Prediction of skid resistance value of glass fiber-reinforced tiling materials
Yükleniyor...
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
WOS:000418961700001
Anahtar Kelimeler
Kaynak
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Sadık, A. Y., Yeşim, T., Gökhan, K. (2017). Prediction of skid resistance value of glass fiber-reinforced tiling materials. Advances in Civil Engineering, 2017.
