Compressive strength optimisation of rice husk ash concrete using Scheffe’s mathematical model

The high cost of cement as a significant component of concrete has led to the high cost of concrete production in most developing countries. Because of its longevity and good benefit-to-cost ratio, blended cement has grown in popularity in developed countries. Rice husk ash (RHA) is a residue produced by the burning of rice husk that is abundant in rice mills. RHA has been proven as a good supplementary cementitious material for concrete production due to its low energy requirements, minimal greenhouse gas emissions during processing and service life, and strong pozzolanic reaction. Using Scheffe's (4, 2) simplex-lattice design, a mathematical model was developed to optimise the compressive strength of RHA reinforced concrete in this research. RHA was used as the second component in concrete, along with water, cement, fine and coarse aggregates, at a partial replacement ratio of 20% in cement. The compressive strength of RHA concrete was determined using Scheffe's Simplex technique for the various componential ratios as well as the control points that would be used to validate the Scheffe's model. The model's adequacy was assessed using the f-statistics test, the student's t-test, and ANOVA at a 5% significance level. The statistical result shows a satisfactory correlation between the values produced from the developed Scheffe's model and the control laboratory data. The maximum compressive strength of RHA concrete obtained was 40.75 N/mm(2) corresponding to a mix ratio of 0.475: 1.0: 2.75: 3.50 and the minimum compressive strength obtained was 7.41 N/mm(2) corresponding to a mix ratio of 0.47: 1.0: 2.5: 4.5 for water, binder (80% cement and 20% RHA), fine aggregate, and coarse aggregate, respectively. The ratio of the mix elements to a particular required compressive strength value may be calculated with a high degree of precision using the established Scheffe's simplex model, while also giving the answer in less time by resolving trial mix challenges.