A Bayesian bridge model update with complex uncertainty under high-speed train passages

K. Matsuoka, D. Mizutani, C. Somaschini, L. Bernardini, A. Collina

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

抄録

Bayesian structural model updating is an important technique to achieve digital twin with uncertainty. However, complexly distorted tails of the joint posterior probability density function (PDF) are not easy to estimate when a correlation exists between model parameters. Estimating the lower confidence bound of the joint posterior PDF requires several samples at the tails of the PDF. However, this task is difficult to achieve because the Markov chain Monte Carlo (MCMC) method concentrates on the samples near the expected value. Thus, to estimate the distorted tails of the joint posterior, the authors develop a new methodology called dual sampling, comprising two-step MCMCs. The second step sampling complements the samples around the tail of the joint posterior PDF, which are insufficient in the first step conventional MCMC method. The proposed method is applied to the maximum acceleration data of an Italian high-speed railway bridge.

本文言語英語
ホスト出版物のタイトルLife-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
編集者Fabio Biondini, Dan M. Frangopol
出版社CRC Press/Balkema
ページ237-243
ページ数7
ISBN(印刷版)9781003323020
DOI
出版ステータス出版済み - 2023
イベント8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023 - Milan, イタリア
継続期間: 2023 7月 22023 7月 6

出版物シリーズ

名前Life-Cycle of Structures and Infrastructure Systems - Proceedings of the 8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023

会議

会議8th International Symposium on Life-Cycle Civil Engineering, IALCCE 2023
国/地域イタリア
CityMilan
Period23/7/223/7/6

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