Neural networks and principle component analysis approaches to predict pile capacity in sand

Neural networks and principle component analysis approaches to predict pile capacity in sand

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Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development o...

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Main Authors: Benali A, Nechnech A, Boukhatem B, Hussein M N, Karry M
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201814902025
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spelling doaj-0b6751d3fb494ff3981b08089c2734d52021-03-02T05:25:35ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-011490202510.1051/matecconf/201814902025matecconf_cmss2018_02025Neural networks and principle component analysis approaches to predict pile capacity in sandBenali ANechnech ABoukhatem BHussein M NKarry MDetermination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.https://doi.org/10.1051/matecconf/201814902025
collection DOAJ
language English
format Article
sources DOAJ
author Benali A
Nechnech A
Boukhatem B
Hussein M N
Karry M
spellingShingle Benali A
Nechnech A
Boukhatem B
Hussein M N
Karry M
Neural networks and principle component analysis approaches to predict pile capacity in sand
MATEC Web of Conferences
author_facet Benali A
Nechnech A
Boukhatem B
Hussein M N
Karry M
author_sort Benali A
title Neural networks and principle component analysis approaches to predict pile capacity in sand
title_short Neural networks and principle component analysis approaches to predict pile capacity in sand
title_full Neural networks and principle component analysis approaches to predict pile capacity in sand
title_fullStr Neural networks and principle component analysis approaches to predict pile capacity in sand
title_full_unstemmed Neural networks and principle component analysis approaches to predict pile capacity in sand
title_sort neural networks and principle component analysis approaches to predict pile capacity in sand
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2018-01-01
description Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.
url https://doi.org/10.1051/matecconf/201814902025
work_keys_str_mv AT benalia neuralnetworksandprinciplecomponentanalysisapproachestopredictpilecapacityinsand
AT nechnecha neuralnetworksandprinciplecomponentanalysisapproachestopredictpilecapacityinsand
AT boukhatemb neuralnetworksandprinciplecomponentanalysisapproachestopredictpilecapacityinsand
AT husseinmn neuralnetworksandprinciplecomponentanalysisapproachestopredictpilecapacityinsand
AT karrym neuralnetworksandprinciplecomponentanalysisapproachestopredictpilecapacityinsand
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