A comparative study on prediction models for strength properties of LWA concrete using artificial neural network

Abstract In this study, Artiﬁcial Neural Network (ANN) model is constructed to predict the compressive strength, split tensile strength and flexural strength of lightweight aggregate concrete made of sintered fly ash aggregate. An empirical relationship between the compressive strength, s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nagarajan,Divyah, Rajagopal,Thenmozhi, Meyappan,Neelamegam
Lenguaje:	English
Publicado:	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile 2020
Materias:	Sintered Fly ash aggregate ANN algorithm variables regression
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000100103
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:scielo:S0718-915X2020000100103
record_format	dspace
spelling	oai:scielo:S0718-915X20200001001032020-05-26A comparative study on prediction models for strength properties of LWA concrete using artificial neural networkNagarajan,DivyahRajagopal,ThenmozhiMeyappan,Neelamegam Sintered Fly ash aggregate ANN algorithm variables regression Abstract In this study, Artiﬁcial Neural Network (ANN) model is constructed to predict the compressive strength, split tensile strength and flexural strength of lightweight aggregate concrete made of sintered fly ash aggregate. An empirical relationship between the compressive strength, split tensile strength, and flexural strength was developed and compared with that of experimental results. The models were formulated based on results obtained from laboratory experiments. The variables considered in the study are the quantity of cement and water-cement ratio. Feed forward neural network and Levenberg-Marquardt back propagation algorithm were used for training algorithm in ANN. Amongst the total data, approximately 70% of the data was considered for training, 15% for testing and the remaining 15% has been considered for validation. The developed models had more accuracy with minimum error and had a higher correlation with the correlation coefﬁcients of 0.916 and 0.955 were obtained for the training and testing data of compressive strength prediction, 0.949 and 0.937 respectively for split tensile strength prediction, 0.926 and 0.928 respectively for prediction of flexural strength. The models were compared with the experimental data’s, and the results were discussed.info:eu-repo/semantics/openAccessEscuela de Construcción Civil, Pontificia Universidad Católica de ChileRevista de la construcción v.19 n.1 20202020-04-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000100103en10.7764/rdlc.19.1.103-111
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Sintered Fly ash aggregate ANN algorithm variables regression
spellingShingle	Sintered Fly ash aggregate ANN algorithm variables regression Nagarajan,Divyah Rajagopal,Thenmozhi Meyappan,Neelamegam A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
description	Abstract In this study, Artiﬁcial Neural Network (ANN) model is constructed to predict the compressive strength, split tensile strength and flexural strength of lightweight aggregate concrete made of sintered fly ash aggregate. An empirical relationship between the compressive strength, split tensile strength, and flexural strength was developed and compared with that of experimental results. The models were formulated based on results obtained from laboratory experiments. The variables considered in the study are the quantity of cement and water-cement ratio. Feed forward neural network and Levenberg-Marquardt back propagation algorithm were used for training algorithm in ANN. Amongst the total data, approximately 70% of the data was considered for training, 15% for testing and the remaining 15% has been considered for validation. The developed models had more accuracy with minimum error and had a higher correlation with the correlation coefﬁcients of 0.916 and 0.955 were obtained for the training and testing data of compressive strength prediction, 0.949 and 0.937 respectively for split tensile strength prediction, 0.926 and 0.928 respectively for prediction of flexural strength. The models were compared with the experimental data’s, and the results were discussed.
author	Nagarajan,Divyah Rajagopal,Thenmozhi Meyappan,Neelamegam
author_facet	Nagarajan,Divyah Rajagopal,Thenmozhi Meyappan,Neelamegam
author_sort	Nagarajan,Divyah
title	A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
title_short	A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
title_full	A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
title_fullStr	A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
title_full_unstemmed	A comparative study on prediction models for strength properties of LWA concrete using artificial neural network
title_sort	comparative study on prediction models for strength properties of lwa concrete using artificial neural network
publisher	Escuela de Construcción Civil, Pontificia Universidad Católica de Chile
publishDate	2020
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-915X2020000100103
work_keys_str_mv	AT nagarajandivyah acomparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork AT rajagopalthenmozhi acomparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork AT meyappanneelamegam acomparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork AT nagarajandivyah comparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork AT rajagopalthenmozhi comparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork AT meyappanneelamegam comparativestudyonpredictionmodelsforstrengthpropertiesoflwaconcreteusingartificialneuralnetwork
_version_	1714206304821575680

A comparative study on prediction models for strength properties of LWA concrete using artificial neural network

Ejemplares similares