Fragility curves of restoration processes for resilience analysis Barberis, Fabiana; Malavisi, Marzia; Cimellaro, Gian Paolo; Mahin, Stephen
In literature the fragility curves are usually adopted to evaluate the probability of exceedance of a given damage state, while in this paper is presented for the first time a procedure for building fragility curves of restoration processes which can be adopted for resilience analysis. The restoration process describes the capacity to recover of a system after a failure. In order to have a resilience system, it is necessary to reduce the consequences from failures by shortening the recovery time and reducing the probability of damage. The restoration process is one of the most uncertain variables in the resilience analysis therefore, it is necessary to consider it in probabilistic terms. The method has been applied to the performance of a hospital during an emergency. A discrete event simulation model has been built to simulate different restoration processes. The set of restoration processes obtained through Monte Carlo simulations has been analyzed statistically to determine the probability of exceedance of a given restoration state. Restoration Fragility Functions (RFF) are obtained using the maximum likelihood estimation (MLE) approach. The probability of restoration for a given earthquake intensity (e.g. MMI) level, x, can then be estimated as the fraction of records for which restoration occurs at a level lower than x. A lognormal cumulative distribution function is used to fit the data, to provide a continuous estimate of the probability of restoration as a function of MMI. Two different case scenarios are compared: the Emergency Department (ED) with and without emergency plan applied. Finally, different methods to build fragility curves are compared in order to evaluate the RFF.
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