We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Determining Mechanical and Physical Properties of Phospho-Gypsum and Perlite-Admixtured Plaster Using an Artificial Neural Network and Regression Models.
- Authors
Oktay, Başak Mesci; Odabaş, Elif
- 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 R2 values for multiple regression analysis with test data were between 0.5264 and 0.9883. R2 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.
- Subjects
PROPERTIES of matter; MECHANICAL behavior of materials; PERLITE; GYPSUM; PLASTER; ARTIFICIAL neural networks; REGRESSION analysis
- Publication
Polish Journal of Environmental Studies, 2017, Vol 26, Issue 5, p2425
- ISSN
1230-1485
- Publication type
Article
- DOI
10.15244/pjoes/70399