Journal of Transportation Research

Journal of Transportation Research

Creating an Analytical Model to Predict the Phase Angle (Δ) in The Dynamic Shear Rheometer (DSR) Test

Document Type : Original Article

Authors
Hassan Hosseinzadeh 1
Sina Arman 2
Behnam Khayat 3
1 M.Sc., Grad., Eqbal Lahori Institute of Higher Education, Mashhad, Iran.
2 M.Sc., Grad.,, Islamic Azad University, Mashhad, Iran.
3 Supervision of maintenance and repairs and operation of facilities
10.22034/tri.2023.398288.3157
Abstract
Superpave bitumen specifications are designed to improve pavement performance by controlling pavement problems under a wide range of temperatures and aging conditions. Dynamic shear rheometer (DSR) is one of the superpave tests used to determine the rheological properties of bitumen. The aim of this study is to create a prediction model with the ability to predict the phase angle (δ) as a main result of the DSR test method. This, in turn, can reduce the time to obtain laboratory results and, consequently, the cost. For this purpose, a ensemble machine learning method with a random forest approach has been used. Based on this, seven effective variables on bitumen phase angle were collected from the results of 1225 samples from the LTPP website. These factors are: test temperature, type of aging, low performance degree (PG-low), high performance degree (PG-high), penetration, kinematic viscosity and absolute viscosity (dynamic). The proposed method is confirmed through a 10-fold cross-validation method and based on the analysis, it reaches more than 90% accuracy in terms of coefficient of determination. Finally, the effect of some key factors in the random forest approach was also investigated, for example, the effect of the sensitivity of the phase angle input parameters. Also, based on the results of sensitivity analysis, the importance of different input variables was obtained. Based on the research, the test temperature and the type of aging have the greatest effect on the bitumen phase angle. By increasing the number and variety of training data, the model can be used to achieve better results and predict other performance properties of bitumen.
Keywords
LTPP
Dynamic Shear Rheometer
Phase Angle
Machine Learning
Random Forest

Subjects

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