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

Joint Authors

Yıldızel, Sadık Alper
Kaplan, Gökhan
Tuskan, Yeşim

Source

Advances in Civil Engineering

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

Civil Engineering

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