Earthquake Hazard Mitigation Using a Systems Analysis Approach to Risk Assessment

Abstract:

The earthquake hazard mitigation goal is to reduce losses due to severe natural events. The first step is to conduct a Seismic Risk Assessment consisting of 1) hazard estimation, 2) vulnerability analysis, 3) exposure compilation. Seismic hazards include ground deformation, shaking, and inundation. The hazard estimation may be probabilistic or deterministic. Probabilistic Seismic Hazard Assessment (PSHA) is generally applied to site-specific Risk assessments, but may involve large areas as in a National Seismic Hazard Mapping program. Deterministic hazard assessments are needed for geographically distributed exposure such as lifelines (infrastructure), but may be important for large communities. Vulnerability evaluation includes quantification of fragility for construction or components including personnel. Exposure represents the existing or planned construction, facilities, infrastructure, and population in the affected area. Risk (expected loss) is the product of the quantified hazard, vulnerability (damage algorithm), and exposure which may be used to prepare emergency response plans, retrofit existing construction, or use community planning to avoid hazards. The risk estimate provides data needed to acquire earthquake insurance to assist with effective recovery following a severe event. Earthquake Scenarios used in Deterministic Risk Assessments provide detailed information on where hazards may be most severe, what system components are most susceptible to failure, and to evaluate the combined effects of a severe earthquake to the whole system or community. Casualties (injuries and death) have been the primary factor in defining building codes for seismic-resistant construction. Economic losses may be equally significant factors that can influence proactive hazard mitigation. Large urban earthquakes may produce catastrophic losses due to a cascading of effects often missed in PSHA. Economic collapse may ensue if damaged workplaces, disruption of utilities, and resultant loss of income produces widespread default on payments. With increased computational power and more complete inventories of exposure, Monte Carlo methods may provide more accurate estimation of severe losses and the opportunity to increase resilience of vulnerable systems and communities.