MATEC Web Conf.
Volume 120, 2017International Conference on Advances in Sustainable Construction Materials & Civil Engineering Systems (ASCMCES-17)
|Number of page(s)||11|
|Section||Transportation and Pavement|
|Published online||09 August 2017|
Comparison of Witczak NCHRP 1-40D & Hirsh dynamic modulus models based on different binder characterization methods: a case study
1 Mansoura University, Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
2 Associate Professor, Mansoura University, Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
3 Professor, Mansoura University, Public Works Engineering Department, Mansoura 35516, Egypt
4 Projects Manager, Ministry of Transport, General Directorate for Material and Research, Riyadh 11178, Kingdom of Saudi Arabia
* Corresponding author: firstname.lastname@example.org
The Pavement ME Design method considers the hot mix asphalt (HMA) dynamic modulus (E*) as the main mechanistic property that affects pavement performance. For the HMA, E* can be determined directly by laboratory testing (level 1) or it can be estimated using predictive equations (levels 2 and 3). Pavement-ME Design introduced the NCHRP1-40D model as the latest model for predicting E* when levels 2 or 3 HMA inputs are used. This study focused on utilizing laboratory measured E* data to compare NCHRP1-40D model with Hirsh model. This comparison included the evaluation of the binder characterization level as per Pavement ME Design and its influence on the performance of these models. E*tests were conducted in the laboratory on 25 local mixes representing different road construction projects in the kingdom of Saudi Arabia. The main tests for the mix binders were dynamic Shear Rheometer (DSR) and Brookfield Rotational Viscometer (RV). Results showed that both models with level 3 binder data produced very similar accuracy. The highest accuracy and lowest bias for both models occurred with level 3 binder data. Finally, the accuracy of prediction and level of bias for both models were found to be a function of the binder input level.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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