Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials

Autor: Sadik Alper Yildizel, Yesim Tuskan, Gökhan Kaplan
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Advances in Civil Engineering, Vol 2017 (2017)
Druh dokumentu: article
ISSN: 1687-8086
1687-8094
DOI: 10.1155/2017/7620187
Popis: This research focuses on the use of adaptive artificial neural network system for evaluating the skid resistance value (British Pendulum Number; BPN) of the glass fiber-reinforced tiling materials. During the creation of the neural model, four main factors were considered: fiber, calcium carbonate content, sand blasting, and polishing properties of the specimens. The model was trained, tested, and compared with the on-site test results. As per the comparison of the outcomes of the study, the analysis and on-site test results showed that there is a great potential for the prediction of BPN of glass fiber-reinforced tiling materials by using developed neural system.
Databáze: Directory of Open Access Journals
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