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Reliable predictions of asphalt materials and pavement performance are important elements in mixture design, mechanistic-empirical pavement design, and performance-related specifications. This paper presents FlexPAVE™, a pavement performance prediction program. FlexPAVE™ is a three-dimensional finite element program that is capable of moving load analysis under realistic climatic conditions. It utilizes the simplified viscoelastic continuum damage (S-VECD) model to predict asphalt pavement fatigue life. This S-VECD model currently incorporates the so-called GR failure criterion to define the failure of asphalt mixtures. In this study, a new failure criterion for the S-VECD model, designated as the DR criterion, has been developed to remedy some of the shortcomings of the GR failure criterion. This DR criterion has been implemented successfully in FlexPAVETM. In this paper, FlexPAVETM is used to simulate the fatigue performance of field test sections. These test sections include various pavement structures, such as perpetual pavements and accelerated load facility test pavements in the United States, South Korea, and China, as well as various materials, such as warm-mix asphalt, reclaimed asphalt pavement, and mixtures with modified binders. The DR-based FlexPAVETM predictions have yielded good agreement with the field measurements and show more reasonable trends compared to predictions obtained using the GR failure criterion.

This paper aims to establish the relationship between the volumetric performance of asphalt mixtures and their performance in relation to pavement fatigue cracking and rutting. A good performance-volumetric relationship (PVR) can dramatically improve the working efficiency of mixtures and can be used in future performance-engineered mixture design and performance-related specifications. For this study, three asphalt mixtures were first designed to incorporate systematic changes in volumetric conditions, then fatigue cracking and rutting performance tests were conducted at each condition. Statistical analyses of the results suggest that a first-order (linear) model and power model would be an appropriate form of the PVR function. The number of volumetric conditions required to calibrate the PVR function is also investigated. Finally, a rule of thumb for selecting the volumetric conditions for the model calibrations is provided. The verification results show that the proposed PVR function is able to capture the response of mixture performance to changes in volumetric conditions.

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The simplified viscoelastic continuum damage model (S-VECD) has been widely accepted as a computationally efficient and a rigorous mechanistic model to predict the fatigue resistance of asphalt concrete. It operates in a deterministic framework, but in actual practice, there are multiple sources of uncertainty such as specimen preparation errors and measurement errors which need to be probabilistically characterized. In this study, a Bayesian inference-based Markov Chain Monte Carlo method is used to quantify the uncertainty in the S-VECD model. The dynamic modulus and cyclic fatigue test data from 32 specimens are used for parameter estimation and predictive envelope calculation of the dynamic modulus, damage characterization and failure criterion model. These parameter distributions are then propagated to quantify the uncertainty in fatigue prediction. The predictive envelope for each model is further used to analyze the decrease in variance with the increase in the number of replicates. Finally, the proposed methodology is implemented to compare three asphalt concrete mixtures from standard testing. The major findings of this study are: (1) the parameters in the dynamic modulus and damage characterization model have relatively strong correlation which indicates the necessity of Bayesian techniques; (2) the uncertainty of the damage characteristic curve for a single specimen propagated from parameter uncertainties of the dynamic modulus model is negligible compared to the difference in the replicates; (3) four replicates of the cyclic fatigue test are recommended considering the balance between the uncertainty of fatigue prediction and the testing efficiency; and (4) more replicates are needed to confidently detect the difference between different mixtures if their fatigue performance is close.