Artificial Intelligence Prediction of Rutting and Fatigue Parameters in Modified Asphalt Binders Article Swipe
Ikenna D. Uwanuakwa
,
Shaban Ismael Albrka Ali
,
Mohd Rosli Mohd Hasan
,
Pınar Akpınar
,
Ashiru Sani
,
Khairul Anuar Shariff
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/app10217764
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/app10217764
The complex shear modulus (G*) and phase angle (δ) are fundamental viscoelastic rheological properties used in the estimation of rutting and fatigue pavement distress in asphalt binder. In the tropical regions, rutting and fatigue cracking are major pavement distress affecting the serviceability of road infrastructure. Laboratory testing of the complex shear modulus and phase angle requires expensive and advanced equipment that is not obtainable in major laboratories within the developing countries of the region, giving rise to the need for an accurate predictive model to support quality pavement design. This research aims at developing a predictive model for the estimation of rutting and fatigue susceptive of asphalt binder at intermediate and high pavement temperatures. Asphalt rheological and ageing test was conducted on eight mixes of modified binders used to build the study database containing 1976 and 1668 data points for rutting and fatigue parameters respectively. The database was divided into training and simulation dataset. The Gaussian process regression (GPR) algorithm was used to predict the rutting and fatigue parameters using unaged and aged conditioned inputs. The proposed GPR was compared with the support vector machine (SVM), recurrent neural networks (RNN) and artificial neural network (ANN) models. Results show that the model performed better in the estimation of rutting parameter than the fatigue parameter. Further, unaged input variables show better reliability in the prediction of fatigue parameter.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app10217764
- https://www.mdpi.com/2076-3417/10/21/7764/pdf?version=1604885215
- OA Status
- gold
- Cited By
- 36
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3095878732
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Raw OpenAlex JSON
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https://openalex.org/W3095878732Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app10217764Digital Object Identifier
- Title
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Artificial Intelligence Prediction of Rutting and Fatigue Parameters in Modified Asphalt BindersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-11-03Full publication date if available
- Authors
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Ikenna D. Uwanuakwa, Shaban Ismael Albrka Ali, Mohd Rosli Mohd Hasan, Pınar Akpınar, Ashiru Sani, Khairul Anuar ShariffList of authors in order
- Landing page
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https://doi.org/10.3390/app10217764Publisher landing page
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https://www.mdpi.com/2076-3417/10/21/7764/pdf?version=1604885215Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/10/21/7764/pdf?version=1604885215Direct OA link when available
- Concepts
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Rut, Asphalt, Fatigue cracking, Artificial neural network, Kriging, Phase angle (astronomy), Computer science, Geotechnical engineering, Environmental science, Engineering, Materials science, Machine learning, Composite material, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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36Total citation count in OpenAlex
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2025: 8, 2024: 11, 2023: 7, 2022: 7, 2021: 3Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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