1 Introduction

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 product of the components, their properties and interactions.

SIRA stands for Systemic Infrastructure 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. SIRA enables the vulnerabilities of each element to be within a facility or a network to be integrated into a holistic assessment of the direct system losses, service disruption and cost.

SIRA 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 facilitates quantification of infrastructure assets’ vulnerability, and 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. SIRA helps generate 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 or a network. 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 most facility types, along with the recovery time estimation method, has been taken from HAZUS [5]. Although, where deemed more appropriate, custom damage scales have been used, e.g. for electrical substations. 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.