Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data
Infrastructure recovery curve estimation using Gaussian process regression on expert elicited data
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DOI：
10.1016/j.ress.2021.108054
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Q. D. Cao;S. Miles;Youngjun Choe
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文献类型：
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作者：
Q. D. Cao;S. Miles;Youngjun Choe
美国国家标准与技术研究院（NIST）的社区恢复规划指南将基础设施功能的恢复时间作为灾难恢复的关键指标。现有文献还广泛使用恢复曲线及其下的面积来衡量基础设施的复原力。因此，基础设施恢复曲线估计对于理解和提高抗灾能力至关重要。不幸的是，由于缺乏历史数据，这一过程在活动前规划方面具有挑战性。为了弥补这一差距，我们考虑这样一种情况，即要求基础设施专家估计不同基础设施系统在场景危害事件后恢复到特定功能级别的时间。我们提出了一个方法框架，使用专家引发的数据来估计一个特定的基础设施系统的预期恢复时间曲线。该框架使用高斯过程回归（GPR）来捕获专家的估计不确定性，并满足已知的恢复过程的物理约束。该框架的目的是找到一个平衡的数据收集成本的专家启发和GPR的预测精度。我们评估的框架模拟专家引发的数据有关的两个案例研究事件，1995年坂神淡路大地震和2011年东日本大地震。结果表明，该框架是强大的，对不同的配置，如专家的数量，如何引起的兴趣，以及专家的估计的不确定性。
The U.S. National Institute of Standards and Technology (NIST)’s Community Resilience Planning Guide uses recovery times of infrastructure functions as key metrics for disaster resilience. The existing literature also widely uses the recovery curve and the area under it to measure infrastructure resilience. Therefore, infrastructure recovery curve estimation is critical to understanding and improving disaster resilience. Unfortunately, this process is challenging in the pre-event planning context due to lack of historical data. To bridge this gap, we consider a situation where infrastructure experts are asked to estimate the time for different infrastructure systems to recover to certain functionality levels after a scenario hazard event. We propose a methodological framework to use expert-elicited data to estimate the expected recovery time curve of a particular infrastructure system. This framework uses the Gaussian process regression (GPR) to capture the experts’ estimation-uncertainty and satisfy known physical constraints of recovery processes. The framework is designed to find a balance between the data collection cost of expert elicitation and the prediction accuracy of GPR. We evaluate the framework on simulated expert-elicited data concerning two case study events, the 1995 Great Hanshin-Awaji Earthquake and the 2011 Great East Japan Earthquake. It is shown that the framework is robust against different configurations such as the number of experts, how the quantities of interest are elicited, and uncertainty in the experts’ estimates.