- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Using Radial Basis Function Artificial Neural Network for Predicting Asphalt Content of Asphalt Paving Mixture | ||
| IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
| Article 24, Volume 13, Issue 3, 2013, Pages 181-193 PDF (0 K) | ||
| DOI: 10.33899/iqjoss.2013.081298 | ||
| Author | ||
| Mohammed Y. Taha | ||
| Abstract | ||
| The determination of the asphalt content of asphalt paving mixtures is a fundamentally important test for many authorities and researchers in highways field . This study seek to examined the application and use of radial basis function artificial neural network with Gaussian activation function by MATLAB software for predicting the asphalt content of the hot mix asphalt paving mixtures using their properties of Marshall test. The architecture of the study developed network consist of five input nodes representing five properties of Marshall test, with six hidden nods, while the output consist of one output node representing the asphalt content percent. The study results have show that the radial basis function network can be applied as a recommended and appropriate computational tool to accurately and quickly determine the asphalt content of asphalt mixtures as alternative to using traditional techniques. | ||
|
Statistics Article View: 410 PDF Download: 213 |
||