Fragility curves of restoration processes for resilience analysis

Abstract

In literature the fragility curves are usually adopted to evaluate the probability of exceedance of a given damage state. This chapter presents for the first time a procedure for developing fragility curves of restoration processes which can be adopted for resilience analysis. The restoration process describes the capacity to recover from a system failure and it is one of the most uncertain variables in the resilience analysis therefore, the problem should be treated in probabilistic terms. In the chapter, a method is proposed for evaluating the Restoration Fragility Functions (RFF) of a given system following an extreme event. The restoration curves have been built empirically using the data obtained by a discrete event simulation model of the system considered. Different restoration processes obtained through Monte Carlo simulations have been analyzed statistically to determine the probability of exceedance of a given restoration state. Then, Restoration Fragility Functions (RFF) are obtained using the Maximum Likelihood Estimation (MLE) approach assuming a lognormal cumulative distribution function. The method has been applied to an Emergency Department of a hospital during a crisis, because these buildings are critical facilities which should withstand after an earthquake in order to assist injuries. Two different case studies have been compared: the Emergency Department (ED) with and without emergency plan.