Suitable Tests and Machine Learning Approach to Predict Moisture Susceptibility of Hot-Mix Asphalt-Reference-Cited by-同舟云学术

Suitable Tests and Machine Learning Approach to Predict Moisture Susceptibility of Hot-Mix Asphalt

Published:2019-09 Issue:3 Volume:145 Page:04019030
ISSN:2573-5438
Container-title:Journal of Transportation Engineering, Part B: Pavements
language:en
Short-container-title:J. Transp. Eng., Part B: Pavements

Author:

B. Mallick Rajib¹,Madankara Kottayi Nivedya²^ORCID,Veeraragavan Ram Kumar³,Dave Eshan⁴,DeCarlo Christopher⁵^ORCID,Sias Jo E.⁶^ORCID

Affiliation:

1. Professor, Dept. of Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA 01609.

2. Postdoctoral Fellow, Dept. of Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 (corresponding author). ORCID: .

3. Ph.D. Scholar, Dept. of Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA 01609.

4. Assistant Professor, Dept. of Civil Engineering, Univ. of New Hampshire, Durham, NH 03824.

5. Research Civil Engineer, Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, NH 03755. ORCID: .

6. Professor, Dept. of Civil Engineering, Univ. of New Hampshire, Durham, NH 03824. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/JPEODX.0000132

Reference56 articles.

1. Chemical and mechanical changes in asphalt binder due to moisture conditioning

2. Development and evaluation of test system to induce and monitor moisture damage to asphalt concrete mixtures;Al-Swailmi S.;Transp. Res. Rec.,1992

3. Moisture susceptibility evaluation of asphalt mixes based on image analysis

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