Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model-Reference-Cited by-同舟云学术

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Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Published:2019-12-09 Issue: Volume: Page:1-15
ISSN:0169-4243
Container-title:Journal of Adhesion Science and Technology
language:en
Short-container-title:Journal of Adhesion Science and Technology

Author:

Arifuzzaman Md¹^ORCID,Gazder Uneb²^ORCID,Islam Muhammad Saiful³^ORCID,Mamun Abdullah Al⁴^ORCID

Affiliation:

1. Civil and Environmental Engineering, King Faisal University, Hofuf, Saudi Arabia;

2. Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain;

3. Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh;

4. College of Engineering, Qatar University, Doha, Qatar

Publisher

Informa UK Limited

Subject

Materials Chemistry,Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanics of Materials,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/01694243.2019.1698201

Reference26 articles.

1. Effect of fly ash and lime as mineral filler in asphalt concrete

2. Comparative laboratory evaluation of waste cooking oil rejuvenated asphalt concrete mixtures for high contents of reclaimed asphalt pavement

3. Local calibration of AASHTOWare pavement ME design software for flexible pavements in Kansas

4. Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence

5. Moisture susceptibility of asphalt mixtures, Part 1: mechanisms

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1. Multi-performance optimization of gas metal arc welding operation in terms of energy saving and quality criteria;Journal of Adhesion Science and Technology;2024-09-05

2. Pavement performance analysis of carbon nanotube/SBS composite modified asphalt;Carbon Letters;2023-09-17

3. Laboratory Simulation of RAP Incorporated Mix in a Cold Region: An Artificial Intelligence-Based Approach;International Journal of Pavement Research and Technology;2023-06-19

4. Artificial Neural Networks for Flexible Pavement;Information;2023-01-18

5. Establishment of correlation model between compositions and dynamic viscoelastic properties of asphalt binder based on machine learning;Construction and Building Materials;2023-01

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