Details
| Translated title of the contribution | EFFICIENT SEISMIC PERFORMANCE ESTIMATION METHOD BY SURROGATE MODELING BASED ON ADAPTIVE KRIGING AND MARKOV CHAIN MONTE CARLO |
|---|---|
| Original language | Japanese |
| Pages (from-to) | 75-86 |
| Journal | Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) |
| Volume | 76 |
| Issue number | 1 |
| Early online date | 20 Dec 2020 |
| Publication status | Published - 2020 |
Abstract
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In: Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)), Vol. 76, No. 1, 2020, p. 75-86.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - 適応型クリギングとMCMC法に基づく代替モデルを用いた効率的な耐震性能評価手法
AU - Kitahara, Masaru
AU - Broggi, Matteo
AU - Beer, Michael
PY - 2020
Y1 - 2020
N2 - It is well known that probabilistic estimation of the residual seismic performance of existing bridges is important for their maintenance and it is hence desired to build an accurate and efficient structural reliability method. In this study, a surrogate modeling method, namely AK-MCMC, based on the adaptive Kriging and Markov chain Monte Carlo (MCMC) is introduced. The adaptive Kriging allows to automatically select important samples for constructing the surrogate model and MCMC searches intermediate failure regions, which will converge to the failure region, step by step. In order to extend the method to dynamic nonlinear problems, a method for calculating the failure probability based on Subset simulation using the obtained Kriging surrogate model is proposed. The applicability to the seismic performance estimation of an aging seismic-isolated bridge is examined. The results show that the proposed method is computationally very efficient and applicable to the seismic performance estimation of both the health and deteriorated conditions with different failure probability.
AB - It is well known that probabilistic estimation of the residual seismic performance of existing bridges is important for their maintenance and it is hence desired to build an accurate and efficient structural reliability method. In this study, a surrogate modeling method, namely AK-MCMC, based on the adaptive Kriging and Markov chain Monte Carlo (MCMC) is introduced. The adaptive Kriging allows to automatically select important samples for constructing the surrogate model and MCMC searches intermediate failure regions, which will converge to the failure region, step by step. In order to extend the method to dynamic nonlinear problems, a method for calculating the failure probability based on Subset simulation using the obtained Kriging surrogate model is proposed. The applicability to the seismic performance estimation of an aging seismic-isolated bridge is examined. The results show that the proposed method is computationally very efficient and applicable to the seismic performance estimation of both the health and deteriorated conditions with different failure probability.
U2 - 10.2208/jscejam.76.1_75
DO - 10.2208/jscejam.76.1_75
M3 - Article
VL - 76
SP - 75
EP - 86
JO - Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM))
JF - Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM))
SN - 2185-4661
IS - 1
ER -