Investigating the effects of ensemble and weight optimization approaches on neural networks’ performance to estimate the dynamic modulus of asphalt concrete-Reference-Cited by-同舟云学术

Investigating the effects of ensemble and weight optimization approaches on neural networks’ performance to estimate the dynamic modulus of asphalt concrete

Published:2022-08-19 Issue: Volume: Page:1-21
ISSN:1468-0629
Container-title:Road Materials and Pavement Design
language:en
Short-container-title:Road Materials and Pavement Design

Author:

Huang Jiandong¹²,Zhang Jia²,Li Xin²,Qiao Yaning³,Zhang Runhua⁴^ORCID,Kumar G. Shiva⁵^ORCID

Affiliation:

1. School of Civil Engineering, Guangzhou University, Guangzhou, China

2. School of Mines, China University of Mining and Technology, Xuzhou, China

3. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, China

4. Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, USA

5. Department of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, India

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14680629.2022.2112061

Reference70 articles.

1. AASHTO, T. (2012). 79-12. Standard method of test for determining the dynamic modulus and flow number for Hot Mix Asphalt (HMA) Using the Asphalt Mixture Performance Tester (AMPT), American Association of Highway and Transportation Officials (AASHTO), Washington DC.

2. Investigation of factors affecting dynamic modulus and phase angle of various asphalt concrete mixtures

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