[论文解读] Fragility curves for power transmission towers in Odisha, India, based on observed damage during 2019 Cyclone Fani

Surender V. Raj, Manish Kumar+1 more|arXiv (Cornell University)|Jun 26, 2021

Infrastructure Resilience and Vulnerability Analysis参考文献 18被引用 9

一句话总结

本研究基于2019年气旋Fani在印度奥里萨邦造成的实际损毁数据，为该地区高压输电塔开发了区域特定的易损性曲线。研究同时考虑了随机不确定性与认知不确定性，特别是风速估算误差，从而生成了稳健的倒塌与功能中断易损性曲线，其结果与全球同类曲线具有良好的可比性。

ABSTRACT

Lifeline infrastructure systems such as a power transmission network in coastal regions are vulnerable to strong winds generated during tropical cyclones. Understanding the fragility of individual towers is helpful in improving the resilience of such systems. Fragility curves have been developed in the past for some regions, but without considering relevant epistemic uncertainties. Further, risk and resilience studies are best performed using the fragility curves specific to a region. Such studies become particularly important if the region is exposed to cyclones rather frequently. This paper presents the development of fragility curves for high-voltage power transmission towers in the state of Odisha, India, based on macro-level damage data from 2019 cyclone Fani, which was obtained through concerned government offices. Two types of damages were identified, namely, collapse and partial damage. Accordingly, fragility curves for collapse and functionality disruption damage states were developed considering relevant aleatory and epistemic uncertainties. The latter class of uncertainties included that associated with wind speed estimation at a location and the finite sample uncertainty. The most significant contribution in the epistemic uncertainty was due to the wind speed estimation at a location. The median and logarithmic standard deviation for the 50th percentile fragility curve associated with collapse was close to that for the functionality disruption damage state. These curves also compared reasonably well with those reported for similar structures in other parts of the world.

研究动机与目标

• 利用2019年气旋Fani期间奥里萨邦实际损毁数据，开发该地区输电塔的易损性曲线。
• 量化并纳入认知不确定性，特别是塔位风速估算误差，以改进易损性建模。
• 通过区分倒塌与部分损毁状态，评估输电基础设施的韧性。
• 基于实证驱动的易损性模型，实现对易受飓风影响的沿海地区的区域特定风险与韧性评估。
• 通过与全球基准对比，验证所开发曲线在结构相似性与可靠性方面的表现。

提出的方法

• 从政府来源收集气旋Fani（2019年）后奥里萨邦输电塔的宏观损毁数据（倒塌与部分损毁）。
• 定义两种损毁状态：(1) 结构倒塌；(2) 由于部分损毁导致的功能中断。
• 采用类地震危险性分析（PSHA）的方法，但针对风致损毁进行调整，以风速作为强度度量。
• 通过塔位风速估算误差及有限样本不确定性，结合贝叶斯推断，纳入认知不确定性。
• 基于对强度度量的对数正态分布假设，使用最大似然估计法校准易损性曲线。
• 量化50百分位易损性曲线的中值承载力与对数标准差，并考虑不确定性传播。

实验结果

研究问题

• RQ1 基于气旋Fani的实测损毁数据，印度奥里萨邦高压输电塔的易损性曲线是什么？
• RQ2 认知不确定性（特别是风速估算）如何影响易损性曲线构建的可靠性？
• RQ3 奥里萨邦输电塔中，倒塌与功能中断损毁状态的中值承载力与离散度如何比较？
• RQ4 所开发的易损性曲线在多大程度上与全球其他地区类似结构的曲线一致？
• RQ5 不同不确定性来源对易损性建模中总体认知不确定性的相对贡献是什么？

主要发现

• 50百分位易损性曲线的中值承载力在倒塌与功能中断两种损毁状态下相近，表明两种状态的失效起始风速阈值相似。
• 两种损毁状态的对数标准差也相近，表明两种状态下失效响应的变异性一致。
• 认知不确定性中最主要的来源是单个塔位的风速估算误差，凸显了高精度站点特定风速数据的重要性。
• 为奥里萨邦开发的易损性曲线与全球其他地区类似结构的曲线具有合理的吻合度，支持其区域适用性。
• 同时纳入随机与认知不确定性显著提升了易损性曲线在风险与韧性评估中的可靠性与现实性。
• 本研究表明，基于实际气旋损毁数据的区域特定易损性曲线，对飓风多发区基础设施韧性建模至关重要。