A deep learning approach to predict Hamburg rutting curve-Reference-Cited by-同舟云学术

A deep learning approach to predict Hamburg rutting curve

Published:2021-02-16 Issue:9 Volume:22 Page:2159-2180
ISSN:1468-0629
Container-title:Road Materials and Pavement Design
language:en
Short-container-title:Road Materials and Pavement Design

Author:

Majidifard Hamed¹^ORCID,Jahangiri Behnam¹^ORCID,Rath Punyaslok¹^ORCID,Alavi Amir H.²,Buttlar William G.¹^ORCID

Affiliation:

1. Civil and Environmental Engineering Department, University of Missouri-Columbia, Columbia, MO, USA

2. Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Funder

Missouri Department of Transportation

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14680629.2021.1886160

Reference53 articles.

1. AASHTO. (2013). 324. Hambg. Wheel. Test. Compact. Hot Mix Asph.

2. Asphalt Mixture Quality Acceptance using the Hamburg Wheel-Tracking Test

3. Evaluation of moisture susceptibility tests for warm mix asphalts

4. Buttlar, W. G., Jahangiri, B., Rath, P., Majidifard, H., Urra, L., Meister, J. & Brown, H. (in-press). Development of a performance-related asphalt mix design specification for the Illinois tollway. Tollw. Rep.

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