Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand

The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input v...

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Autores principales:	Safeena Nazeer, Rakesh Dutta
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	bearing capacity e-shaped footing error metrics multi-layer perceptron multi-linear regression model equation Technology T
Acceso en línea:	https://doaj.org/article/38aec2a805e443dd963d53e8c8527ad3
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spelling	oai:doaj.org-article:38aec2a805e443dd963d53e8c8527ad32021-12-03T15:12:29ZApplication of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand2588-287210.22115/scce.2021.303113.1360https://doaj.org/article/38aec2a805e443dd963d53e8c8527ad32021-10-01T00:00:00Zhttp://www.jsoftcivil.com/article_140630_af9890a287c38ccd66bbc5364d3c8e29.pdfhttps://doaj.org/toc/2588-2872The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input variables used in the developed neural network model were the bearing capacity of square footing, thickness ratio, friction angle ratio and the output were the bearing capacity of E-shaped footing on layered sand. Multiple layer perceptron (MLP) and multiple linear regression (MLR) prediction models were used for the determination of error metrics and the ultimate bearing capacity of E-shaped footing resting on layered sand. Finally, for the ANN model development, a model equation was developed with the assistance of weights and biases, based on the MLP and MLR model using open-source WEKA and Anaconda software respectively. Sensitivity analysis has been performed on the data sets which correlates the various input variables with the output variable of both the models. The coefficient of determination (R2) comes out to be 0.99 and 0.98 for the MLP and MLR models respectively indicating that both the models were able to predict the bearing capacity for the E shaped footing with acceptable accuracy.Safeena NazeerRakesh DuttaPouyan Pressarticlebearing capacitye-shaped footingerror metricsmulti-layer perceptronmulti-linear regressionmodel equationTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 4, Pp 74-89 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bearing capacity e-shaped footing error metrics multi-layer perceptron multi-linear regression model equation Technology T
spellingShingle	bearing capacity e-shaped footing error metrics multi-layer perceptron multi-linear regression model equation Technology T Safeena Nazeer Rakesh Dutta Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
description	The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input variables used in the developed neural network model were the bearing capacity of square footing, thickness ratio, friction angle ratio and the output were the bearing capacity of E-shaped footing on layered sand. Multiple layer perceptron (MLP) and multiple linear regression (MLR) prediction models were used for the determination of error metrics and the ultimate bearing capacity of E-shaped footing resting on layered sand. Finally, for the ANN model development, a model equation was developed with the assistance of weights and biases, based on the MLP and MLR model using open-source WEKA and Anaconda software respectively. Sensitivity analysis has been performed on the data sets which correlates the various input variables with the output variable of both the models. The coefficient of determination (R2) comes out to be 0.99 and 0.98 for the MLP and MLR models respectively indicating that both the models were able to predict the bearing capacity for the E shaped footing with acceptable accuracy.
format	article
author	Safeena Nazeer Rakesh Dutta
author_facet	Safeena Nazeer Rakesh Dutta
author_sort	Safeena Nazeer
title	Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
title_short	Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
title_full	Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
title_fullStr	Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
title_full_unstemmed	Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand
title_sort	application of machine learning techniques in predicting the bearing capacity of e-shaped footing on layered sand
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/38aec2a805e443dd963d53e8c8527ad3
work_keys_str_mv	AT safeenanazeer applicationofmachinelearningtechniquesinpredictingthebearingcapacityofeshapedfootingonlayeredsand AT rakeshdutta applicationofmachinelearningtechniquesinpredictingthebearingcapacityofeshapedfootingonlayeredsand
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Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand

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