Developing effective optimized machine learning approaches for settlement prediction of shallow foundation

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dc.contributor.authorMohammad Khajehzadeh
dc.contributor.authorSuraparb Keawsawasvong
dc.contributor.authorViroon Kamchoom‬
dc.contributor.authorChao Shi
dc.contributor.authorAlimorad Khajehzadeh
dc.date.accessioned2026-05-08T19:16:51Z
dc.date.issued2024-8-25
dc.description.abstractwent from 0.9494 to 0.9290 to 0.9903, which is an increase of 4.5 % and 6 %.
dc.identifier.doi10.1016/j.heliyon.2024.e36714
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15712
dc.publisherHeliyon
dc.subjectGeotechnical Engineering and Analysis
dc.subjectDam Engineering and Safety
dc.subjectGeotechnical Engineering and Underground Structures
dc.titleDeveloping effective optimized machine learning approaches for settlement prediction of shallow foundation
dc.typeArticle

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