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Cited by 77Geotechnical and construction materials studiesGeotechnical Engineering and Soil StabilizationMaterials Engineering and Processing

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Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste

Ho, Lanh Si, Tran, Van Quan·2022·Journal of Cleaner Production
DOI 10.1016/j.jclepro.2022.133587
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Metadata

Authors
Ho, L. S., & Tran, V. Q.
Affiliations
University Of Transport Technology (VN)
Corresponding author
Tran, Van Quan
ORCID
Venue
Journal of Cleaner Production
Publisher
Elsevier BV
Published
2022-10
Volume
370
Pages
133587
ISSN
0959-6526 (ISSN-L)
DOI
10.1016/j.jclepro.2022.133587
OpenAlex
W4293203128
Cited by
77
References
2
Citations by year
2026: 23 · 2025: 17 · 2024: 23 · 2023: 14
FWCI
5.91
Type
journal article
Language
en
Open access
closed
Licence
Locations
Journal of Cleaner Production (publishedVersion)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical and construction materials studies
  • Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical Engineering and Soil Stabilization
  • Physical Sciences › Engineering › Building and Construction › Materials Engineering and Processing
Keywords
California bearing ratio, stabilized soil, industrial waste, cement stabilization, machine learning prediction, random forest, gradient boosting, hybrid model, feature importance, plasticity index, cement content, Atterberg limits, optimum moisture content, maximum dry density

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CoChat. (2026). Citation check: Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste. https://app.cochat.ai/papers/10.1016/j.jclepro.2022.133587
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Ho, L. S., & Tran, V. Q. (2022). Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2022.133587