Test Method for Determining the Flexural Creep Stiffness of Asphalt Binder Using the Bending Beam Rheometer (BBR)

10.1520/d6648 ◽  
2008 ◽  
Author(s):  
2018 ◽  
Vol 45 (7) ◽  
pp. 594-604 ◽  
Author(s):  
Augusto Cannone Falchetto ◽  
Ki Hoon Moon ◽  
Di Wang ◽  
Chiara Riccardi

In this paper, the possibility of using air as an alternative cooling medium for testing asphalt binder in the bending beam rheometer (BBR) is considered and evaluated. For this purpose, five asphalt binders were characterized with the BBR; creep stiffness, m-value, performance grade (PG), thermal stress, and critical cracking temperature were computed both for ethanol and air. In addition, the rheological Huet model was fitted to the experimental measurements to further investigate the effect of the cooling medium. It was found that air measurements result in stiffer materials, with higher low PG, higher thermal stress, and critical cracking temperature. The parameters of the Huet model confirm such a stiffening effect when air is used. Based on the material response observed in this study, further research is recommended before potentially replacing ethanol with air in the BBR, as the latter appears to provide a substantially different material grading.


2019 ◽  
Vol 821 ◽  
pp. 500-505
Author(s):  
Mohammad Fuad Aljarrah ◽  
Mohammad Ali Khasawneh ◽  
Aslam Ali Al-Omari ◽  
Mohammad Emad Alshorman

The major objective of this study is to investigate the possibility of using Artificial Neural Networks in creating prediction models capable of estimating Bending Beam Rheometer outputs; namely creep stiffness, and m-value based on test temperature, modifier content; in our case waste vegetable oil, and testing time interval. A feedforward backpropagation neural network with Bayesian Regulation training algorithm and an SSE performance function was implemented. It was found that the neural network model shows high predictive powers with training and testing performance of 99.8% and 99.2% respectively. Plots between laboratory obtained values and neural network predicted outputs were also considered, and a strong correlation between the two methods was concluded. Therefore, it was reasonable to state that using neural networks to build prediction models in order to find BBR test values is justified.


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