Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI Models Article Swipe
Katayoon Kiany
,
Abolfazl Baghbani
,
Hossam Abuel-Naga
,
Hasan Baghbani
,
Mahyar Arabani
,
Mohammad Mahdi Shalchian
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/a16100456
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/a16100456
This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. For the first time, two grey-box AI models, GP and CART, and one hybrid AI model, GA-ENN, were used in the literature to predict UBC. The inputs of the model are the width of footing (B), depth of footing (D), footing geometry (ratio of length to width, L/B), unit weight of sand (γd or γ′), and internal friction angle (ϕ). The results of the present model were compared with those obtained via two theoretical approaches and one AI approach reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of qu. This study shows that the developed AI models are a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter. The findings showed that the width and depth of the foundation and unit weight of soil (γd or γ′) played the most significant roles, while the internal friction angle and L/B showed less importance in predicting qu.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/a16100456
- https://www.mdpi.com/1999-4893/16/10/456/pdf?version=1695636444
- OA Status
- gold
- Cited By
- 6
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387057051
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387057051Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/a16100456Digital Object Identifier
- Title
-
Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-25Full publication date if available
- Authors
-
Katayoon Kiany, Abolfazl Baghbani, Hossam Abuel-Naga, Hasan Baghbani, Mahyar Arabani, Mohammad Mahdi ShalchianList of authors in order
- Landing page
-
https://doi.org/10.3390/a16100456Publisher landing page
- PDF URL
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https://www.mdpi.com/1999-4893/16/10/456/pdf?version=1695636444Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1999-4893/16/10/456/pdf?version=1695636444Direct OA link when available
- Concepts
-
Bearing capacity, Geotechnical engineering, Genetic programming, Soil water, Cart, Artificial neural network, Mathematics, Genetic algorithm, Geology, Computer science, Soil science, Artificial intelligence, Engineering, Mathematical optimization, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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76Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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