United States Department of Transportation
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NTL Classification:NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance
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Abstract:The in situ moduli of unbound pavement materials vary on a seasonal basis as a function of temperature and moisture conditions.
Knowledge of these variations is required for accurate prediction of pavement life for pavement design and other pavement
management activities. The primary objective of this study is to advance the rational estimation of seasonal variations in
backcalculated pavement layer moduli using data collected via the Seasonal Monitoring Program of the Long-Term Pavement
Performance Program. Principal components of this endeavor included: evaluation of the moisture predictive capabilities of the
Enhanced Integrated Climatic Model (EICM); development of empirical models to predict backcalculated pavement layer moduli
as a function of moisture content, stress state, and other explanatory variables; and trial application of the models developed to
prediction backcalculated moduli for unbound pavement layers.
This investigation yielded two key findings. First, it provided the impetus for developing EICM Version 2.6 by demonstrating the
practical inadequacies of EICM Versions 2.0 and 2.1 when applied to the prediction of in situ moisture content, and then
demonstrated that substantial improvement in the moisture predictive capability of the EICM had been achieved in Version 2.6.
Second, the research identified fundamental discrepancies between layer moduli backcalculated using linear-layered elastic
theory and the laboratory resilient modulus test conditions.
Other important findings included (1) variation in moisture content is not always the most important factor associated with
seasonal variations in pavement layer moduli, and (2) a model form that fits linear elastic backcalculated moduli reasonably well.
The overall accuracy of the modulus predictions achieved in the trial application of the predictive models was not fully acceptable.
Several avenues for further research to improve upon these results are identified.
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