Publication Detail

Analysis of Representative Volume Element for Asphalt Concrete Laboratory Shear Testing



UC Pavement Research Center

Suggested Citation:
Coleri, Erdem and John T. Harvey (2011) Analysis of Representative Volume Element for Asphalt Concrete Laboratory Shear Testing. Journal of Materials in Civil Engineering 23 (12), 1642 - 1653

The primary purposes of this paper are to develop a method of quantifying the precision and bias in repeated simple shear test at constant height (RSST-CH) laboratory test results for different-sized specimens and to determine the effects of this precision and bias on predicted rutting performance. The effect of RSST-CH variability was quantified by using a statistical sampling method called bootstrapping. The contribution of test variability to variability in predicted in situ rutting performance was determined by performing Monte Carlo simulations that used a shear-based incremental-recursive rutting analysis model. Results indicated that significant bias exists between the predicted rut depths of different specimen sizes. Increasing the specimen size decreased the test variability. Specimen size requirements for two different mix types are proposed on the basis of the analysis. The effect of test temperature on test results variability was also determined. In addition, analyzing various rutting performance parameters was used to determine the parameter that contained minimum size-related bias. Permanent shear strain at 5,000 repetitions appears to be an unbiased rutting performance evaluation parameter, when compared with other parameters, because it is not significantly affected by specimen size-related bias when three or more replicate tests are conducted. Analyses were performed by using RSST-CH results and a specific rutting model; however, the general procedure can be used to identify specimen size-related bias and precision for any type of laboratory test and distress model.


simple shear test; representative volume element; asphalt concrete; rutting; bootstrapping; Monte Carlo method; variability; bias