Variance‐based sensitivity analyses and uncertainty quantification for FEMA P‐58 consequence predictions

Gemma Cremen, Jack W. Baker
2020 Earthquake engineering & structural dynamics (Print)  
Earthquake loss assessment procedures for individual buildings can be a useful tool for various stakeholders, including building owners, insurers, and lenders. However, it is often not possible to provide complete information for the required inputs to these procedures because there is substantial cost and effort associated with gathering necessary data. It is therefore important to understand how different inputs to these procedures (building information/ground shaking intensity) impact the
more » ... s predictions. This can be done via sensitivity analyses. We conduct variance-based sensitivity analyses for the FEMA P-58 methodology, a building-specific seismic performance assessment procedure that is making its way into seismic design and risk analysis practice. We determine how variations in different input variables of the methodology affect predictions of building loss ratio and reoccupancy time, and benchmark calculated sensitivities using the HAZUS earthquake loss estimation methodology . We also quantify additional uncertainty in consequence predictions caused by uncertainty in input variables. We use an example site in downtown Los Angeles and consider a 7-story and a 14-story building. Of the six inputs considered in the analyses, building loss ratio predictions are most sensitive to shaking intensity and building age, while reoccupancy time predictions are most sensitive to shaking intensity and the type of lateral system/building period. The largest additional uncertainties in building loss ratio predictions are caused by the building's lateral system or age (or both) being unknown. The results of this study provide an enhanced understanding of the interaction between inputs and consequence predictions of the P-58 methodology. K E Y W O R D S FEMA P-58 methodology, seismic loss predictions, sensitivity analysis, uncertainty quantification
doi:10.1002/eqe.3370 fatcat:bo5yx2edjbh5rir3lkx7zc3c5i