Using Artificial Neural Network Methods for Modeling Moisture Susceptibility of Asphalt Mixture Modified by Nano TiO2-Reference-Cited by-同舟云学术

Using Artificial Neural Network Methods for Modeling Moisture Susceptibility of Asphalt Mixture Modified by Nano TiO2

Published:2022-06 Issue:6 Volume:34 Page:
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Babagoli Rezvan¹,Rezaei Mohsen²

Affiliation:

1. Associate Professor, Faculty of Civil Engineering, Univ. of Science and Technology of Mazandaran, P.O. Box 48518-78195, Behshahr, Iran (corresponding author).

2. Associate Professor, Dept. of Industrial Engineering, Univ. of Science and Technology of Mazandaran, Behshahr, Iran.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0004255

Reference60 articles.

1. Evaluation of steel slag coarse aggregate in hot mix asphalt concrete

2. Evaluation of elevated temperature properties of asphalt cement modified with aluminum oxide and calcium carbonate nanoparticles

3. Evaluation of the effect of carbon nano tube on water damage resistance of Stone matrix asphalt mixtures containing polyphosphoric acid and styrene butadiene rubber

4. The effects of gilsonite and crumb rubber on moisture damage resistance of stone matrix asphalt mixtures

5. Experimental investigation of the influence of Nano TiO2 on rheological properties of binders and performance of stone matrix asphalt mixtures containing steel slag aggregate

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