Determining mechanical and physical properties of phospho-gypsum and perlite-admixtured plaster using an artificial neural network and regression models

• Autorzy: Oktay B.M. , Odabas E.
• Języki publikacji: EN

Abstrakty

EN

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.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2017
• Tom: 26
• Numer: 5
• Opis fizyczny: p.2425-2430,fig.,ref.

Twórcy

autor

Oktay B.M.

• Department of Material Science and Engineering, Faculty of Engineering, Ondokuz Mayis University, Samsun, Turkey

autor

Odabas E.

• Department of Material Science and Engineering, Faculty of Engineering, Ondokuz Mayis University, Samsun, Turkey

Bibliografia

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Identyfikatory

DOI

10.15244/pjoes/70399