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

地震风险评估框架支持在规划防灾减灾战略时作出风险知情的决策。这些框架依赖烈度度量(IMS)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连锁影响。然而，目前采用基于地面运动时程的整数阶导数或积分的灾害强度测量的做法对于预测基础设施的性能并不理想，并且可能在区域风险分析的最终结果中引入重大不确定性。为了克服离散整数阶差分运算用于IM表征的局限性，本研究将基于分数阶微积分的概念，将空间相关的新型地震烈度度量用于基础设施的区域风险评估，称为“α阶IMS”。该方法和工具通过显著减少不确定性，为结构和基础设施部件的地震响应提供了更准确的概率预测，从而增加了对复杂系统的地震可靠性和风险评估的信心。这一成果将对负责管理大型分布式基础设施系统风险的业主和广大公众产生广泛的影响，他们受益于相关的缓解和响应战略风险知情决策。该项目将使用新的分数阶地面运动响应的推导来表征地震烈度，包括识别执行分数阶运算的计算高效算法。将确定IMS的最佳需求模型形式和顺序，以实现对区域投资组合中预期的各种复杂基础设施组成部分的概率响应预测。该项目还将开发分数阶IMS的相关地面运动预测方程(GMPE)，并量化由此降低的分布式基础设施系统风险估计的不确定性(例如网络性能、经济损失)。这项研究的进展将为区域地震的概率特征提供更可靠的分析方法，对基础设施的实际需求进行有效的建模，并增加对由此产生的风险估计的信心。总体上减少不确定性可以推进风险知情决策，旨在减少人员伤亡、经济损失和地震区基础设施功能丧失。

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.