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

Yildizel S. A., TUSKAN Y., Kaplan G.

ADVANCES IN CIVIL ENGINEERING, vol.2017, 2017 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2017
  • Publication Date: 2017
  • Doi Number: 10.1155/2017/7620187
  • Journal Name: ADVANCES IN CIVIL ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Manisa Celal Bayar University Affiliated: No

Abstract

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.