Neural Network Model for Asphalt Concrete Permeability-Reference-Cited by-同舟云学术

Neural Network Model for Asphalt Concrete Permeability

Published:2005-02 Issue:1 Volume:17 Page:19-27
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Tarefder Rafiqul Alam¹²³,White Luther¹²³,Zaman Musharraf¹²³

Affiliation:

1. Assistant Professor of Civil Engineering, Idaho State Univ., Pocatello, ID 83209.

2. Professor, Dept. of Mathematics, Univ. of Oklahoma, Norman, OK 73019.

3. David Ross Boyd Professor, Dept. of Civil Engineering and Environmental Science, Univ. of Oklahoma, 202 West Boyd, Norman, OK 73015.

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%290899-1561%282005%2917%3A1%2819%29

Reference19 articles.

1. American Association of the Highway and Transportation Officials (AASHTO). (1999). “Bulk specific gravity of compacted bituminous mixtures using saturated surface-dry specimens.” AASHTO T166 Washington D.C.

2. ASTM. (2001a). “Standard test method for percent air voids in compacted dense and open bituminous paving mixtures.” ASTM D 3203 West Conshohocken Pa.

3. ASTM. (2001b). “Standard test method for theoretical maximum specific gravity and density of bituminous paving mixtures.” ASTM D 2041 West Conshohocken Pa.

4. ASTM. (2001c). “Standard test method for thickness or height of compacted bituminous paving mixture samples.” ASTM D 3549 West Conshohocken Pa.

5. Bahia H. U. Benson C. H. and Kanitpong. K. (2001). “Hydraulic conductivity (permeability) of laboratory-compacted asphalt mixtures.” Transportation Research Record 1767 Transportation Research Board Washington D.C. 25–32.

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