using global variance-based sensitivity analysis (SA), bridges are ranked according to how much their retrofit statuses influence the expected cost of road network disruption, as measured by their total-order sensitivity (Sobol') indices. In a case study of 71 bridges in San Francisco, the proposed method identifies more effective retrofits than other heuristic retrofit prioritization strategies. The proposed method also remains computationally tractable while accounting for uncertainty in the seismic hazard, the stochastic nature of bridge damage, the uniqueness of individual bridges, network effects, and decision-makers' priorities, including budget considerations (but not constraints). As this method leverages existing risk assessment tools and models without imposing further assumptions, it should be extensible to other types of networks, hazards, and decision variables. Second, this dissertation proposes a method with which to increase the computational tractability of the SA-based bridge retrofit prioritization method when the decision variable of interest requires traffic simulation. To more efficiently compute bridges' Sobol' indices, a neural network is trained to serve as a surrogate model for a traffic simulator. For the same set of 71 bridges in San Francisco previously studied, a retrofit strategy based on bridges' total-order Sobol' indices computed using the surrogate model agrees closely with a strategy based on indices computed using only the traffic simulator while reducing the computational time required by as much as 99%. A surrogate model-based approach is also effective at prioritizing bridge retrofits for a set of 141 highway bridges in two Bay Area counties. Leveraging the power of surrogate models to reduce the computational burden of estimating bridges' total-order Sobol' indices will allow application of the SA-based retrofit prioritization method to larger numbers of bridges and larger sets of earthquake scenarios. It will also enable the use of more sophisticated traffic models to characterize network performance. Third, this dissertation integrates two measures of how post-earthquake road network disruption impacts individuals with a probabilistic seismic risk assessment framework in a computationally tractable way. Impacts on individual commuters are characterized using welfare loss, which is a measure of individual well-being and was previously formulated by Mackie et al. (2001), and the number of jobs affected by road network disruption, a novel measure. A case study of the San Francisco Bay Area shows that while all commuters have a similar risk of increased travel time due to post-earthquake road network disruption, commuters with low incomes have substantially higher risk of welfare loss than commuters with high incomes. Traditional metrics of road network disruption like travel time delay, infeasible trips, or combinations thereof obscure these disparate impacts. Quantitative risk metrics that account for variations in individuals' experiences without becoming computationally impracticable should prove useful in reducing risk to regional infrastructure networks in more equitable ways. A novel method for modifying post-earthquake commute demand to account for business interruptions is also presented. This method allows us to better distinguish between the impacts of road network disruption and the impacts of building damage on workers in a region, which is necessary to design effective risk reduction policies. Lastly, this dissertation includes a study of earthquake responders' building damage information needs and use. Although many responders need to understand the scale and distribution of building damage to react effectively, their building damage information needs and information use remain poorly understood, limiting the efficacy of information production, sharing, and research. Based on interview data and questionnaire responses gathered from experienced responders, six post-disaster tasks that rely on building damage information are characterized by their timing and by the necessary qualities of the information they require. Through inductive analysis of the interview data, responders' use of building damage information is also found to depend on factors beyond the building damage information itself -- namely, trust, impediments to information sharing, their varying understandings of disaster, and their attitudes toward emerging technologies. These factors must be considered in the design of any effort to create and/or disseminate post-disaster building damage information.