Quantifying uncertainties in fragility function parameter estimation for structural modeling uncertainties

Date

2024-3-11

Author

Ünal, Barış

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Large-scale seismic risk assessments have a crucial role in disaster management planning. Ensuring the resilience of communities against seismic events necessitates the accurate assessment of structural vulnerabilities. In this study, the effects of uncertainties originating from diverse sources such as material properties, geometric complexities, and modeling assumptions on fragility curves are investigated for uncertainty quantification in fragility functions to improve the accuracy of loss assessment analyses. This research focuses on estimating fragility function parameters for selected code-compliant residential buildings consisting of reinforced concrete frames while considering various modeling uncertainties. Through rigorous simulations and sensitivity analyses, the study systematically examines the influence of these uncertainties on the fragility predictions. The Latin hypercube sampling technique is utilized to represent the probabilistic distribution of random variables in structural analyses. The seismic input is expressed through hazard analyses conducted for different soil conditions and hazard levels. The study emphasizes the importance of accounting for structural variability in fragility calculations with comparative analysis between scenarios with and without modeling uncertainties and contributes to probabilistic structure response assessment by quantifying the impact of a wide range of structural input variables on the fragility calculations. This research contributes to probabilistic structure response assessment by offering insights into the influence of structural input variables on fragility, ultimately contributing to safer and more resilient civil infrastructure in seismic regions.

Subject Keywords

Structural fragility, Modeling uncertainty, Uncertainty quantification, Parameter sensitivity

URI

https://hdl.handle.net/11511/109363

Collections

Graduate School of Natural and Applied Sciences, Thesis