Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials
| dc.contributor.author | Yildizel S.A. | |
| dc.contributor.author | Tuskan Y. | |
| dc.contributor.author | Kaplan G. | |
| dc.date.accessioned | 2025-04-10T11:08:42Z | |
| dc.date.available | 2025-04-10T11:08:42Z | |
| dc.date.issued | 2017 | |
| dc.description.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. © 2017 Sadik Alper Yildizel et al. | |
| dc.identifier.DOI-ID | 10.1155/2017/7620187 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/48225 | |
| dc.publisher | Hindawi Limited | |
| dc.title | Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials | |
| dc.type | Article |