Journal
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
Volume 10, Issue 5, Pages 341-352Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298430802342690
Keywords
dynamic (vertical bar E*vertical bar) modulus; hot mix asphalt; artificial neural networks; prediction model; MEPDG; pavement design
Ask authors/readers for more resources
This paper describes an innovative approach related to the development of a new hot mix asphalt (HMA) dynamic modulus (vertical bar E*vertical bar) prediction model by employing the artificial neural networks (ANNs) methodology. Many studies have been conducted over the last 50 years related to the development of HMA vertical bar E*vertical bar prediction models based on the regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with the ANNs that have proved useful for solving certain types of problems that are too complex, poorly understood or resource intensive to tackle using more traditional numerical and statistical methods. The ANN vertical bar E*vertical bar prediction models were developed using the latest comprehensive vertical bar E*vertical bar database that is available to the researchers (from the NCHRP Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak vertical bar E*vertical bar prediction model, which is included in the mechanistic-empirical pavement design guide (MEPDG) and the new revised version as well. The sensitivity of input variables to the ANN model predictions were also examined and discussed. The ANN vertical bar E*vertical bar models show significantly higher prediction accuracy compared with the existing regression models and could easily be incorporated into the MEPDG. This approach may lead to more accurate characterisation of the HMA dynamic modulus resulting in better performance prediction, thereby reducing the risk of premature pavement failure.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available