Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions

Concrete as a building material is classified as either normal or high strength based on its compressive strength. The compressive strength of conventional concrete ranges somewhere around 20–40 MPa. The incorporation of high-performance nanomaterials, such as carbon nanotubes (CNT), into the concre...

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Autores principales:	Nzar Shakr Piro, Ahmed Salih, Samir M. Hamad, Rawaz Kurda
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Conventional concrete Carbon nanotube Statistical analysis Modeling Sensitivity Mining engineering. Metallurgy TN1-997
Acceso en línea:	https://doaj.org/article/1afab7c8954d4df1bf7f40055c0dd514
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spelling	oai:doaj.org-article:1afab7c8954d4df1bf7f40055c0dd5142021-12-04T04:34:26ZComprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions2238-785410.1016/j.jmrt.2021.11.028https://doaj.org/article/1afab7c8954d4df1bf7f40055c0dd5142021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2238785421013077https://doaj.org/toc/2238-7854Concrete as a building material is classified as either normal or high strength based on its compressive strength. The compressive strength of conventional concrete ranges somewhere around 20–40 MPa. The incorporation of high-performance nanomaterials, such as carbon nanotubes (CNT), into the concrete mix, is gaining popularity to produce multifunctional composite materials with improved mechanical, physical, and electrical properties. However, the compressive strength of normal concrete (NC) increased with the addition of CNT to the mix design. Therefore, a reliable mathematical model is required to estimate the amount of CNT to gain the necessary compressive strength. In this research, five different models were proposed to forecast the compressive strength of conventional concrete modified with carbon nanotubes, including the artificial neural network model (ANN), M5P tree model, nonlinear regression model (NLR), multilinear regression model (MLR), and linear regression model (LR). For this purpose, 282 data were collected from the literature review to examine and develop the models. During the model development, the most powerful parameters influencing concrete's compressive strength were found, i.e., curing time ranged from 1 to 180 days, cement varied between 250 and 475 kg/m3, water to binder ratio ranged from 0.4–0.87, coarse aggregate 498–1466.8 kg/m3, fine aggregate 175.5–1285 kg/m3, and carbon nanotube varied between (0–10%). Based on statistical assessment parameters such as coefficient of determination R2, mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and objective (OBJ), the ANN model execute better performance in predicting the compressive strength of NC modified with CNT.Nzar Shakr PiroAhmed SalihSamir M. HamadRawaz KurdaElsevierarticleConventional concreteCarbon nanotubeStatistical analysisModelingSensitivityMining engineering. MetallurgyTN1-997ENJournal of Materials Research and Technology, Vol 15, Iss , Pp 6506-6527 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Conventional concrete Carbon nanotube Statistical analysis Modeling Sensitivity Mining engineering. Metallurgy TN1-997
spellingShingle	Conventional concrete Carbon nanotube Statistical analysis Modeling Sensitivity Mining engineering. Metallurgy TN1-997 Nzar Shakr Piro Ahmed Salih Samir M. Hamad Rawaz Kurda Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
description	Concrete as a building material is classified as either normal or high strength based on its compressive strength. The compressive strength of conventional concrete ranges somewhere around 20–40 MPa. The incorporation of high-performance nanomaterials, such as carbon nanotubes (CNT), into the concrete mix, is gaining popularity to produce multifunctional composite materials with improved mechanical, physical, and electrical properties. However, the compressive strength of normal concrete (NC) increased with the addition of CNT to the mix design. Therefore, a reliable mathematical model is required to estimate the amount of CNT to gain the necessary compressive strength. In this research, five different models were proposed to forecast the compressive strength of conventional concrete modified with carbon nanotubes, including the artificial neural network model (ANN), M5P tree model, nonlinear regression model (NLR), multilinear regression model (MLR), and linear regression model (LR). For this purpose, 282 data were collected from the literature review to examine and develop the models. During the model development, the most powerful parameters influencing concrete's compressive strength were found, i.e., curing time ranged from 1 to 180 days, cement varied between 250 and 475 kg/m3, water to binder ratio ranged from 0.4–0.87, coarse aggregate 498–1466.8 kg/m3, fine aggregate 175.5–1285 kg/m3, and carbon nanotube varied between (0–10%). Based on statistical assessment parameters such as coefficient of determination R2, mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and objective (OBJ), the ANN model execute better performance in predicting the compressive strength of NC modified with CNT.
format	article
author	Nzar Shakr Piro Ahmed Salih Samir M. Hamad Rawaz Kurda
author_facet	Nzar Shakr Piro Ahmed Salih Samir M. Hamad Rawaz Kurda
author_sort	Nzar Shakr Piro
title	Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
title_short	Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
title_full	Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
title_fullStr	Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
title_full_unstemmed	Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
title_sort	comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/1afab7c8954d4df1bf7f40055c0dd514
work_keys_str_mv	AT nzarshakrpiro comprehensivemultiscaletechniquestoestimatethecompressivestrengthofconcreteincorporatedwithcarbonnanotubesatvariouscuringtimesandmixproportions AT ahmedsalih comprehensivemultiscaletechniquestoestimatethecompressivestrengthofconcreteincorporatedwithcarbonnanotubesatvariouscuringtimesandmixproportions AT samirmhamad comprehensivemultiscaletechniquestoestimatethecompressivestrengthofconcreteincorporatedwithcarbonnanotubesatvariouscuringtimesandmixproportions AT rawazkurda comprehensivemultiscaletechniquestoestimatethecompressivestrengthofconcreteincorporatedwithcarbonnanotubesatvariouscuringtimesandmixproportions
_version_	1718372955656814592

Comprehensive multiscale techniques to estimate the compressive strength of concrete incorporated with carbon nanotubes at various curing times and mix proportions

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