2013
DOI: 10.1016/j.compositesb.2012.08.015
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Improving the workability and rheological properties of Engineered Cementitious Composites using factorial experimental design

Abstract: In the development of Engineered Cementitious Composites (ECC), micromechanics-based design theory is adopted to properly select the matrix constituents, fiber, and fiber-matrix interface properties to exhibit strain hardening and multiple cracking behaviors. Despite the micromechanics design constraints, practical applications show that the workability and rheological properties of matrix can affect the fiber dispersion uniformity, which have also direct concerns on composite mechanical properties. For this r… Show more

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Cited by 75 publications
(26 citation statements)
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References 31 publications
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“…Also, Maruthachalam et al [19] and Sahmaran et al [27] used statistical tools to test the properties of fiber reinforced high performance concrete (FRHPC), and developed regression models using Analysis of Variance which adequately fit the experimental data with p<0.05.…”
Section: Predictive Empirical Modelsmentioning
confidence: 99%
“…Also, Maruthachalam et al [19] and Sahmaran et al [27] used statistical tools to test the properties of fiber reinforced high performance concrete (FRHPC), and developed regression models using Analysis of Variance which adequately fit the experimental data with p<0.05.…”
Section: Predictive Empirical Modelsmentioning
confidence: 99%
“…The F value in Table II provides an information of the degree of contribution of the independent parameters to the measured dependent parameter (test results). If the F is high, the contribution of the factors to that particular response is high [8,9]. This analysis was carried out for a level of confidence of 90% i.e., for a level of significance of 10%.…”
Section: Analysis Of Variance (Anova)mentioning
confidence: 99%
“…Also, the effect of the independent variable, RI, on each of the dependent variables, FS, IIEA and UIEA are evaluated in as the significant models [14]. For Models 1 and 2 representing CS and TS respectively, the R 2 of 0.976 and 0.847 suggest that both the independent variables, CP and RI jointly predict the CS and TS of PL-FRCC and explains 97.6 and 84.7 per cent of the variations in CS and TS respectively.…”
Section: B Regression Modelsmentioning
confidence: 99%
“…The regression model proposed in the present study will be helpful to predict the strength of FRCC, and accordingly vary the input parameters or the matrix design. In order to arrive at the best set of predictors of a particular dependent variable [14,47], the objective of the present study is to develop linear regression equations using SPSS [48] to evaluate the effect of fiber percentage and/or curing days on the mechanical strength of FRCC.…”
Section: Research Significancementioning
confidence: 99%
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