A statistical analysis of the effect of confining pressure on deformation characteristics of HMA mixtures in the modified wheel track testing

Abstract

AbstractPermanent deformation in the form of rutting is a critical mode of failure observed in flexible pavements. While several research have been conducted to develop multiple tests for the characterisation of permanent deformation, little information is obtainable from the existing literature on how the factors or the interaction of the factors from these tests affect the permanent deformation behaviour in a simulative test, such as the wheel tracker. This research focuses on statistical analysis and ruggedness testing of the factors affecting the permanent deformation behaviour of asphalt mixes tested in the Modified Wheel Tracker (MWT). The analysis involved five factors in total, each at two levels. These factors are binder type, voids in total mix (VTM %), nominal maximum aggregate size, temperature, and confining pressure. The study utilised half-fractional factorial design in accordance with ASTM E1169-20. Significant parameters were determined through statistical analysis and regression models were proposed. The contour plots provided various combinations of the most significant factors for the corresponding responses. Based on the statistical analysis of the experiments conducted without any confinement, temperature, amongst all other factors, was found to impose the greatest effect on the permanent deformation behaviour based on vertical and horizontal FN Indices. Experiments with controlled confinement show that “confining pressure” is the most significant factor for the rutting parameters. The sensitivity analysis points out that to maintain the vertical deformation at 2000 cycles to be in the range ± 25% from the model, the confining pressure and temperature should be controlled within the range ± 25% and ± 5%. The use of the MWT shows that long-term rutting development can well be predicted from rutting at 2000 cycles using linear and exponential models.