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Journal of Applied Sciences
  Year: 2020 | Volume: 20 | Issue: 1 | Page No.: 1-13
DOI: 10.3923/jas.2020.1.13
 
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Modeling and Analysis of the DSR Complex Shear Modulus Using the Principal Component Analysis (PCA)
M.A. Khasawneh and D.M. Al-Oqaily

Abstract:
Background and Objective: Dynamic Shear Rheometer is one of the Superpave tests used to establish the rheological properties of asphalt binders at intermediate to high temperatures and it is conducted on unaged, rolling thin film oven aged and pressure aging vessel aged samples. This study aims to model the dynamic shear rheometer complex shear modulus using different analytical techniques; these are non-linear regression, artificial neural network and principle component analysis. Materials and Methods: This could in turn reduce the effort and time to acquire laboratory results and consequently the cost. To this end, seven factors were found to influence the rheological properties of asphalt binders based on a dataset of 627 records of asphalt binder samples collected from the long-term pavement performance website. Results: These factors are: test temperature, type of aging, low performance grade, high performance grade, penetration, kinematic viscosity and absolute (dynamic) viscosity. Based on the analysis performed, it was found that all developed models were satisfactory in predicting the complex shear modulus as measured by the coefficient of determination and the root mean squared error. In addition, the artificial neural network and the principal component neural network were found to be the best modeling techniques. Conclusion: Finally, the principle component analysis approach was found to be robust and reliable modeling technique especially when the original input parameters are correlated.
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How to cite this article:

M.A. Khasawneh and D.M. Al-Oqaily, 2020. Modeling and Analysis of the DSR Complex Shear Modulus Using the Principal Component Analysis (PCA). Journal of Applied Sciences, 20: 1-13.

DOI: 10.3923/jas.2020.1.13

URL: https://scialert.net/abstract/?doi=jas.2020.1.13

 
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