Collaborative Research: Novel Fractional Order Ground Motion Intensity Measures for High Confidence Risk Assessment of Distributed Infrastructures

合作研究：用于分布式基础设施高置信度风险评估的新型分数阶地震动强度测量

• 批准号: 1462177
• 负责人: Jamie Padgett
• 金额: $ 22.18万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462177&HistoricalAwards=false
• 关键词: Collaborative; Research; Novel; Fractional; Order

Seismic risk assessment frameworks support risk-informed decision making in planning strategies for disaster prevention and mitigation. Such frameworks rely on intensity measures (IMs) to represent the strength of earthquake events, and to predict the behavior of key infrastructure components and their cascading effects on network performance and socio-economic systems. However, the current practice of adopting hazard intensity measures based on integer order derivatives or integrals of the ground motion time history is not ideal to predict infrastructure performance, and may induce significant uncertainties in the final outcome of regional risk analyses. In order to release the limitation of discrete integer order differential operations for IM characterization, this research will use new classes of spatially correlated earthquake intensity measures for regional risk assessment of infrastructures termed "á-order IMs" based on concepts from fractional order calculus. The methodology and tools provide more accurate probabilistic predictions of the seismic response of structures and infrastructure components by significantly reducing uncertainties, thus increasing the confidence in seismic reliability and risk assessment of complex systems. This achievement will offer broad impacts to owners charged with managing risks to large distributed infrastructure systems, and the public at large who benefit from associated risk-informed decisions on mitigation and response strategies.The project will use derivation of novel fractional order ground motion responses to characterize earthquake intensity, including identification of computationally efficient algorithms to conduct the fractional order operations. The optimal demand model form and á-order for the IMs will be identified to enable probabilistic response prediction of a wide range of complex infrastructure constituents anticipated across a regional portfolio. The project will also develop correlated ground motion prediction equations (GMPEs) for the fractional order IMs and quantify the resulting reduced uncertainty in risk estimates (e.g. network performance, economic losses) for distributed infrastructure systems. The advancements offered by this research will afford more robust analytical methods for probabilistic characterization of earthquakes across a region, efficient modeling of the physical demand imparted on infrastructures, and increased confidence in resulting risk estimates. The overall uncertainty reduction can advance risk-informed decision making targeted at reducing human casualties, economic losses, and loss of function of infrastructure in seismic zones.

地震风险评估框架支持在规划防灾和减灾战略时作出风险知情的决策。这种框架依赖于强度测量（IM）来表示地震事件的强度，并预测关键基础设施组件的行为及其对网络性能和社会经济系统的级联效应。然而，目前的做法，采用灾害强度措施的基础上整数阶导数或积分的地面运动时程是不理想的预测基础设施的性能，并可能导致重大的不确定性，在最终结果的区域风险分析。为了克服离散整数阶微分运算对IM特性的限制，本研究将基于分数阶微积分的概念，采用新的空间相关地震烈度测度对基础设施进行区域风险评估，称为“α阶IM”。该方法和工具通过显著降低不确定性，提供了对结构和基础设施部件地震响应的更准确的概率预测，从而提高了对复杂系统的地震可靠性和风险评估的信心。这一成果将提供广泛的影响，业主负责管理风险的大型分布式基础设施系统，并在广大公众谁受益于相关的风险知情的决策缓解和响应strategies.The项目将使用推导新颖的分数阶地面运动响应来表征地震烈度，包括识别计算效率高的算法进行分数阶操作。将确定IM的最佳需求模型形式和α-顺序，以便能够对区域投资组合中预期的各种复杂基础设施组成部分进行概率响应预测。该项目还将为分数阶IM开发相关地面运动预测方程（GMPEs），并量化分布式基础设施系统风险估计（例如网络性能，经济损失）中的不确定性。这项研究所提供的进步将提供更强大的分析方法，用于对整个地区的地震进行概率表征，对基础设施的物理需求进行有效建模，并提高对由此产生的风险估计的信心。整体不确定性的降低可以促进风险知情决策，旨在减少人员伤亡，经济损失和地震区基础设施功能的损失。