Using Artificial Neural Networks (ANNs) for Hot Mix Asphalt E* Predictions-Reference-Cited by-同舟云学术

Using Artificial Neural Networks (ANNs) for Hot Mix Asphalt E* Predictions

Published:2016-07-21 Issue: Volume: Page:
ISSN:
Container-title:Geo-China 2016
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Author:

El-Badawy Sherif M.¹²³,Khattab Ahmed M.¹²³,Al Hazmi Al Abbas¹²³

Affiliation:

1. Associate Professor, Public Works Engineering Dept., Mansoura Univ., Mansoura 35516, Egypt.

2. Graduate Student, Public Works Engineering Dept., Mansoura Univ., Mansoura 35516, Egypt.

3. Project Manager, Ministry of Transport, Research and Material Dept., Kingdom of Saudi Arabia.

Publisher

American Society of Civil Engineers

Link

http://ascelibrary.org/doi/pdf/10.1061/9780784480076.010

Reference16 articles.

1. AASHTO T315-06. (2006). "Standard Method of Test Determining the Rheological Properties of Asphalt Binder Using a Dynamic Shear Rheometer (DSR)." American Association of State Highway and Transportation Officials Washington DC.

2. AASHTO TP 342-11. (2011). "Standard Method of Test for Determining Dynamic Modulus of Hot Mix Asphalt Concrete Mixtures." American Association of State Highway and Transportation Officials Washington DC.

3. ASTM D2493M-09. (2009). "Standard Viscosity-Temperature Chart for Asphalts." West Conshohocken PA.

4. Awed A. El-Badawy S. and Bayomy F. (2011). "Influence of the MEPDG Binder Characterization Input Level on the Predicted Dynamic Modulus for Idaho Asphalt Concrete Mixtures" Paper No. 11-1268 Presented at the 90 th Annual Meeting of the Transportation Research Board and Published in the Annual Meeting DVD Washington DC.

5. Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models

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