Experimental evaluation and modeling of the compressive strength of concretes with various strength classes of cements
Abstract
This study aimed to
propose a prediction model for estimation of strength of concretes with various
cements and mixture proportions. The strength of the samples produced with
three different types of cement at different rates of water-to-cement ratios
and cement richness were investigated experimentally and evaluated statistically.
Three type of cement possessing 28-day strengths of 32.5, 42.5, and 52.5 MPa
was used in the production of concretes. The concretes were produced at cement richness
values of 300, 400, and 500 kg/m3 and w/c rates at changing levels within
the interval of between 0.3 and 0.6. By this way, combined influences of cement
strength, amount of cement and w/c ratio was experimentally investigated. Totally
36 mixes were cast then the compressive strength values were examined after specified
moist curing periods (7 and 28 day). A statistical study were conducted on the
experimental results and the significances of the cement strength, w/c values
and amount of cement on the compressive strength of the concretes were assessed.
Another crucial focus of the current paper is to generate an explicit
expression to predict the compressive strength of the concretes tackled with
the current study. To derive an explicit formula for estimation, a soft
computing method called gene expression programming (GEP) was benefited. The
GEP model was also compared with a less complicated estimation model developed
by multi linear regression method. The results revealed that compressive
strength of the samples were significantly influenced by cement type and
aggregate-to-cement ratio. The proposed GEP model indicated a high correlation
between experimental and predicted values.
Keywords
Compressive strength,Gene Expression Programming,Multiple Linear Regression,Statistical evaluation
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