Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

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 beneath shallow foundations. The inputs of the model are width of footing (<i>B</i>), depth of footing (<i&...

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Autores principales:	Mahmood Ahmad, Feezan Ahmad, Piotr Wróblewski, Ramez A. Al-Mansob, Piotr Olczak, Paweł Kamiński, Muhammad Safdar, Partab Rai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cohesionless soil machine learning Gaussian process regression shallow foundation ultimate bearing capacity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9500e13ff9ec412f982811748e6838e8
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spelling	oai:doaj.org-article:9500e13ff9ec412f982811748e6838e82021-11-11T15:20:33ZPrediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach10.3390/app1121103172076-3417https://doaj.org/article/9500e13ff9ec412f982811748e6838e82021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10317https://doaj.org/toc/2076-3417This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (<i>B</i>), depth of footing (<i>D</i>), footing geometry (<i>L</i>/<i>B</i>), unit weight of sand (<i>γ</i>), and internal friction angle (<i>ϕ</i>). 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 (<i>q<sub>u</sub></i>). This study shows that the developed GPR is a robust model for the <i>q<sub>u</sub></i> prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.Mahmood AhmadFeezan AhmadPiotr WróblewskiRamez A. Al-MansobPiotr OlczakPaweł KamińskiMuhammad SafdarPartab RaiMDPI AGarticlecohesionless soilmachine learningGaussian process regressionshallow foundationultimate bearing capacityTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10317, p 10317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cohesionless soil machine learning Gaussian process regression shallow foundation ultimate bearing capacity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	cohesionless soil machine learning Gaussian process regression shallow foundation ultimate bearing capacity Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Mahmood Ahmad Feezan Ahmad Piotr Wróblewski Ramez A. Al-Mansob Piotr Olczak Paweł Kamiński Muhammad Safdar Partab Rai Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
description	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 beneath shallow foundations. The inputs of the model are width of footing (<i>B</i>), depth of footing (<i>D</i>), footing geometry (<i>L</i>/<i>B</i>), unit weight of sand (<i>γ</i>), and internal friction angle (<i>ϕ</i>). 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 (<i>q<sub>u</sub></i>). This study shows that the developed GPR is a robust model for the <i>q<sub>u</sub></i> prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.
format	article
author	Mahmood Ahmad Feezan Ahmad Piotr Wróblewski Ramez A. Al-Mansob Piotr Olczak Paweł Kamiński Muhammad Safdar Partab Rai
author_facet	Mahmood Ahmad Feezan Ahmad Piotr Wróblewski Ramez A. Al-Mansob Piotr Olczak Paweł Kamiński Muhammad Safdar Partab Rai
author_sort	Mahmood Ahmad
title	Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
title_short	Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
title_full	Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
title_fullStr	Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
title_full_unstemmed	Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach
title_sort	prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: a gaussian process regression approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9500e13ff9ec412f982811748e6838e8
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Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

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