Model Selection and Uncertainty Quantification of Seismic Fragility Functions
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5, Issue 3
Abstract
A fragility function quantifies the probability that a structural system exposed to a given hazard exceeds an undesirable limit state event conditioned on the occurrence of a hazard level. Multiple sources of uncertainty affect this function, including record-to-record variation, geometric and material properties, aging, modeling assumptions and errors, and even the analyzed dataset. This study presents a methodology for statistical model selection and uncertainty quantification of seismic fragility functions. The statistical models are created by implementing a hierarchical Bayesian framework with a sequential Monte Carlo technique. The most probable model is selected using Bayesian model selection. This model is validated through multiple metrics using predictive intervals and the Kolmogorov-Smirnov test. Then, the epistemic uncertainty is quantified as the variance of the area under the fragility functions. The methodology is implemented on a twenty-story steel benchmark model case study, demonstrating that the log-normal distribution yields superior performance relative to other models considered. Finally, further analysis of the case study demonstrates that the epistemic uncertainty is considerably reduced when using forty observations.
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Acknowledgments
The authors acknowledge the financial support provided by COLCIENCIAS (Departamento Administrativo de Ciencia, Tecnologia e Innovacion) through scholarship 568 (2012) for Ph.D. studies abroad and the Colombia-Purdue Institute for Advanced Scientific Research toward this research.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 5 • Issue 3 • September 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jan 4, 2018
Accepted: Jan 9, 2019
Published online: Jun 26, 2019
Published in print: Sep 1, 2019
Discussion open until: Nov 26, 2019
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