Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks

The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand...

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Vydané v:Journal of soft computing in civil engineering Ročník 2; číslo 4; s. 34 - 46
Hlavní autori: Rakesh Dutta, Radha Rani, Tammineni Gnananandarao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Pouyan Press 01.10.2018
Predmet:
artificial neural network
different regular shaped skirted footings
feed forward backpropagation algorithm
multiple regression analysis
ultimate bearing capacity
ISSN:2588-2872, 2588-2872
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Abstract The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand, while the output was the ultimate bearing capacity. The artificial neural network algorithm uses a back propagation model. The training of artificial neural network model has been conducted and the weights were obtained which described the relationship between the input parameters and output ultimate bearing capacity. Further, the sensitivity analysis has been performed and the parameters affecting the ultimate bearing capacity of different regular shaped skirted footing resting on sand were identified. The study shows that the prediction accuracy of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.
AbstractList The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand, while the output was the ultimate bearing capacity. The artificial neural network algorithm uses a back propagation model. The training of artificial neural network model has been conducted and the weights were obtained which described the relationship between the input parameters and output ultimate bearing capacity. Further, the sensitivity analysis has been performed and the parameters affecting the ultimate bearing capacity of different regular shaped skirted footing resting on sand were identified. The study shows that the prediction accuracy of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.
Author Tammineni Gnananandarao
Radha Rani
Rakesh Dutta
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  organization: Department of Civil Engineering, NIT Hamirpur, Himachal Pradesh, India
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Snippet The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network. The...
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SubjectTerms artificial neural network
different regular shaped skirted footings
feed forward backpropagation algorithm
multiple regression analysis
ultimate bearing capacity
Title Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks
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