Modeling uncertainty can have a significant impact on the assessed risk of earthquake-induced damage and collapse in a building obtained through the performance-based earthquake engineering framework. This paper quantifies the effect of modeling uncertainty on performance-based risk assessments, accounting for differences in software platforms, solution algorithm, element-types, and model parameter calculation or selection, using results of the 7-story reinforced concrete (RC) building blind prediction contest (2006) at University of California at San Diego (UCSD). The blind prediction test data provide a unique opportunity to quantify the influence of modeling uncertainty on structural response predictions. In this study, the bias and variability of predicted drift in the contest submissions are taken to represent modeling uncertainty. The modeling uncertainty quantified from the UCSD test submissions is combined with uncertainty due to record-to-record variability and a set of fragility curves are computed for different drift levels. The final fragility curves account for modeling and ground motion uncertainty. Results are compared with the results with no modeling uncertainty. The key contribution of this study is to investigate the uncertainty embedded in the response due to combination of ground motion and modeling uncertainty.

Quantification of modeling uncertainties based on the blind prediction contest submissions

MARTINELLI, PAOLO
2013-01-01

Abstract

Modeling uncertainty can have a significant impact on the assessed risk of earthquake-induced damage and collapse in a building obtained through the performance-based earthquake engineering framework. This paper quantifies the effect of modeling uncertainty on performance-based risk assessments, accounting for differences in software platforms, solution algorithm, element-types, and model parameter calculation or selection, using results of the 7-story reinforced concrete (RC) building blind prediction contest (2006) at University of California at San Diego (UCSD). The blind prediction test data provide a unique opportunity to quantify the influence of modeling uncertainty on structural response predictions. In this study, the bias and variability of predicted drift in the contest submissions are taken to represent modeling uncertainty. The modeling uncertainty quantified from the UCSD test submissions is combined with uncertainty due to record-to-record variability and a set of fragility curves are computed for different drift levels. The final fragility curves account for modeling and ground motion uncertainty. Results are compared with the results with no modeling uncertainty. The key contribution of this study is to investigate the uncertainty embedded in the response due to combination of ground motion and modeling uncertainty.
Structures Congress 2013: Bridging Your Passion with Your Profession - Proceedings of the 2013 Structures Congress
978-078441284-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/972332
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