Determining Mechanical and Physical Properties of Phospho-Gypsum and Perlite-Admixtured Plaster Using an Artificial Neural Network and Regression Models

Abstract

This research investigates the utilization of artificial neural networks for improving the mechanical and physical properties of phospho-gypsum and perlite-admixtured plaster. The values obtained were modeled using an artificial neural network. Phospho-gypsum (CaSO4.2H2O) is known as a by-product of waste material of the phosphoric acid production process. Perlite is an amorphous volcanic glass. This study examined the effects of perlite and phospho-gypsum additives on fresh and hardened properties of plaster putty and also the feasibility of a plaster with these additives and heat insulation properties. Mixture and physico-mechanical properties after mixture conforming to standards have been provided. The values obtained were modeled with both multiple regression analysis and an artificial neural network. The R-2 values for multiple regression analysis with test data were between 0.5264 and 0.9883. R-2 value of the artificial neural network was found to be 0.9907. The test results of these mixtures have been compared and the plaster mixture with best values was obtained.