No-slump concrete is a type of concrete with slump between 0-25 mm which has wide application in the prefabricated concrete industries. The first section of this paper is dealt with introducing the ACI 211. 3 mix design method and based on this method, 32 no-slump concrete mixtures were made in the laboratory. A number of theses mixtures were made by using of silica fume and pozzolan as supplementary cementing materials and also siliceous powder as filler. Of theses mixtures, 4 mixes were elected as optimized ones. Moreover, an adaptive nero-fuzzy system called ANFIS, were utilized to predict the 28 days compressive strength of no-slump concrete. The results of the study showed that ACI 211.3 based on the utilized domestic aggregates, has more water demand and it is recommendable to reduce the water content. In addition, by using the supplementary cementing materials like silica fume and pozzolans and neutral fillers, the consumption of cement could be reduced. Also, the results of paper indicated that the proposed ANFIS system, can predict the strength of no-slump with higher accuracy. The importance of this finding is laid on robustness of the system because of utilizing the human reasoning scheme and learning capabilities.
Najimi, M., Sobhani, J., & Pourkhorshidi, A. R. (2010). Design and Optimization of No-slump Concrete and Prediction of Compre -ssive Strength with Adaptive Nero-Fuzzy Systems. Concrete Research, 3(1), 21-31.
MLA
Meysam Najimi; Jafar Sobhani; Ali Reza Pourkhorshidi. "Design and Optimization of No-slump Concrete and Prediction of Compre -ssive Strength with Adaptive Nero-Fuzzy Systems". Concrete Research, 3, 1, 2010, 21-31.
HARVARD
Najimi, M., Sobhani, J., Pourkhorshidi, A. R. (2010). 'Design and Optimization of No-slump Concrete and Prediction of Compre -ssive Strength with Adaptive Nero-Fuzzy Systems', Concrete Research, 3(1), pp. 21-31.
VANCOUVER
Najimi, M., Sobhani, J., Pourkhorshidi, A. R. Design and Optimization of No-slump Concrete and Prediction of Compre -ssive Strength with Adaptive Nero-Fuzzy Systems. Concrete Research, 2010; 3(1): 21-31.
)