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

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.

 

Funding Source: National Science Foundation https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462183