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Probabilistic Modeling of Transportation Infrastructure and its Inter- Dependencies in Community Resilience Analysis

Taghizadeh, Mehdi | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53683 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mahsuli, Mojtaba; Poorzahedi, Hossein
  7. Abstract:
  8. This thesis extends a probabilistic framework for evaluating the seismic resilience of communities by modeling the transportation system. The framework that is being extended is under continuous development at the Center for Infrastructure Sustainability and Resilience Research (INSURER) at Sharif University of Technology quantifies community resilience by integrating hazard models, risk models, and recovery models within an agent-based simulation. In this framework, the transportation system is modeled by developing a library of probabilistic models to predict the occurrence and intensity of earthquake hazard, damage and capacity of network components, network travel demand, and economic, social, socioeconomic, and environmental consequences. The hazard models characterize the occurrence, magnitude, and rupture location of the seismic sources, and consequently, the ground shaking and ground failure at the location of network components as well as the components of other infrastructure systems. The end results of hazard models are the ground shaking intensity and the ground failure effects comprising surface fault rupture, landslide, and liquefaction in the form of lateral spreading and vertical settlement, at the location of all infrastructure components. Then, the risk models asses the initial post-hazard state of the community, including the transportation system. To this aim, a group of risk models predicts the damage of vulnerable components of the transportation network, including bridges and tunnels, due to ground shaking and ground failure, and the damage state of the building stock in each urban block. Given the damage state of the building stock, the debris width in adjacent links is predicted by a probabilistic Bayesian model calibrated to real-world observations from the reconnaissance of the 2017 Sarpol-e Zahab, Iran earthquake. Moreover, another group of probabilistic models computes the residual traffic capacity of network links given the damage state of bridges and tunnels and the debris width of buildings in adjacent links. The travel demand of the network is determined by simplified models at different time periods in the aftermath of the seismic event. Thereafter, given the modified capacity and demand of the network, the traffic assignment model predicts the travel time and other major parameters of the network both during the disaster response phase and during the recovery phase, using a different method for each phase. Finally, the recovery models simulate the operations required to restore the community and its infrastructures, including the transportation system, to the pre-disaster state and compute the cost and duration of the operations. This simulation includes both disaster response and recovery phases seamlessly. In the disaster response phase, the transportation system has a critical role in the search-and-rescue operation. Given the travel time of each link, the operation of transporting the injured to the hospital is simulated using an agent-based model. Through this model, the social losses due to the delay in the transportation of the injured and the ensuing health degradation and increase in fatalities are computed. These social losses are translated into social costs through the notion of the value of a statistical life. In the recovery period, the effects of transportation interruption on direct and indirect economic costs, socioeconomic costs, and environmental costs are quantified. The direct economic costs include the repair or replacement cost of damaged bridges and tunnels and the cost of debris removal. Such operations are simulated as discrete events within an agent-based simulation. The socioeconomic costs are determined by computing the cost of delay caused by increased travel times due to the closure of bridges, tunnels, and roads until their restoration and the cost of opportunity loss resulting from the cancelation of trips. As travel times increase, the environmental costs due to increased emission of air pollutants and greenhouse gases and indirect economic costs resulting from increased fuel consumption are quantified by probabilistic models. Eventually, the total cost incurred by the community is obtained by accumulating the losses related to the transportation system and other infrastructures over the timespan between the occurrence of the hazard event and the complete recovery of the community. The total community cost is then used to quantify a community resilience measure. The framework is showcased by a comprehensive application to a virtual community entitled “INSURER City,” comprising a population of 125,000, a portfolio of residential, commercial, and industrial buildings, the transportation system, and the healthcare system subject to an earthquake. The primary results of this analysis are the total community costs and the community resilience measure for various scenarios of the occurrence of an earthquake. Disaggregation of cost into various categories reveals that social costs have the largest share, followed by socioeconomic costs. The analysis of results indicates that the ground failure significantly affects the community resilience measure; therefore, neglecting the ground failure, as has been done in most of the existing literature, leads to an overestimation of community resilience. The effect of modeling the debris due to the damage to buildings is of secondary importance. The proposed framework is implemented in Rtx, a computer program for reliability, risk, and resilience analysis, and can be used as a decision support tool for community resilience enhancement policies. For instance, in the case study presented herein, the proposed framework is utilized to assess bridge retrofit actions, construction of detours for bridges in order to increase redundancy, and “mutual aid” agreements with neighboring communities
  9. Keywords:
  10. Resilience ; Seismic Damage ; Probabilistic Modeling ; Community Resilience ; Interdependent Infrastructure Systems ; Transportation Network Vulnerability ; Travel Demand Estimation ; Ground Failure ; Socioeconomic Loss

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