TY - JOUR
T1 - Bayesian framework for updating seismic loss functions with limited observational data in low-to-moderate seismicity regions
AU - Choi, Insub
AU - Kim, JunHee
AU - Kang, WonHee
AU - Kim, Youngsuk
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/1959.7/uws:65326
U2 - 10.1080/13632469.2021.1987356
DO - 10.1080/13632469.2021.1987356
M3 - Article
SN - 1363-2469
VL - 26
SP - 8205
EP - 8228
JO - Journal of Earthquake Engineering
JF - Journal of Earthquake Engineering
IS - 16
ER -