Linking Infrastructure and Urban Economy: Simulation of Water-Disruption Impacts in Earthquakes

Abstract

In this paper a simulation approach to modeling the linkages between physical infrastructure systems and the urban economy is developed. A simulation approach based on probabilistically specifying the key model relationships is effective for situations that involve substantial uncertainty, and is particularly suited to assessing risk from natural hazards. In this paper, a model of economic losses from earthquakes is developed and applied to the Memphis, Tennessee, region of the United States. We focus on water as a critical infrastructure service supporting the urban economy. The methodological approach involves systems integration of natural-science, engineering, and social-science databases and models. The concept of infrastructure services provides the linchpin in this integration process. Key spatial, temporal, and functional dimensions of infrastructure services are explicitly modeled in the simulation framework. The resulting model permits the analyst to compare the effectiveness of alternative actions, including both predisaster mitigation and postdisaster emergency-response activities. The model is calibrated in part with data from the 1994 Northridge and 1995 Kobe earthquakes. Results for several scenario earthquakes indicate the likely range of loss from economic disruption as well as uncertainties associated with the loss estimates. Sensitivity analysis indicates that one type of risk-management strategy for the water system, retrofitting pump stations, appears to be highly effective in reducing expected losses from future disasters.

Mitigation of the consequence of seismically induced damage on a utility water network by means of next generation SCADA

“…In fact, the scope of analysis has expanded to the extent which the analysis can now predict, for example, the performance of the network in terms of the system resilience. This was demonstrated by Cheng, Svekla, and Shinozuka [3], integrating the economic impact of the earthquake damage, and by Chang and Shinozuka [4] by further incorporating additional parameters of resilience in the analysis using the same test-bed. The prediction is probabilistic in reflecting the random nature of the seismic event (location, intensity, and the time of occurrence) and the state of physical, operational and organizational conditions of the network at the time of its occurrence.…”

“…The prediction is probabilistic in reflecting the random nature of the seismic event (location, intensity, and the time of occurrence) and the state of physical, operational and organizational conditions of the network at the time of its occurrence. In the model used by Chang et al ( [3] and [1]), the pipelines are directly subjected to the seismic ground motion and can be damaged in accordance with the prescribed seismic fragility function for the pipes. The function determines the probability of the pipe damage (or in repairs) needed per km as a function of the peak ground acceleration (PGA) at the location of the pipe.…”

Post-disaster infrastructure restoration: A comparison of events for future planning

“…In defining the restoration of infrastructure functionality over time, modeling approaches typically take the form of statistical curve fitting or through simulations, such as resource constrained models [35,9], Markov processes [34,41,77], or detailed network models [5,51]. In modeling the restoration process, the deterministic resource constraint approach estimates recovery times based on empirical or assumed repair rates and available resources.…”

A framework to assess the social and economic impacts on communities and industries due to loss in serviceability of infrastructure networks after floods

“…Burrus et al [1] have developed a full-day equivalents lost (FDEL) metric to measure the impact of frequent business interruption due to lowintensity hurricane regions. Chang et al [3] applied a simulation approach to model the linkage between physical infrastructure systems and the urban economy. In addition, Chang [2] has developed a framework for extended life cycle cost analysis to evaluate mitigation strategies for lifeline systems, such as electric power, water and transportation.…”