A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete

Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete prod...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	David Suescum-Morales, Lorenzo Salas-Morera, José Ramón Jiménez, Laura García-Hernández
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	construction and demolition waste recycled concrete aggregate compressive strength artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/e67c8a5f34d1494b9758c5692c73918d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e67c8a5f34d1494b9758c5692c73918d
record_format	dspace
spelling	oai:doaj.org-article:e67c8a5f34d1494b9758c5692c73918d2021-11-25T16:43:47ZA Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete10.3390/app1122110772076-3417https://doaj.org/article/e67c8a5f34d1494b9758c5692c73918d2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11077https://doaj.org/toc/2076-3417Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work.David Suescum-MoralesLorenzo Salas-MoreraJosé Ramón JiménezLaura García-HernándezMDPI AGarticleconstruction and demolition wasterecycled concrete aggregatecompressive strengthartificial neural networksTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11077, p 11077 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	construction and demolition waste recycled concrete aggregate compressive strength artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	construction and demolition waste recycled concrete aggregate compressive strength artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 David Suescum-Morales Lorenzo Salas-Morera José Ramón Jiménez Laura García-Hernández A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
description	Most regulations only allow the use of the coarse fraction of recycled concrete aggregate (RCA) for the manufacture of new concrete, although the heterogeneity of RCA makes it difficult to predict the compressive strength of concrete, which is an obstacle to the incorporation of RCA in concrete production. The compressive strength of recycled aggregate concrete is closely related to the dosage of its constituents. This article proposes a novel artificial neural network (ANN) model to predict the 28-day compressive strength of recycled aggregate concrete. The ANN used in this work has 11 neurons in the input layer: the mass of cement, fly ash, water, superplasticizer, fine natural aggregate, coarse natural or recycled aggregate, and their properties, such as: sand fineness modulus of sand, water absorption capacity, saturated surface dry density of the coarse aggregate mix and the maximum particle size. Two training methods were used for the ANN combining 15 and 20 hidden layers: Levenberg–Marquardt (LM) and Bayesian Regularization (BR). A database with 177 mixes selected from 15 studies incorporating RCA were selected, with the aim of having an underlying set of data heterogeneous enough to demonstrate the efficiency of the proposed approach, even when data are heterogeneous and noisy, which is the main finding of this work.
format	article
author	David Suescum-Morales Lorenzo Salas-Morera José Ramón Jiménez Laura García-Hernández
author_facet	David Suescum-Morales Lorenzo Salas-Morera José Ramón Jiménez Laura García-Hernández
author_sort	David Suescum-Morales
title	A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
title_short	A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
title_full	A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
title_fullStr	A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
title_full_unstemmed	A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete
title_sort	novel artificial neural network to predict compressive strength of recycled aggregate concrete
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e67c8a5f34d1494b9758c5692c73918d
work_keys_str_mv	AT davidsuescummorales anovelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT lorenzosalasmorera anovelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT joseramonjimenez anovelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT lauragarciahernandez anovelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT davidsuescummorales novelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT lorenzosalasmorera novelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT joseramonjimenez novelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete AT lauragarciahernandez novelartificialneuralnetworktopredictcompressivestrengthofrecycledaggregateconcrete
_version_	1718413050621460480

A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete

Ejemplares similares