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In this study, the effects of various factors (weight fraction of the SiO2, Al2O3, Fe2O3, Na2O, K2O, CaO, MgO, Cl, SO3, and the Blaine of the cement particles) on the concrete compressive strength and also initial setting time have been investigated. Compressive strength and setting time tests have been carried out based on DIN standards in this study. Interactions of these factors have been obtained by the use of analysis of variance and regression equations of these factors have been obtained to predict the concrete compressive strength and initial setting time. Also, simple and applicable formulas with less than 6% absolute mean error have been developed using the genetic algorithm to predict these parameters. Finally, the effect of each factor has been investigated when other factors are in their low or high level.
CCS by the predictive equations for each experiment 704 B Abolpour et al Fig The calculated Error of the predicted IST by the predictive equations for each experiment Variation in Fe2O3 causes to vary CCS as a curve with a minimum at zero level when other factors are stabilized at low level and have a descending nonlinear curve when other factors are stabilized at high level Increasing of Fe2O3 decreases IST linearly in both cases, i.e other factors are stabilized in their high or low level This variation has been shown in Fig 12 Increasing of CaO causes a nonlinear decrease in the CCS when other factors are in their low level The CCS varies as a curve with a maximum at level 0.6 of the CaO, when other factors are in their high level Increasing of CaO causes a negligible linear increase in the IST in both cases when other factors are in their high or low level This behavior of the concrete has been shown in Fig 13 Fig 14 shows that increasing of SO3 causes an increase or decrease in the CCS linearly when other factors are in their high or low level, respectively This increment has a more complex effect on the IST Increasing of this factor causes a nonlinear decrease in the IST when other factors are in their high level This Figure shows that variation in the SO3 value has no important effect on the IST when other factors are in their low level As can be observed from Fig 15 variation in Blaine has no significant effect on the CCS and IST when the concrete composition is stabilized at their low level When composition of Fig The effects of SiO2 on the CCS and IST when other factors are in their low or high level Fig The effects of Al2O3 on the CCS and IST when other factors are in their low or high level Statistical analysis of the effective factors on the main properties of the concrete Fig Fig Fig 10 The effects of Na2O on the CCS and IST when other factors are in their low or high level The effects of Cl on the CCS and IST when other factors are in their low or high level The effects of MgO on the CCS and IST when other factors are in their low or high level 705 706 B Abolpour et al Fig 11 Fig 12 Fig 13 The effects of K2O on the CCS and IST when other factors are in their low or high level The effects of Fe2O3 on the CCS and IST when other factors are in their low or high level The effect of CaO on the CCS and IST when other factors are in their low or high level Statistical analysis of the effective factors on the main properties of the concrete Fig 14 Fig 15 Table xSiO2 xCaO xMgO xNa2 O xK2 O xSO3 xCl xBlaine The effects of Blaine on the CCS and IST when other factors are in their low or high level Level of other fixed factors xSiO2 xFe2 O3 The effects of SO3 on the CCS and IST when other factors are in their low or high level The effect of factors on the CCS and IST Considered factor xAl2 O3 707 + À + À + À + À + À + À + À + À + À xAl2 O3 xFe2 O3 xCaO xMgO xNa2 O xK2 O xSO3 xCl xBlaine + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À + À Effect on the CCS Effect on the IST Decrease Decrease Increase Decrease Decrease Complex Complex Decrease Decrease Decrease Increase Decrease Increase Increase Increase Decrease Increase Decrease Increase Complex Complex Complex Decrease Decrease Decrease Decrease Increase Increase Complex Complex Decrease Decrease Decrease Decrease Decrease Complex Decrease Decrease Complex Complex 708 the concrete is stabilized at high level, increasing of Blaine will increase CCS by an ascending curve and changes IST through a curve with a maximum at about level 0.2 The setting and hardening of cement are the result of chemical reactions between cement and water (i.e hydration) The hydration reactions starts directly after adding water to cement and in the first 30 a part of C3A and sulfate carrier is dissolved and results more strength in concrete This very fast process produces heat during the initial period of hydration C3A phase sets quickly with evolution of heat and enhances strength of the silicates Coarse cements with low specific surface area usually take longer times to set due to the sluggish hydration kinetics On the other hand, high content of C3A speeds up the reactions resulting in relatively short setting times Increasing the amount of C3A causes a significant increase in the CCS and also decreases the IST as Eqs (11) and (12) Conclusions In this study, the effects of various factors on the concrete compressive strength and also initial setting time have been investigated The effective factors are weight percent of the SiO2, Al2O3, Fe2O3, Na2O, K2O, CaO, MgO, Cl, SO3 of the raw materials and the Blaine of cement particles Interactions of these factors with probability of a 97.5% confidence have been obtained using analysis of variance Then the equations have been obtained through regression to predict the concrete compressive strength and initial setting time as function of the mentioned factors The mean of the calculated absolute Error for predicted values of CCS and IST was 1.92% and 4.3%, respectively for regression equations Attention to the coefficients of these regression equations shows that the quadruplet combinations of xSiO2 Á xMgO Á xSO3 Á xBlaine and xSiO2 Á xSO3 Á xK2 O Á xBlaine have the most positive and negative effect on the CCS, respectively Also the quadruplet combinations of xSiO2 Á xMgO Á xNa2 O Á xK2 O and xSiO2 Á xNa2 O Á x2K2 O have the most positive (increasing) and negative (reducing) effect on the IST of concrete, respectively Also, simple and applicable formulas have been developed using the genetic algorithm to predict these parameters The accuracy of these predictive equations is completely acceptable They have a less than 6% absolute mean error Finally the effect of each factor has been investigated when other factors are in their low or high level and summary of the results has been presented in Table Conflict of interest The authors have declared no conflict of interest Compliance with Ethics Requirements This article does not contain any studies with human or animal subjects References [1] Lee CC Fuzzy logic in control system: fuzzy logic controller Part I and Part II IEEE Trans Syst Man Cyber 1995;20:404–18 B Abolpour et al [2] Tanyildizi H, Qoskun A Fuzzy logic model for prediction of compressive strength of lightweight concrete made with scoria aggregate and fly ash International Earthquake Symposium Kocaeli; 2007 [3] Uyunoglu T, Unal O A new approach to determination of compressive strength of fly ash concrete using fuzzy logic J Sci Ind Res 2006;65:894–9 [4] Nataraja MC, Jayaram MA, Ravikumar CN A fuzzy – neuro model for normal concrete mix design Eng Lett 2006;13(2):98 [5] Tesfamariam S, Najjaran H Adaptive 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and also initial setting time have