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ID 110892
著者
Liao, Kuo‑Wei National Taiwan University of Science and Technology
Chen, Wei‑Lun National Taiwan University of Science and Technology
Wu, Bang‑Ho National Taiwan University of Science and Technology
キーワード
Bridge safety
Flood-resistant reliability
MCS
Bayesian LS-SVM
資料タイプ
学術雑誌論文
抄録
To further capture the influences of uncertain factors on river bridge safety evaluation, a probabilistic approach is adopted. Because this is a systematic and nonlinear problem, MPP-based reliability analyses are not suitable. A sampling approach such as a Monte Carlo simulation (MCS) or importance sampling is often adopted. To enhance the efficiency of the sampling approach, this study utilizes Bayesian least squares support vector machines to construct a response surface followed by an MCS, providing a more precise safety index. Although there are several factors impacting the flood-resistant reliability of a bridge, previous experiences and studies show that the reliability of the bridge itself plays a key role. Thus, the goal of this study is to analyze the system reliability of a selected bridge that includes five limit states. The random variables considered here include the water surface elevation, water velocity, local scour depth, soil property and wind load. Because the first three variables are deeply affected by river hydraulics, a probabilistic HEC-RAS-based simulation is performed to capture the uncertainties in those random variables. The accuracy and variation of our solutions are confirmed by a direct MCS to ensure the applicability of the proposed approach. The results of a numerical example indicate that the proposed approach can efficiently provide an accurate bridge safety evaluation and maintain satisfactory variation.
掲載誌名
SpringerPlus
ISSN
21931801
出版者
Springer
5
開始ページ
783
並び順
783
発行日
2016-06-18
備考
© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
EDB ID
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フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
理工学系