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Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials
Joint Authors
Yıldızel, Sadık Alper
Kaplan, Gökhan
Tuskan, Yeşim
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yıldızel, Sadık Alper& Tuskan, Yeşim& Kaplan, Gökhan. 2017. Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials. Advances in Civil Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1121355
Modern Language Association (MLA)
Yıldızel, Sadık Alper…[et al.]. Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials. Advances in Civil Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1121355
American Medical Association (AMA)
Yıldızel, Sadık Alper& Tuskan, Yeşim& Kaplan, Gökhan. Prediction of Skid Resistance Value of Glass Fiber-Reinforced Tiling Materials. Advances in Civil Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1121355
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121355