Uncertainty quantification of fragility and risk estimates due to seismic input variability and capacity model uncertainty. (15th September 2019)

• Record Type: Journal Article
• Authors: Skoulidou, Despoina; Romão, Xavier
• Is Part Of: Engineering structures. Volume 195(2019)
• Page Start: 425
• Page End: 437
• Publication Date: 2019-09-15
• Subjects: Fragility function -- Parameter variability -- Failure rate variability -- Probabilistic capacity -- Bootstrap; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.05.067
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Seismic fragility and risk variability from bootstrap and regrouping are compared. Some bootstrap conditions are inconsistent with common PBEE applications. Conditions under which bootstrap yields adequate results. Limit state uncertainty doesn't affect the relative performance of the two methods. Limit state uncertainty yields lower variability estimates for all parameters. Abstract: When evaluating the probabilistic seismic performance of a structure in the context of Performance-Based Earthquake Engineering (PBEE) applications, only estimates of the true response are obtained due to the finite size of the group of records selected to represent the seismic scenario. The proposed study examines the variability of these estimates, in terms of fragility parameters and failure rates, by implementing a procedure that creates seismic scenario-consistent groups of records based on regrouping criteria applied to a larger set of records. The results are then compared to those obtained from a statistical approach based on the bootstrap resampling procedure, whose validity is disputed due to its incompatibility with common applications involving probabilistic seismic performance assessment. Given that bootstrap resampling assumes that each bootstrap sample is random, this condition is incompatible with requirements involving the need to match specific spectral statistics of the group of records with a target spectrum. Due to the increased computational cost of the procedure based on regrouping criteria, the effect of this incompatibility is analysed by examining the agreement between the two procedures for several case studies and the conditions under which bootstrap resampling leads to a reduced level of error. Further insights about the variability of the estimates are obtained by analysing scenarios with deterministic and probabilistic thresholds of the limit state capacity. Among other aspects, the results show that, for both capacity thresholds, bootstrap resampling can provide acceptable results as long as a sufficient number of ground motions and number of stripes are used.