Department of Civil, Construction, & Environmental Engineering North Carolina State University, Campus Box 7908, Raleigh, North Carolina, 27695-7908, USA
Dr. Youngsoo Kim
Department of Civil, Construction, & Environmental Engineering North Carolina State University, Campus Box 7908, Raleigh, North Carolina, 27695-7908, USA
This paper presents a new energy-based failure criterion that is based on the simplified viscoelastic continuum damage model. This study found that the average reduction in pseudo stiffness up to failure, referred to here as DR, is a material constant that is independent of mode of loading, temperature, and stress/strain amplitude. Twenty different asphalt mixtures were used to validate the proposed method. This paper presents typical values of DR and shows that the DR changes with the mixture characteristics. The advantages of the DR failure criterion over a previous failure criterion (known as the GR criterion) are that it: (1) allows the prediction of fatigue failure in arithmetic scale, which reduces possible errors due to extrapolation of the accelerated laboratory fatigue test data to realistic traffic volumes encountered in the field and (2) reduces the number of tests required to characterise the failure criterion. Statistical analysis was performed in this study and the results show confidence levels that can be obtained from two or three fatigue tests.
1 August 2013 in Road Materials and Pavement Design
The failure criterion defines the applicable region associated with the continuum damage model and is important in characterising the service life of asphalt mixtures. A proper failure criterion should consistently predict the failure of the material that reaches macro-fracture. A previously developed criterion that uses the viscoelastic continuum damage (VECD) model exhibits high variability and is considered to be inefficient because it requires calibration tests at different temperatures. In this paper, a new concept that involves released pseudo strain energy is introduced. This released pseudo strain energy concept focuses on the dissipated energy that is related to stiffness reduction only and is fully compatible and predictable using the VECD model. A characteristic relationship is found between the stable rate of pseudo energy release during testing and the final fatigue life of the same mixture, independent of strain amplitude and temperature. Based on these observations, a new failure criterion is proposed. The proposed failure criterion combines the advantages of the VECD model and this characteristic relationship, which both originate from fundamental mixture properties, and is able to predict the fatigue life of asphalt concrete mixtures across different temperatures and strain amplitudes.
Fatigue cracking is one of the major distresses in asphalt pavements. Engineers have used many methods, from sophisticated numerical simulations to experience-based knowledge, to address fatigue cracking and deliver pavements that perform well. One strategy that has become increasingly widespread is the use of a fatigue index parameter that identifies the cracking potential of asphalt mixtures independent of the pavement structure. The use of such indices allows pavement engineers to make quick and targeted decisions with regard to mix design, mix acceptance, and mix quality assurance. This paper proposes a new fatigue index parameter, Sapp, which represents the damage capacity of a mixture and is based on the viscoelastic continuum damage theory. Unlike other indices, this new index considers the effects of both material toughness and stiffness. Sapp was employed in this study to evaluate 105 different asphalt mixtures with varied design factors. The results indicate that Sapp is able to distinguish the fatigue resistance of asphalt mixtures with varied properties, including different binder contents, binder performance grades, reclaimed asphalt pavement contents, types of binder modifier, air void contents, aggregate gradations, and aging levels. This paper also presents a set of recommended Sapp threshold values for different expected traffic volumes.