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Machine Learning in Geotechnics: Predicting Unconfined Compressive Strength of Stabilized Organic Soils Using Hybrid Models.

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Title: Machine Learning in Geotechnics: Predicting Unconfined Compressive Strength of Stabilized Organic Soils Using Hybrid Models.
Authors: Utkarsh1, Jain, Pradeep Kumar2, Ahmed, Bulbul3, bulbul@ce.ruet.ac.bd, Jothimani, Muralitharan, muralitharan.jothimani@amu.edu.et
Source: Advances in Civil Engineering; 1/20/2026, Vol. 2026, p1-22, 22p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190987899
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PubType: Academic Journal
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  Data: Machine Learning in Geotechnics: Predicting Unconfined Compressive Strength of Stabilized Organic Soils Using Hybrid Models.
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      – Type: doi
        Value: 10.1155/adce/4612199
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      – Code: eng
        Text: English
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        PageCount: 22
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      – TitleFull: Machine Learning in Geotechnics: Predicting Unconfined Compressive Strength of Stabilized Organic Soils Using Hybrid Models.
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            NameFull: Utkarsh
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            NameFull: Ahmed, Bulbul
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              Text: 1/20/2026
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              Y: 2026
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              Value: 2026
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