Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: a gaussian process regression approach

Ahmad, Mahmood and Ahmad, Feezan and Wróblewski, Piotr and Al-Mansob, Ramez A. and Olczak, Piotr and Paweł, Kamiński and Safdar, Muhammad and Rai, Partab (2021) Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: a gaussian process regression approach. Applied Sciences, 11 (21). E-ISSN 2076-3417

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Abstract

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils be‐ neath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches 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 UBC (qu). This study shows that the developed GPR is 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 pa‐ rameter.

Item Type:	Article (Journal)
Additional Information:	10283/93594
Uncontrolled Keywords:	cohesionless soil; machine learning; Gaussian process regression; shallow foundation; ultimate bearing capacity
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Civil Engineering
Depositing User:	Dr. Ramez Al-Mansob
Date Deposited:	09 Nov 2021 12:14
Last Modified:	25 Nov 2021 11:56
URI:	http://irep.iium.edu.my/id/eprint/93594

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