University of California Transportation Center

The accurate prediction of rutting development is an essential element for the efficient management of pavement systems. In addition, progression models of highway pavement rutting can be used to study the effects of different loading levels, and thus in allocating cost responsibilities to various vehicle classes for their use of the highway system. Further, such models can be used for evaluating different strategies for design, maintenance and rehabilitation. Finally, if the models contain information about asphalt concrete mixes, they can also provide directions in the proportioning of aggregate, asphalt and air in the mix.

The objective of this paper is to demonstrate the effectiveness of the estimation of rutting models by combining the information from two data sources. The data sources considered are the AASHO Road Test and the WesTrack Road Test. Combined estimation with both data sources is used to identify parameters that are not identifiable from one data source alone. In addition, this estimation approach also yields more efficient parameter estimates.

The results presented in this paper demonstrate that joint estimation produces more realistic parameter estimates than those obtained by using either data set alone. Furthermore, joint estimation allows us to account for the effects of pavement structure, axle load configuration, asphalt concrete mix properties, freeze-thaw cycles and hot temperatures in a single model. Finally, it allows to us to predict the relative contributions of rutting originating both in the asphalt concrete and in the unbound layers in the same model.