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ID 112202
著者
Liao, Kuo-Wei National Taiwan University
Lin, Jhe-Yu Veterans Affairs Council
キーワード
scour
flood
complicated foundation
optimization
machine learning
資料タイプ
学術雑誌論文
抄録
A scour depth prediction formula for a river bridge is established using experimental data in which the effects of the pier, pile-cap and pile group are considered. More than 170 experimental data entries, including different pier structural sizes, flow depths and soil covering depths, are collected and verified by existing formulae, which failed to deliver a promising prediction. A machine learning prediction model was then developed to enhance the accuracy. For application purpose, a sequential quadratic programming optimization was adopted to construct an explicit prediction formula. The MAPE was significantly improved from 102.8 to 28.9. The results indicate that the proposed formula can simultaneously satisfy the requirements of accuracy and simplicity. The proposed formula has the advantages of being conceptually consistent with observed scour behaviors and provides a solid scour depth prediction, which is an important and critical step in the bridge safety evaluation if floods are considered.
掲載誌名
KSCE Journal of Civil Engineering
ISSN
12267988
19763808
出版者
Korean Society of Civil Engineers
22
7
開始ページ
2241
終了ページ
2255
発行日
2017-10-12
備考
This is a post-peer-review, pre-copyedit version of an article published in KSCE Journal of Civil Engineering.The final publication is available at link.springer.com
権利情報
©2018 Korean Society of Civil Engineers
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
著者版
部局
理工学系