Predicting the Strength of Cement Mortars Containing Natural Pozzolan and Silica Fume Using Multivariate Regression Analysis

Abstract

In this study, mortars containing locally available natural pozzolan (NP) in Almadinah Almunawara, Kingdom of Saudi Arabia, were investigated as a partial substitute for sand or cement in mortars and silica fume (SF). The benefit of using local NP powder as a replacement for cement is that it reduces the carbon dioxide emission during the cement manufacturing process, whereas the benefit of using local NP as fine aggregates is that it reduces the density of the produced mortars and improves its properties because of its pozzolanic effect. Because of these reasons, there is a need to develop an effective predictive model to estimate the compressive strength of mortars with partial replacement of cement or sand with NP and with SF as a replacement for cement at 28 days. Data of 68 cubic specimens of 50 mm were established through experimental work with other researchers, and they were chosen to create a database for the proposed model. There were three input parameters: a) level of partial substitution of cement with NP powder, b) level of partial substitution of sand with NP, and c) level of partial substitution of cement with SF. The output parameter was compressive strength. Best correlations were obtained between the compressive strength and sand replacement with NP. To predict the compressive strengths of cement mortars containing NP and SF, multivariate regression models were proposed and compared to find the best one. It was concluded that the full quadratic model was the best model with highest correlation when compared with other proposed models.

Multifunctional computational models to predict the long‐term compressive strength of concrete incorporated with waste steel slag

“…The appropriateness of the entire quadratic model for projecting the compressive strength of concrete with steel slag substituted for typical coarse aggregate was evaluated; each parameter is shown in squared, interaction (product), linear, and constant terms; Equation 3a depicts the revealed model for data containing steel slag aggregate and Equation 3b indicates the model for steel slag absent. 51,52 There does not seem to be any evidence linking model predictions to mistaken words.…”

“…The appropriateness of the entire quadratic model for projecting the compressive strength of concrete with steel slag substituted for typical coarse aggregate was evaluated; each parameter is shown in squared, interaction (product), linear, and constant terms; Equation depicts the revealed model for data containing steel slag aggregate and Equation indicates the model for steel slag absent 51,52 . There does not seem to be any evidence linking model predictions to mistaken words. …”

Effect of polypropylene fibre on cementitious mortar early shrinkage cracking using the eccentric-ring test

“…Multilinear regression was used to formulate the response of the tensile strength and crack width to changes in the amount of water to cement (w/c) and PP fibres [V f (%)]. Multiple regression is an extension of ordinary least-squares (OLS) regression (Dahish et al, 2021), and uses more than one variable to describe the tensile strength and crack width in fibrous cementitious mortar.…”

Prediction of California Bearing Ratio of Granular Soil by Multivariate Regression and Gene Expression Programming

“…e PQ which is none linear generally contains linear variables and constants; it is effective when such a pattern does not appear to be linear, and the relationships of the variables tend to be curvilinear [16]. Table 8 illustrates the PQ models for the prediction of CBR of granular soil.…”