Scopus: Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials
dc.contributor.author | Yildizel S. | |
dc.contributor.author | Tuskan Y. | |
dc.contributor.author | Kaplan G. | |
dc.date.accessioned | 2023-04-12T02:31:11Z | |
dc.date.available | 2023-04-12T02:31:11Z | |
dc.date.issued | 2017-01-01 | |
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. | |
dc.identifier.doi | 10.1155/2017/7620187 | |
dc.identifier.issn | 16878086 | |
dc.identifier.scopus | 2-s2.0-85042098170 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12597/5561 | |
dc.relation.ispartof | Advances in Civil Engineering | |
dc.rights | true | |
dc.title | Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials | |
dc.type | Article | |
dspace.entity.type | Scopus | |
oaire.citation.volume | 2017 | |
person.affiliation.name | Karamanoğlu Mehmetbey Üniversitesi | |
person.affiliation.name | Celal Bayar Üniversitesi | |
person.affiliation.name | Kastamonu University | |
person.identifier.orcid | 0000-0001-5702-807X | |
person.identifier.orcid | 0000-0001-6067-7337 | |
person.identifier.scopus-author-id | 57120104100 | |
person.identifier.scopus-author-id | 57193419558 | |
person.identifier.scopus-author-id | 57118954700 | |
relation.isPublicationOfScopus | c900a017-ea68-4842-97d7-d8a916925852 | |
relation.isPublicationOfScopus.latestForDiscovery | c900a017-ea68-4842-97d7-d8a916925852 |