Volume 37, Issue 1 (January 2009)
Field Calibration of an Analytical Model for Pavement Friction Testing Applications
Regular evaluation and maintenance of skid resistance (friction) of highway and runway pavements is an essential task in improving transportation safety. Various measuring devices operating under different mechanisms have been developed for friction evaluation. Generally, there is a significant variation of friction measurements provided by different devices on the same surface, requiring reliable guidelines for comparison of devices. This paper presents the results of a study conducted to verify whether the LuGre model, an existing dynamic friction model composed of physically significant parameters, would satisfy the above need. A revised formulation which would enhance the applicability of the average lumped version of the LuGre model to model real-world dynamic friction problems is also presented. The original LuGre model was applied to two widely used pavement friction measuring devices; the locked wheel skid trailer and the dynamic friction tester (DFT). In the work presented here the characteristic equations for each device have been formulated based on the LuGre model. In addition an analytical treatment of the kinetics of the DFT is performed to facilitate the application of the LuGre model to the DFT. For each device, the LuGre model parameters were tuned to match a part of the field data collected during a field test carried out by the authors. It was seen that the obtained parameters are reasonably comparable with those reported in the literature. Moreover, the calibrated model equations were seen to predict the observed data that were not used in model fitting, with acceptable accuracy. The results obtained in this study clearly show that the LuGre model can be utilized as an efficient physically intuitive tool with possible applications in: (1) predicting the three-dimensional variation of friction (with wheel slip and traveling speed) as measured by selected friction measuring devices, (2) providing physical interpretations to friction data from different devices, (3) regular self-calibration of friction devices, and (4) comparing pavement friction measurements from different devices.