Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques

Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and ver...

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Autores principales:	Ahmed M. Ebid, Kennedy C. Onyelowe, Emmanuel E. Arinze
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7292a1f4f4684580a490e95857cced07
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spelling	oai:doaj.org-article:7292a1f4f4684580a490e95857cced072021-11-22T01:09:38ZEstimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques2314-491210.1155/2021/3267018https://doaj.org/article/7292a1f4f4684580a490e95857cced072021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3267018https://doaj.org/toc/2314-4912Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise.Ahmed M. EbidKennedy C. OnyeloweEmmanuel E. ArinzeHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040ENJournal of Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
description	Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise.
format	article
author	Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze
author_facet	Ahmed M. Ebid Kennedy C. Onyelowe Emmanuel E. Arinze
author_sort	Ahmed M. Ebid
title	Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
title_short	Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
title_full	Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
title_fullStr	Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
title_full_unstemmed	Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques
title_sort	estimating the ultimate bearing capacity for strip footing near and within slopes using ai (gp, ann, and epr) techniques
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/7292a1f4f4684580a490e95857cced07
work_keys_str_mv	AT ahmedmebid estimatingtheultimatebearingcapacityforstripfootingnearandwithinslopesusingaigpannandeprtechniques AT kennedyconyelowe estimatingtheultimatebearingcapacityforstripfootingnearandwithinslopesusingaigpannandeprtechniques AT emmanuelearinze estimatingtheultimatebearingcapacityforstripfootingnearandwithinslopesusingaigpannandeprtechniques
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Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques

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