English
| dc.contributor.author | Yildizel, SA | |
| dc.contributor.author | Tuskan, Y | |
| dc.contributor.author | Kaplan, G | |
| dc.date.accessioned | 2024-07-18T11:56:58Z | |
| dc.date.available | 2024-07-18T11:56:58Z | |
| dc.description.abstract | HINDAWI LTD | |
| dc.identifier.issn | 1687-8094 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/6903 | |
| dc.language.iso | Article | |
| dc.publisher | 1687-8086 | |
| dc.subject | 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. | |
| dc.title | English | |
| dc.type | MECHANICAL-PROPERTIES | |
| dc.type | NEURAL-NETWORKS | |
| dc.type | CONCRETE | |
| dc.type | CAPACITY | |
| dc.type | BEHAVIOR | |
| dc.type | TEXTURE | |
| dc.type | SLIP | |
| dc.type | LOAD |