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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Format: | Article |
Language: | English |
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University of Tehran Press
2021-06-01
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Series: | Civil Engineering Infrastructures Journal |
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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. |
first_indexed | 2024-04-13T19:37:53Z |
format | Article |
id | doaj.art-7301a12336284348ac1b31bc5ac1f7a8 |
institution | Directory Open Access Journal |
issn | 2322-2093 2423-6691 |
language | English |
last_indexed | 2024-04-13T19:37:53Z |
publishDate | 2021-06-01 |
publisher | University of Tehran Press |
record_format | Article |
series | Civil Engineering Infrastructures Journal |
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 |