Prediction performance of compressive strength of cementitious materials containing rubber aggregates and filler using fuzzy logic method

Abstract

Purpose 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). Design/methodology/approach 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. Findings 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. Originality/value 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.

Predicting of proactive environmental management for unhairing wastewater treatment in Tunisia using neural network learning algorithms

“…Moreover, many environmental protection initiatives have been taken as a priority in several Tunisian companies. But they should adopt a more proactive approach for their proactive environmental management and strategies (PEMS) to foster the decreasing of environmental impacts by best practices (Turki et al, 2017a(Turki et al, , 2017b. In the same case, the effectiveness of such initiatives taken by senior managers is assessed on the basis of different measures: first, the removal or reducing of several pollutants characterizing the unhairing effluents, second, the reduction of water and energy consumptions and third, the efficiency of solid wastes management.…”

“…Structure of ANN model. Several systems are not amenable to conventional modeling approaches due to the lack of precise and formal knowledge about the system, strongly nonlinear behavior and a high degree of uncertainty or time varying characteristics (Turki et al, 2017a(Turki et al, , 2017b. ANN modeling has been recognized as a powerful tool which can facilitate the effective development of models by combining information from different sources, such as empirical models, heuristics and data (Babuska, 2002).…”

Innovative Approaches to Using Kazakhstan’s Industrial Ferrous and Nonferrous Tailings in the Production of Ceramic Materials

“…Thus, what determined the quality of a specimen was the percentage of tailings. This research used regression analysis [16][17][18][19][20], which can identify unknown relationships and patterns to enable the researcher to find suitable equations. Regression analysis was carried out (for the basic ceramic brick indicators) in five stages: stage 1 was to analyze how the content of chromite-ore tailings and polymetallic-ore tailings affects the compressive strength of ceramic bricks; stage 2 was to analyze how the content of chromite-ore tailings and polymetallic-ore tailings affects the bending strength of ceramic bricks; Materials Science and Metallurgical Technology II stage 3 was to analyze how the content of chromite-ore tailings and polymetallic-ore tailings affects the frost resistance of ceramic bricks; stage 4 was to analyze how the content of chromite-ore tailings and polymetallic-ore tailings affects the thermal conductivity of ceramic bricks; stage 5 was to analyze how the content of chromite-ore tailings and polymetallic-ore tailings affects the heat resistance of ceramic bricks.…”

Use of Nonferrous Metallurgy Waste: Clayey Portion of the Zircon-Ilmenite Ore Gravity Tailings and Pyrite Cinders in Tile-Making

“…The common method of linear regression was used to study the dependency of the basic physicomechanical properties of tiles on how much clay from the ZIOG tailings, pyrite cinders, and wollastonite is used in manufacture. This method can be used to find how changes in variables affect the physico-mechanical parameters [18][19][20]. The model is based on the actual experimental data and provides an analytical description of the test results as a function.…”

“…Electron microscopy, ×16000 (a, b), ×31000 (c, d), and ×17000 (e,f) magnification Tiles were made in a soft-mud process at 20% to 22% moisture content, then dried to 7-8% max and fired at 1050 о С.The common method of linear regression was used to study the dependency of the basic physicomechanical properties of tiles on how much clay from the ZIOG tailings, pyrite cinders, and wollastonite is used in manufacture. This method can be used to find how changes in variables affect the physico-mechanical parameters[18][19][20]. The model is based on the actual experimental data and provides an analytical description of the test results as a function.Some factors such as pressure and firing temperature were unaltered during the experiments.…”