Phosphate mine by-products as new cementitious binders for eco-mortars production: Experiments and machine learning approach

Abstract

Phosphate mining industry generates different types of by-products that have significant environmental impacts such as ecosystems destruction and soil contamination. To reduce their environmental footprint, these wastes were investigated as supplementary cementitious materials (SCMs). The generated by-products included a clayey material and calcareous marl which were used in the current study. Blends of the abovementioned materials with cement (ratio of 1:1) were investigated using X-Ray Fluorescence spectrometry (XRF), X-Ray Diffraction (XRD), Thermogravimetric Analysis (TDA-TG), Mercury Porosimetry, Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Electromechanical testing device. Using these results, a learning model based on multiple linear regression (MLR) was proposed to predict the compressive strength and the specific surface area from the constituents of the material, the additives, the L/S ratio, and the hardening regime. The accuracy of the models was assessed using the correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE). Compressive strength results confirmed that the sample's strength improved with the amount of calcined clay. Unlike the water demand where the mixtures required more water than the OPC mixture. SEM-EDS examinations proved the existence of the C–S–H gel, responsible for the specimen strength. The used machine learning model demonstrated excellent performance and practical potential to predict both compressive strength (CS) and specific surface area (SS) by capturing both linear and nonlinear relationships. As well as time and plasticizer were the most influential factors on the properties studied (CS and SS) and their effect was positive. This sensitivity study provides important information on the critical factors influencing compressive strength and specific surface area in the different ranges considered.