Critical infrastructure facilities typically comprise a number of interconnected components that work in concert to deliver a service. In the context of natural hazard vulnerability, the components have differing susceptibilities, require different resource levels and time to repair, and have a range of criticalities to the overall service delivery. The vulnerability of the facility, then, is a complex convolution of all these components and factors.
SIFRA stands for System for Infrastructure Facility Resilience Analysis.
It comprises a method and software tools that provide a framework for
simulating the fragility of infrastructure facilities to natural hazards,
based on assessment of the fragilities and configuration of components that
comprises the facility. Currently the system is designed to work with
earthquake hazards only. SIFRA enables the vulnerabilities of each element
to be integrated into an assessment of the implications of severe hazard
exposure to facility damage, service disruption and cost.
SIFRA is used to model the vulnerability of high-value infrastructure facilities to natural hazards. Earthquake ground motion is the present focus and many uncertainties are captured through a Monte Carlo sampling process. The tool not only enables current facility vulnerability to be quantified, but also enables the most vulnerable components to be identified in terms of repair cost, time to recovery, and service disruption implications. The outcomes also support benefit versus cost studies of retrofit options. Ultimately, it provides information that supports asset managers in regards to the most cost-effective utilisation of limited retrofit resources.
Vulnerability of a facility is modelled by assigning fragilities to the individual components that make up a facility. The program accounts for variability in component fragilities by sampling probability distributions for the each fragility curve median and beta values. Once values have been selected for each curve it checks that fragility curves do not overlap and if they do, re-samples the median and beta probability distributions until non-overlapping fragility curves are produced.
Damage scales for several assets, and method for estimating recovery times have been taken from HAZUS [5]. Repair cost (and hence damage index) and recovery times for each component are customised for each asset type, based on consultation with assets operators. The threshold values of spectral acceleration for each of four damage states are sampled by randomly sampling the fragility curves described above.
Hazard modelling is done externally using other applications, and provided to this tool as an input.
Currently only peak ground acceleration can be accepted by the system, but this is to be expanded in the near future.