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.
Fatigue Performance Prediction of Asphalt Pavements with FlexPAVETM, the S-VECD Model, and DR Failure Criterion
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2672 , 40 ; 217-227
2018-03-03
Article (Journal)
Electronic Resource
English
Viscoelastic continuum damage (VECD) models for cracking problems in asphalt mixtures
Online Contents | 2017
|Prediction of Fatigue Cracking in Asphalt Pavements
Transportation Research Record | 2007
|Fatigue life and endurance limit prediction of asphalt mixtures using energy-based failure criterion
Online Contents | 2017
|