Alternative Approach for Predicting the Phase Angle Characteristics of Asphalt Concrete Mixtures Based on Recurrent Neural Networks-Reference-Cited by-同舟云学术

Alternative Approach for Predicting the Phase Angle Characteristics of Asphalt Concrete Mixtures Based on Recurrent Neural Networks

Published:2021-09 Issue:9 Volume:33 Page:04021215
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Hussain Fizza¹,Ali Yasir²^ORCID,Irfan Muhammad³

Affiliation:

1. Graduate Research Assistant, School of Electrical Engineering and Computer Science, National Univ. of Sciences and Technology (NUST), Islamabad 44000, Pakistan.

2. Research Fellow, National Institute of Transportation, National Univ. of Sciences and Technology (NUST), Islamabad 44000, Pakistan (corresponding author). ORCID: .

3. Professor, Military College of Engineering (MCE), National Univ. of Sciences and Technology (NUST), NUST Campus, Risalpur 24080, Pakistan.

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.1943-5533.0003855

Reference21 articles.

1. AASHTO. 2007. TP 62-07 Standard test method for determining the dynamic modulus of hot mix asphalt (HMA). Washington, DC: AASHTO.

2. Abdić I. L. Fridman D. E. Brown W. Angell B. Reimer E. Marchi and B. Schuller. 2016. “Detecting road surface wetness from audio: A deep learning approach.” In Proc. 2016 23rd Int. Conf. on Pattern Recognition (ICPR) 3458–3463. New York: IEEE.

3. Empirical Correlation of Permanent Deformation Tests for Evaluating the Rutting Response of Conventional Asphaltic Concrete Mixtures

4. Sensitivity analysis of dynamic response and fatigue behaviour of various asphalt concrete mixtures

5. The impact of data noise on permanent deformation behaviour of asphalt concrete mixtures

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optimizing asphalt mix design through predicting volumetric properties using interpretable machine learning;Powder Technology;2024-08

2. An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures;Construction and Building Materials;2021-08