@article{ref1,
title="Model selection and uncertainty quantification of seismic fragility functions",
journal="ASCE-ASME journal of risk and uncertainty in engineering systems. Part A, Civil engineering",
year="2019",
author="",
volume="5",
number="3",
pages="e04019009-e04019009",
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.<p /> <p>Language: en</p>",
language="en",
issn="2376-7642",
doi="10.1061/AJRUA6.0001014",
url="http://dx.doi.org/10.1061/AJRUA6.0001014"
}