Bayesian Framework for Updating Seismic Loss Functions with Limited Observational Data in Low-to-Moderate Seismicity Regions

ABSTRACT

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.

KEYWORDS:

• Bayesian updating
• seismic loss function
• limited observational data
• damage ratio
• RC school buildings

Acknowledgments

This research was supported by a grant (NRF-2021R1A2C2007064) from the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Science and ICT (MSIT) and by the Yonsei University Research Fund (Post Doc. Researcher Supporting Program) of 2020 (2020-12-0144).

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by a grant (NRF-2021R1A2C2007064) from the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Science and ICT (MSIT) and by the Yonsei University Research Fund (Post Doc. Researcher Supporting Program) of 2020 (2020-12-0144).