ID | 112202 |
Author |
Liao, Kuo-Wei
National Taiwan University
Muto, Yasunori
Tokushima University
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Lin, Jhe-Yu
Veterans Affairs Council
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Keywords | scour
flood
complicated foundation
optimization
machine learning
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Content Type |
Journal Article
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Description | 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.
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Journal Title |
KSCE Journal of Civil Engineering
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ISSN | 12267988
19763808
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Publisher | Korean Society of Civil Engineers
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Volume | 22
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Issue | 7
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Start Page | 2241
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End Page | 2255
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Published Date | 2017-10-12
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Remark | 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
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Rights | ©2018 Korean Society of Civil Engineers
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
Author
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departments |
Science and Technology
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