Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Model Selection and Uncertainty Quantification of Seismic Fragility Functions
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.
Model Selection and Uncertainty Quantification of Seismic Fragility Functions
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.
Model Selection and Uncertainty Quantification of Seismic Fragility Functions
Peña, Francisco (Autor:in) / Bilionis, Ilias (Autor:in) / Dyke, Shirley (Autor:in)
26.06.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Efficient seismic fragility functions through sequential selection
| Elsevier | 2020
Efficient seismic fragility functions through sequential selection
| Elsevier | 2020