Simulation-based tsunami evacuation risk assessment and risk-informed mitigation - Wang, Zhenqiang, author; Jia, Gaofeng, advisor; Ellingwood, Bruce R., committee member; Mahmoud, Hussam N., committee member; Quinn, Jason C., committee member (Colorado State University. Libraries, 2021)
Earthquake-induced tsunami can be very destructive involving significant loss of life. Evacuation to safety zones is regarded as one of the most effective ways to save lives from the tsunami strike due to the limited effectiveness of structural countermeasures. However, it is extremely challenging to successfully evacuate many people under the multi-hazard environment within a condensed time frame, especially under the near-field tsunami. Proper evacuation planning is crucial to support effective evacuation and reduce casualty. For effective evacuation planning, it is important to better understand the complex evacuation behavior for recommending proper response and behavior in an emergency. Also, it is important to have a clear picture of evacuation risk for informing policy and decision-making. Furthermore, it is important to identify effective pre-event mitigation strategies for effective risk reduction. Important limitations exist in current research on the above aspects. Tsunami evacuation simulation using the agent-based model has been used to investigate the complex evacuation behavior; however, existing agent-based evacuation models usually neglect or simplify many important factors and/or mechanisms associated with the evacuation. The neglect or simplification would make the evacuation simulation less realistic and hence a good understanding of evacuation behavior challenging. For the quantification of tsunami evacuation risk, a systematic framework that can address complex evacuation models and uncertainty (including aleatory and epistemic uncertainties) models is needed; however, no such framework has been developed for the quantification of tsunami evacuation risk. Also, some important uncertainties such as that in the seismic damage to the bridge are usually neglected or the uncertainty quantification is simplified. In this case, it would be difficult to assess the evacuation risk accurately and provide a clear picture of the evacuation risk. For effective pre-event evacuation risk mitigation, the effectiveness of different mitigation strategies needs to be quantitatively evaluated to identify more effective strategies. However, the effectiveness of the mitigation strategy is usually evaluated more qualitatively than quantitatively. Furthermore, the evaluation is typically conducted without systematically considering various uncertainties, which makes the identified strategies not robust to uncertainties. In tsunami evacuation risk assessment and mitigation, risk evaluation using general stochastic simulation techniques (e.g., Monte Carlo simulation) typically entails significant computational challenges. Efficient algorithms are needed to alleviate such computational challenges and facilitate such tasks. To bridge the above knowledge gaps, this research proposes a generalized framework for simulation-based tsunami evacuation risk assessment and risk-informed mitigation. The framework is built layer by layer through integrating tsunami evacuation simulation using agent-based modeling (ABM) technique, simulation-based evacuation risk assessment, sensitivity analysis of evacuation risk, and risk-informed evaluation of mitigation strategies. An improved agent-based tsunami evacuation model is developed for more realistic tsunami evacuation simulation by incorporating many of the typically neglected or simplified but important factors and/or mechanisms in the evacuation. Using the proposed agent-based evacuation model, a simulation-based framework is proposed to quantify the evacuation risk, in which various uncertainties (including aleatory and epistemic uncertainties) associated with the evacuation are explicitly considered and modeled by proper selection of probability distribution models. Sensitivity analysis of evacuation risk with respect to the epistemic uncertainty is performed, and the sensitivity information can be used to guide effective epistemic uncertainty reduction and hence for more accurate risk assessment. Also, sensitivity analysis is performed to identify critical risk factors, and the sensitivity information can be used to guide effective evacuation modeling and selection of candidate risk mitigation strategies. Risk-informed evaluation of different types of candidate mitigation strategies (including infrastructural and non-infrastructural strategies) is conducted to identify more effective strategies that are robust to uncertainties. Efficient sample-based approaches are developed to alleviate the computational challenges in evacuation risk assessment, sensitivity analysis, and risk-informed evaluation of mitigation strategies. As an illustrative example, the proposed framework is applied to tsunami evacuation risk assessment and risk-informed mitigation for the coastal community of Seaside, Oregon.
Keywords: risk assessment; sensitivity analysis; tsunami evacuation; risk mitigation; agent-based model; simulation