Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand

This paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of sk...

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Main Authors: Tammineni Gnananandarao, Vishwas Nandkishor Khatri, Rakesh Kumar Dutta
Format: Article
Language:English
Published: Universidad Nacional de Colombia 2020-09-01
Series:Ingeniería e Investigación
Subjects:
Online Access:https://revistas.unal.edu.co/index.php/ingeinv/article/view/83170
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spelling doaj-ef016a526609473289d7620ffa1e75172021-03-15T14:21:38ZengUniversidad Nacional de ColombiaIngeniería e Investigación0120-56092248-87232020-09-0140392110.15446/ing.investig.v40n3.8317066534Bearing capacity and settlement prediction of multi-edge skirted footings resting on sandTammineni Gnananandarao0Vishwas Nandkishor Khatri1Rakesh Kumar Dutta2https://orcid.org/0000-0002-4611-9950NIT HamirpurIIT DhanbadDepartment of Civil EngineeringNational Institute of TechnologyHamirpurHimachal PradeshIndiaThis paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of skirted to unskirted footing and the settlement reduction factor (SRF), the ratio of the difference in settlement of unskirted and skirted footing to the settlement of unskirted footing at a given pressure. The model equations for the prediction of the BCR and the SRF of the regular shaped footing were first developed using the available data collected from the literature. These equations were later modified to predict the BCR and the SRF of the multi-edge skirted footing, for which the data were generated by conducting a small scale laboratory test. The input parameters chosen to develop ANN models were the angle of internal friction (ϕ) and skirt depth (Ds) to the width of the footing (B) ratio for the prediction of the BCR; as for the SRF one additional input parameter was considered: normal stress (𝛔). The architecture for the developed ANN models was 2-2-1 and 3-2-1 for the BCR and the SRF, respectively. The R2 for the multi-edge skirted footings was in the range of 0,940-0,977 for the ANN model and 0,827-0,934 for the regression analysis. Similarly, the R2 for the SRF prediction might have been 0,913-0,985 for the ANN model and 0,739-0,932 for the regression analysis. It was revealed that the predicted BCR and SRF for the multi-edge skirted footings with the use of ANN is superior to MRA. Furthermore, the results of the sensitivity analysis indicate that both the BCR and the SRF of the multi-edge skirted footings are mostly affected by skirt depth, followed by the friction angle of the sand.https://revistas.unal.edu.co/index.php/ingeinv/article/view/83170square/circular skirted footingsmulti-edged skirted footingsbearing capacity ratiosettlement reduction factorartificial neural networksmultivariable regression analysis
collection DOAJ
language English
format Article
sources DOAJ
author Tammineni Gnananandarao
Vishwas Nandkishor Khatri
Rakesh Kumar Dutta
spellingShingle Tammineni Gnananandarao
Vishwas Nandkishor Khatri
Rakesh Kumar Dutta
Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
Ingeniería e Investigación
square/circular skirted footings
multi-edged skirted footings
bearing capacity ratio
settlement reduction factor
artificial neural networks
multivariable regression analysis
author_facet Tammineni Gnananandarao
Vishwas Nandkishor Khatri
Rakesh Kumar Dutta
author_sort Tammineni Gnananandarao
title Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
title_short Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
title_full Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
title_fullStr Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
title_full_unstemmed Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
title_sort bearing capacity and settlement prediction of multi-edge skirted footings resting on sand
publisher Universidad Nacional de Colombia
series Ingeniería e Investigación
issn 0120-5609
2248-8723
publishDate 2020-09-01
description This paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of skirted to unskirted footing and the settlement reduction factor (SRF), the ratio of the difference in settlement of unskirted and skirted footing to the settlement of unskirted footing at a given pressure. The model equations for the prediction of the BCR and the SRF of the regular shaped footing were first developed using the available data collected from the literature. These equations were later modified to predict the BCR and the SRF of the multi-edge skirted footing, for which the data were generated by conducting a small scale laboratory test. The input parameters chosen to develop ANN models were the angle of internal friction (ϕ) and skirt depth (Ds) to the width of the footing (B) ratio for the prediction of the BCR; as for the SRF one additional input parameter was considered: normal stress (𝛔). The architecture for the developed ANN models was 2-2-1 and 3-2-1 for the BCR and the SRF, respectively. The R2 for the multi-edge skirted footings was in the range of 0,940-0,977 for the ANN model and 0,827-0,934 for the regression analysis. Similarly, the R2 for the SRF prediction might have been 0,913-0,985 for the ANN model and 0,739-0,932 for the regression analysis. It was revealed that the predicted BCR and SRF for the multi-edge skirted footings with the use of ANN is superior to MRA. Furthermore, the results of the sensitivity analysis indicate that both the BCR and the SRF of the multi-edge skirted footings are mostly affected by skirt depth, followed by the friction angle of the sand.
topic square/circular skirted footings
multi-edged skirted footings
bearing capacity ratio
settlement reduction factor
artificial neural networks
multivariable regression analysis
url https://revistas.unal.edu.co/index.php/ingeinv/article/view/83170
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