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

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dc.contributor.authorYildizel, Sadik Alper
dc.contributor.authorTuskan, Yesim
dc.contributor.authorKaplan, Gökhan
dc.date.accessioned2026-01-02T23:53:56Z
dc.date.issued2017-01-01
dc.description.abstractThis 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.description.urihttps://doi.org/10.1155/2017/7620187
dc.description.urihttp://downloads.hindawi.com/journals/ace/2017/7620187.pdf
dc.description.urihttps://doaj.org/article/2a93ef533b454f7e9083f5c6ac05a591
dc.description.urihttps://dx.doi.org/10.1155/2017/7620187
dc.description.urihttps://hdl.handle.net/11492/2818
dc.identifier.doi10.1155/2017/7620187
dc.identifier.eissn1687-8094
dc.identifier.endpage8
dc.identifier.issn1687-8086
dc.identifier.openairedoi_dedup___::ab6ae37d4bc0a9be1089f855b1565275
dc.identifier.orcid0000-0001-5702-807x
dc.identifier.orcid0000-0001-6067-7337
dc.identifier.scopus2-s2.0-85042098170
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12597/36340
dc.identifier.volume2017
dc.identifier.wos000418961700001
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofAdvances in Civil Engineering
dc.rightsOPEN
dc.subjectTA1-2040
dc.subjectEngineering (General). Civil engineering (General)
dc.titlePrediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials
dc.typeArticle
dspace.entity.typePublication
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