Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction-Reference-Cited by-同舟云学术

Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction

Published:2018-07 Issue:7 Volume:30 Page:04018128
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

El-Badawy Sherif¹^ORCID,Abd El-Hakim Ragaa²,Awed Ahmed³

Affiliation:

1. Associate Professor, Dept. of Public Works Engineering, Faculty of Engineering, Mansoura Univ., Mansoura 35516, Egypt (corresponding author). ORCID: .

2. Assistant Professor, Dept. of Public Works Engineering, Faculty of Engineering, Tanta Univ., Tanta 31527, Egypt.

3. Assistant Professor, Dept. of Public Works Engineering, Faculty of Engineering, Mansoura Univ., Mansoura 35516, Egypt.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0002282

Reference26 articles.

1. Awed A. 2016. “Characterization of macro-scale and meso-scale performance of asphalt concrete mixtures under compression.” Ph.D. dissertation Texas A&M Univ.

2. Awed A. S. El-Badawy F. Bayomy and M. Santi. 2011. “Influence of the MEPDG binder characterization input level on the predicted dynamic modulus for Idaho asphalt concrete mixtures.” In Proc. 90th Annual Meeting of the Transportation Research Board. Washington DC: Transportation Research Board.

3. Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures;Bari J.;J. Assoc. Asphalt Paving Technol.,2006

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