A novel approach to modeling environmentally sustainable mortars incorporating pozzolanic additives and recycled aggregate through PLS regression

Abstract

This paper presents the application of Partial Least Squares (PLS) regression for optimizing mortar compositions with the aim of achieving a balance between mechanical performance and environmental impact. Mortars containing pozzolanic additives (metakaolin, zeolite) and recycled materials were tested, with variations in cement and water content. The chemical characterization of raw materials was performed using energy-dispersive X-ray fluorescence (EDXRF) and inductively coupled plasma optical emission spectrometry (ICP-OES), enabling insight into the content of key oxides (SiO 2, Al 2O 3) that influence pozzolanic activity. Based on experimental data regarding mortar composition and compressive strength, as well as calculated CO₂ emissions per unit volume, a PLS model was constructed to quantify the influence of individual components on the target properties. The results showed that specific proportions of metakaolin and zeolite can contribute to the reduction of CO 2 emissions without a significant compromise in mechanical performance. Furthermore, the use of recycled concrete as a partial replacement for natural aggregate enables more efficient resource utilization and a reduction in construction waste, without impairing key properties of the mortar. The proposed model represents a tool for the efficient design of environmentally optimized mortar formulations, with strong potential for further application in engineering practice and the construction materials industry.

Keywords

• mortar
• pozzolanic additives
• recycled aggregates
• pls regression
• xrf analysis
• environmental optimization
• co 2 emissions