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Bayesian Framework for Updating Seismic Loss Functions with Limited Observational Data in Low-to-Moderate Seismicity Regions
In low-to-moderate seismicity regions, seismic loss functions (SLFs) are barely established due to limited observational data, making it difficult to derive decision-making on disaster prevention and management. Herein, a Bayesian framework is developed to update the SLFs with limited observational data. The proposed point-based Bayesian method updates local probability density function parameters for damage ratios at each seismic intensity, which helps to avoid an unrealistic underestimation of damage ratios in the low-to-moderate range of seismic intensities. The feasibility of the developed framework in a low-to-moderate seismicity region is verified by the comparison between the updated SLF and post-event data.
Bayesian Framework for Updating Seismic Loss Functions with Limited Observational Data in Low-to-Moderate Seismicity Regions
In low-to-moderate seismicity regions, seismic loss functions (SLFs) are barely established due to limited observational data, making it difficult to derive decision-making on disaster prevention and management. Herein, a Bayesian framework is developed to update the SLFs with limited observational data. The proposed point-based Bayesian method updates local probability density function parameters for damage ratios at each seismic intensity, which helps to avoid an unrealistic underestimation of damage ratios in the low-to-moderate range of seismic intensities. The feasibility of the developed framework in a low-to-moderate seismicity region is verified by the comparison between the updated SLF and post-event data.
Bayesian Framework for Updating Seismic Loss Functions with Limited Observational Data in Low-to-Moderate Seismicity Regions
Choi, Insub (Autor:in) / Kim, JunHee (Autor:in) / Kang, WonHee (Autor:in) / Kim, Youngsuk (Autor:in)
Journal of Earthquake Engineering ; 26 ; 8205-8228
10.12.2022
24 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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