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Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms

Rungroad Suppakul Wittaya Jitchaijaroen Suraparb Keawsawasvong Sutasinee Intui Shinya Inazumi

Artificial Intelligence in Geosciences2025，Vol.6Issue(2)：P.467-480,14.

Artificial Intelligence in Geosciences2025，Vol.6Issue(2)：P.467-480,14.DOI:10.1016/j.aiig.2025.100162

Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms

Rungroad Suppakul ¹Wittaya Jitchaijaroen ¹Suraparb Keawsawasvong ¹Sutasinee Intui ²Shinya Inazumi³

作者信息

1. Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures,Department of Civil Engineering,Faculty of Engineering,Thammasat School of Engineering,Thammasat University,Pathumthani,Thailand
2. Faculty of Engineering at Sriracha,Kasetsart University,Chonburi,Thailand
3. College of Engineering,Shibaura Institute of Technology,Tokyo,Japan
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摘要

关键词

Open-caisson anchor/Mutation-based genetic algorithms/Genetic algorithms/XGBoost/FELA

分类

建筑与水利

引用本文复制引用

Rungroad Suppakul,Wittaya Jitchaijaroen,Suraparb Keawsawasvong,Sutasinee Intui,Shinya Inazumi..Undrained uplift capacity prediction of open-caisson anchors in anisotropic clays using XGBoost integrated with mutation-based genetic algorithms[J].Artificial Intelligence in Geosciences,2025,6(2):P.467-480,14.

Artificial Intelligence in Geosciences

ISSN：2666-5441

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