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Cited by 42Geotechnical and construction materials studiesGeotechnical Engineering and Soil StabilizationGeotechnical Engineering and Soil Mechanics

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The application of deep neural networks for the prediction of California Bearing Ratio of road subgrade soil

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Othman, Kareem, Abdelwahab, Hassan·2023·Ain Shams Engineering Journal
DOI 10.1016/j.asej.2022.101988
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Abstract

California Bearing Ratio (CBR) of the subgrade soil is one of the essential values for the design and construction of the asphalt pavement of highway projects. However, the estimation of the CBR value through laboratory tests is time-consuming and labor-intensive. Thus, this study focuses on providing artificial neural network (ANN) prediction models that can be efficiently used for the prediction of the CBR value of the subgrade soil in Egypt from the grain size distribution, Atterberg limits, and compaction parameters. 240 ANNs with different hyperparameters are investigated in order to optimize the hyperparameters so that the final chosen ANN can provide accurate results. The analysis shows that the deep neural networks outperform the shallow ANNs. Finally, comparing the performance of the ANNs with the traditional multiple linear regression (MLR) shows that ANNs outperform the MLR models as the ANNs have much better performance and can generate highly accurate predictions.

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Metadata

Authors
Othman, K., & Abdelwahab, H.
Affiliations
Cairo University (EG)
Corresponding author
Othman, Kareem
ORCID
Othman, Kareem
Venue
Ain Shams Engineering Journal
Publisher
Elsevier BV
Published
2023-07
Volume
14
Issue
7
Pages
101988
ISSN
2090-4479 (ISSN-L)
DOI
10.1016/j.asej.2022.101988
OpenAlex
W4298325774
Cited by
42
References
2
Citations by year
2026: 11 · 2025: 11 · 2024: 11 · 2023: 9
FWCI
3.39
Type
journal article
Language
en
Open access
Licence
Locations
  • Ain Shams Engineering Journal (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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 › Civil and Structural Engineering › Geotechnical Engineering and Soil Mechanics
Keywords
California bearing ratio, subgrade soil, deep neural network, artificial neural networks, flexible pavement design, grain size distribution, Atterberg limits, compaction parameters, hyperparameter optimization, multiple linear regression comparison, Egyptian soils

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CoChat. (2026). Citation check: The application of deep neural networks for the prediction of California Bearing Ratio of road subgrade soil. https://app.cochat.ai/papers/10.1016/j.asej.2022.101988
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Othman, K., & Abdelwahab, H. (2023). The application of deep neural networks for the prediction of California Bearing Ratio of road subgrade soil. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2022.101988