- Title
- Enhancing ultimate bearing capacity prediction of cohesionless soils beneath shallow foundations with grey box and hybrid AI models
- Creator
- Kiany, Katayoon; Baghbani, Abolfazl; Abuel-Naga, Hossam; Baghbani, Hasan; Arabani, Mahyar; Shalchian, Mohammad
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198343
- Identifier
- vital:19040
- Identifier
-
https://doi.org/10.3390/a16100456
- Identifier
- ISSN:1999-4893 (ISSN)
- Abstract
- 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 (
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Relation
- Algorithms Vol. 16, no. 10 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; 49 Mathematical sciences; Artificial intelligence; Classification and regression random forest; Cohesionless soils; Genetic algorithm-emotional neural network; Genetic programming; Ultimate bearing capacity
- Full Text
- Reviewed
- Funder
- This research is supported by an Australian Government Research Training Program (RTP) Scholarship for the second author (A.B.).
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