Potential for utilising concrete mix properties to predict strength at different ages

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5 Citations (Scopus)

Abstract

In this study, the potential for utilising properties of concrete as a powerful tool to predict its compressive strength at different ages has been realised. Novel mathematical models were proposed and developed using multiple non-linear regression equations to predict the concrete strength. The variables used in the prediction models, such as the mix proportion elements, were statistically analysed. According to the analysis, the models provided a good estimation of compressive strength and yielded good correlations with the data used in this study. The correlation coefficients were 0.995 for the prediction of 7- and 28-day compressive strength. Moreover, the proposed models proved to be a significant tool in predicting the compressive strength of different concretes despite variations in the data used to validate the model.

Original languageEnglish
Pages (from-to)2831-2838
Number of pages8
JournalJournal of Applied Sciences
Volume10
Issue number22
Publication statusPublished - 2010

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Concrete mixtures
Compressive strength
Concretes
Mathematical models

Keywords

  • Compressive strength
  • Concrete
  • Mix proportion
  • Prediction
  • Regression model
  • Statistical analysis

ASJC Scopus subject areas

  • General

Cite this

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title = "Potential for utilising concrete mix properties to predict strength at different ages",
abstract = "In this study, the potential for utilising properties of concrete as a powerful tool to predict its compressive strength at different ages has been realised. Novel mathematical models were proposed and developed using multiple non-linear regression equations to predict the concrete strength. The variables used in the prediction models, such as the mix proportion elements, were statistically analysed. According to the analysis, the models provided a good estimation of compressive strength and yielded good correlations with the data used in this study. The correlation coefficients were 0.995 for the prediction of 7- and 28-day compressive strength. Moreover, the proposed models proved to be a significant tool in predicting the compressive strength of different concretes despite variations in the data used to validate the model.",
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AU - Mohd. Zain, Muhammad Fauzi

AU - Abd, Suhad M.

AU - Hamid, Roszilah

AU - Jamil, Maslina

PY - 2010

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N2 - In this study, the potential for utilising properties of concrete as a powerful tool to predict its compressive strength at different ages has been realised. Novel mathematical models were proposed and developed using multiple non-linear regression equations to predict the concrete strength. The variables used in the prediction models, such as the mix proportion elements, were statistically analysed. According to the analysis, the models provided a good estimation of compressive strength and yielded good correlations with the data used in this study. The correlation coefficients were 0.995 for the prediction of 7- and 28-day compressive strength. Moreover, the proposed models proved to be a significant tool in predicting the compressive strength of different concretes despite variations in the data used to validate the model.

AB - In this study, the potential for utilising properties of concrete as a powerful tool to predict its compressive strength at different ages has been realised. Novel mathematical models were proposed and developed using multiple non-linear regression equations to predict the concrete strength. The variables used in the prediction models, such as the mix proportion elements, were statistically analysed. According to the analysis, the models provided a good estimation of compressive strength and yielded good correlations with the data used in this study. The correlation coefficients were 0.995 for the prediction of 7- and 28-day compressive strength. Moreover, the proposed models proved to be a significant tool in predicting the compressive strength of different concretes despite variations in the data used to validate the model.

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KW - Regression model

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