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The purpose of this paper is to focus on compressive strength modelling of cementitious mixtures like mortar and Roller-compacted concrete (RCC) containing rubber aggregates from shredded worn tires and filler using adaptive neuro fuzzy inference systems (ANFIS).

The volume substitution contains a ratio of rubber aggregates vs sand in mortar and with crushed sand in RCC and ranges from 0 to 50 per cent. As for the filler, they are substituted with sand by 5 per cent in mortar mixture. The methodology consists of optimizing the percentage of substitution in cementitious mixtures to ensure better mechanical properties of materials like compressive strength. The prediction of compressive strength and the optimization of cementitious mixtures encourage their uses in such construction pavements, in area games or in other special constructions. These cementitious materials are considered as friendly to the environment by focussing on their improved deformability.

The results of this paper show that the performance of the constructed fuzzy method was measured by correlation of experimental and model results of mortar and RCC mixtures containing both rubber aggregates and filler. The comparison between elaborated models through the error and the accuracy calculations confirms the reliability of the ANFIS method.

The purpose of this paper is to assess the performance of the constructed fuzzy model by the ANFIS method for two types of cementitious materials like mortar and RCC containing rubber aggregates and filler. The fuzzy method could predict the compressive strength based on the limited measurement values in the mechanical experiment. Furthermore, the comparison between the elaborated models confirms the reliability of the ANFIS method through the error and the accuracy calculations for the best cementitious material mixtures.

The purpose of this paper is to present the modeling of statistical variation of experimental data using the design of experiments method to optimize the formulation of a high performance concrete (HPC) using materials that are locally available in Algeria. For this, two mineral additions (natural pozzolana and limestone filler [LF]) were used. Both additions are added by substitution of cement up to 25%. To better appreciate the effect of replacing a part of cement by natural pozzolana and LF and to optimize their combined effect on the characteristics of HPC, an effective analytical method is therefore needed to reach the required objective.

The experimental part of the study consisted of substituting a portion of cement by various proportions of these additions to assess their effects on the physico-mechanical characteristics of HPC. A mixture design with three factors and five levels was carried out. The JMP7 software was used to provide mathematical models for the statistical variation of measured values and to perform a statistical analysis. These models made it possible to show the contribution of the three factors and their interactions in the variation of the response.

The mixture design approach made it possible to visualize the influence of LF and pozzolanic filler (PF) on the physico-mechanical characteristics of HPC, the developed models present good correlation coefficients (R² = 0.82) for all studied responses. The obtained results indicated that it is quite possible to substitute a part of cement with LF and PF in the formulation of a HPC. Thanks to the complementary effect between the two additions, the workability could be improved and the strengths drop could be avoided in the short, medium and long term. The optimization of mixture design factors based on the mathematical models was carried out to select the appropriate factors combinations; a good agreement between the experimental results and the predicted results was obtained.

The coefficient of PF in Cs₂₈ model is closer to that of LF than in Cs₇ model, thanks to the complementary effect between LF and PF at the age of 28 days. It was found that the optimal HPC14 concrete (10%LF–5%PF) provides the best compromise between the three responses. It is also worth noting that the use of these two local materials can reduce the manufacturing costs of HPC and reduce carbon dioxide emissions into the atmosphere. This can be an important economic and environmental alternative.