Estimation of Rutting Models by Combining Data from Different Sources

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Abstract
The accurate prediction of rutting development is an essential element for the efficient management of pavement systems. 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 AASHO and the WesTrack road tests. 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 us to predict the relative contributions of rutting originating both in the asphalt concrete and in the unbound layers in the same model.

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