Assessment of dynamic modulus prediction models in fatigue cracking estimation

The dynamic modulus (E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document}) of Hot Mix Asphalt (HMA) mixtures is a key input parameter in the Mechanistic-Empirical (M-E) pavement design and analysis processes for the prediction of fatigue and rutting damage. The determination of E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} in the laboratory requires specialized equipment and is an overall time consuming procedure. With this in mind, various prediction models have been developed over the years for the estimation of the E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document}, based on the volumetric properties of the HMA and the binder properties. Flexible pavement design processes require, amongst others, an accurate prediction of the fatigue behavior of the asphalt mixtures. With regards to M-E pavement design, a fatigue model to predict the number of load repetitions to fatigue cracking as a function of the tensile strain and the E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} of the asphalt mixture is considered. Taking the above into consideration the aim of the present research study is the comparative assessment of the most widely used E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} prediction models and their impact on the predicted fatigue cracking in the context of M-E pavement design in comparison. Further, the impact of an E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} prediction model developed through calibration process is also investigated. For this purpose, an asphalt mixture and a pavement structure often implemented in highways of the national transportation network, was selected and fatigue cracking was calculated utilizing both, predicted E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} and laboratory determined values. Analysis showed that the large bias in the E∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E^{*}$$\end{document} prediction models is compensated to a certain extend in the final output which is the fatigue cracking. Relevant results from the sensitivity analysis are presented in the paper.