Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs

In the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used...

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Main Authors: Atefeh Gholamzadeh Chitgar, Javad Berenjian
Format: Article
Language:English
Published: University of Tehran Press 2021-06-01
Series:Civil Engineering Infrastructures Journal
Subjects:
Online Access:https://ceij.ut.ac.ir/article_79229_33d0d38756b48e60aeaaa6af8573c827.pdf
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author Atefeh Gholamzadeh Chitgar
Javad Berenjian
author_facet Atefeh Gholamzadeh Chitgar
Javad Berenjian
author_sort Atefeh Gholamzadeh Chitgar
collection DOAJ
description In the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used in the networks are classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs.
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spelling doaj.art-7301a12336284348ac1b31bc5ac1f7a82022-12-22T02:32:58ZengUniversity of Tehran PressCivil Engineering Infrastructures Journal2322-20932423-66912021-06-01541597310.22059/ceij.2020.288257.161179229Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCsAtefeh Gholamzadeh Chitgar0Javad Berenjian1Department of Civil Engineering, Tabari University of Babol, Babol, IranFaculty of Civil Engineering, Babol University of Technology, Babol – IranIn the present study, Radial Basis Function (RBF) neural networks are applied to forecast the compressive strength and elastic modulus of Self-Compacting Concrete (SCC). To construct the models, different experimental specimens of diverse kinds of SCC are gathered from the literature. The data used in the networks are classified into two different sets of input parameters. The results revealed that the proposed RBF models can accurately forecast the properties of SCCs with low test error. Furthermore, a comparison between models with two different sets of inputs proves that the selected parameters as input variables, straightly impress the precision of the networks, in the prediction of the intended outputs.https://ceij.ut.ac.ir/article_79229_33d0d38756b48e60aeaaa6af8573c827.pdfparametersrbf artificial neural networksself-compacting concretetest mse
spellingShingle Atefeh Gholamzadeh Chitgar
Javad Berenjian
Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
Civil Engineering Infrastructures Journal
parameters
rbf artificial neural networks
self-compacting concrete
test mse
title Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
title_full Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
title_fullStr Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
title_full_unstemmed Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
title_short Performance Evaluation of RBF Networks with Various Variables to Forecast the Properties of SCCs
title_sort performance evaluation of rbf networks with various variables to forecast the properties of sccs
topic parameters
rbf artificial neural networks
self-compacting concrete
test mse
url https://ceij.ut.ac.ir/article_79229_33d0d38756b48e60aeaaa6af8573c827.pdf
work_keys_str_mv AT atefehgholamzadehchitgar performanceevaluationofrbfnetworkswithvariousvariablestoforecastthepropertiesofsccs
AT javadberenjian performanceevaluationofrbfnetworkswithvariousvariablestoforecastthepropertiesofsccs