Abstract

Due to continuous population expansion and the threat of climate change, the past century has witnessed increasing occurrences of natural hazards, leading to significant global losses and requiring substantial restoration efforts. This issue challenges decision makers to act in a timely and effective manner to protect infrastructure systems from future natural hazards. This study presents a policy-based decision model for restoration planning, as part of the PRAISys platform, to support informed disaster mitigation of interdependent infrastructure systems under uncertainty. Following the concept of disaster recovery priority used in practice, this model determines the priority rank of each recovery task from pre-defined policies and simulates the restoration accordingly. This model captures different types of interdependencies with rigorous models at the component and system levels and predicts possible system recoveries under a given damage scenario in a probabilistic manner. This model can quantitatively evaluate the effectiveness of decision strategies on system recovery and resilience under different disaster recovery policies. As a demonstration example, this study applies the proposed model to the post-earthquake recovery simulation of three interdependent infrastructure systems (i.e., power, communication, and transportation) in the Lehigh Valley, Pennsylvania, USA. A total of sixteen cases were considered to represent different restoration strategies. For every case, the uncertainties in the recovery steps are captured by probabilistic simulation, and system resilience is calculated for every recovery sample. Simulation results from different strategies are compared to evaluate the effectiveness of non-intuitive strategies on system recovery and resilience. The proposed model uses a simple and straightforward concept to mimic practical disaster recovery plans. It is easy to understand and implement for modelers, and it is also useful to compare outcomes from different recovery criteria and decision strategies for practitioners.

摘要

由于人口的不断增长和气候变化的威胁，过去一个世纪以来，自然灾害的发生率不断上升，导致全球重大损失，需要大量的恢复工作。这个问题向决策者发出挑战，要求他们采取及时有效的行动来保护基础架构系统免受未来的自然灾害的影响。这项研究提出了一个基于策略的决策模型，作为PRAISys平台的一部分，用于恢复计划，以支持在不确定情况下相互依赖的基础架构系统的知情减灾。遵循实践中使用的灾难恢复优先级的概念，该模型根据预定义的策略确定每个恢复任务的优先级等级，并相应地模拟恢复。该模型使用组件和系统级别的严格模型来捕获不同类型的相互依赖关系，并以概率方式预测在给定损坏情况下可能的系统恢复。该模型可以定量评估不同灾难恢复策略下系统恢复和弹性决策策略的有效性。作为演示示例，本研究将提出的模型应用于美国宾夕法尼亚州里海谷的三个相互依赖的基础设施系统（即电力，通信和运输）的震后恢复模拟。总共考虑了十六例代表不同的恢复策略。对于每种情况，恢复步骤中的不确定性都是通过概率模拟来捕获的，并为每个恢复样本计算系统弹性。比较了来自不同策略的仿真结果，以评估非直观策略对系统恢复和弹性的有效性。提议的模型使用简单明了的概念来模仿实际的灾难恢复计划。对于建模者来说，这很容易理解和实施，对于从业者来说，比较不同恢复标准和决策策略的结果也很有用。