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预防灾难和减轻灾难的计划策略中风险信息的决策。这样的框架依靠强度措施（IMS）来代表地震事件的强度，并预测关键基础设施组件的行为及其对网络绩效和社会经济系统的级联影响。但是，基于整数订单衍生物或地面运动时间历史的积分进行危害强度措施的当前实践并不是预测基础设施绩效的理想选择，并且可能在区域风险分析的最终结果中引起重大不确定性。为了释放IM表征的离散整数差异操作的局限性，该研究将使用基于分数级计算的概念的基础设施的区域风险评估，使用空间相关的地震强度度量进行区域风险评估。该方法和工具通过显着降低不确定性，为结构和基础设施组件的地震反应提供了更准确的概率预测，从而提高了对复杂系统的地震可靠性和风险评估的信心。这项成就将为负责管理大型分布式基础架构系统风险的所有者和大大受益于缓解和响应策略的风险信息的决策受益的公众。该项目还将为IMS开发相关的地面运动预测方程（GMPE），并量化了分布式基础设施系统的风险估计值（例如网络绩效，经济损失）的降低的不确定性。这项研究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的概率表征，对基础设施赋予的物理需求的有效建模以及对产生风险估计的信心提高。总体不确定性降低可以推进针对减少人类伤亡，经济损失和地震区基础设施功能损失的风险信息的决策。