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Developing soft-computing regression model for predicting bearing capacity of eccentrically loaded footings on anisotropic clay

Authors :
Kongtawan Sangjinda
Rungkhun Banyong
Saif Alzabeebee
Suraparb Keawsawasvong
Source :
Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 68-75 (2023)
Publication Year :
2023
Publisher :
KeAi Communications Co. Ltd., 2023.

Abstract

In this investigation, the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model. The lower and upper bound finite element limit analysis (FELA) approaches are utilized to establish precise modeling and derive the numerical outcomes of a strip footing's bearing capacity. All analyses use effective automated adaptive meshes with three iteration stages to enhance the accuracy of the outcomes. The parametric analysis is performed to examine the influence of four dimensionless parameters which are taken into account in this study, namely the anisotropic strength ratio, the dimensionless eccentricity, the load inclination angle, and the adhesion factor to the bearing capacity factor. Furthermore, a new model has been proposed to predict the bearing capacity factor for the calculation of the undrained bearing capacity for footings resting on an anisotropic clay using an advanced data-driven method (MOGA-EPR). The new model takes into account the anisotropy, eccentricity, and inclination of the applied load and could be used with confidence in routine designs of shallow foundations in undrained conditions with the consideration of the anisotropic strengths of clays.

Details

Language :
English
ISSN :
26665441
Volume :
4
Issue :
68-75
Database :
Directory of Open Access Journals
Journal :
Artificial Intelligence in Geosciences
Publication Type :
Academic Journal
Accession number :
edsdoj.bd4d2d58bc5c45029f9c355433954ce0
Document Type :
article
Full Text :
https://doi.org/10.1016/j.aiig.2023.05.001