01593294

Component

https://doi.org/10.3141/2525-10

This paper proposes a new model for predicting the speed gradient of peak friction values on asphalt pavements on the basis of surface characteristics. The innovative feature of the proposed model is the reliable estimation of peak friction values experienced by vehicles equipped with an antilock brake system at a certain vehicle speed. To define the experimental model, several types of dense asphalt concrete surface layers with various surface characteristics were analyzed by in situ tests. Friction was measured with the Skiddometer BV11 and the British pendulum tester, and texture properties were measured with a laser profilometer. The Rado model was used to predict peak friction values at three vehicle speeds, and these data were used to determine the gradient of peak friction values for each pavement section. The spectral analysis of pavement profile data was used to define a texture parameter negatively correlated with peak friction values; this parameter was introduced in a new formulation of the speed number S*p that was a measure of the influence of pavement macrotexture on peak friction values. The speed number S*p was used in the new exponential model proposed for defining the gradient of peak friction values. The results show that the model is highly reliable; because the model allows identification of texture characteristics to be modified to optimize peak friction values, it is particularly useful for optimization of the mix design and maintenance of pavement surfaces.

01592675

15-4127

English

pp 91–99

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369497

Print

Figures (8) ; References (18) ; Tables (3)

Asphalt concrete pavements; Friction; Mix design; Profilometers; Speed; Texture

Highways; Pavements

TRIS, TRB, ATRI

Dec 30 2014 1:21PM

• Adaptive Spike Removal Method for High-Speed Pavement Macrotexture Measurements by Controlling the False Discovery Rate
• Backcalculation with an Implanted Inertial Sensor
• Detecting and Correcting Localized Roughness Features
• Dynamic Viscoelastic Analysis of Falling Weight Deflectometer Deflections for Rigid and Flexible Pavements
• Evaluation and Validation of a Model for Predicting Pavement Structural Number with Rolling Wheel Deflectometer Data
• Importance of Rubber Characteristics in the Frictional Response of Asphalt Concrete Surfaces
• Measurement of Pavement Treatment Macrotexture and Its Effect on Bicycle Ride Quality
• Monitoring Pavement Surface Macrotexture and Friction: Case Study
• Performance Evaluations of Unbound Aggregate Permanent Deformation Models for Various Aggregate Physical Properties
• Roughness-Induced Pavement–Vehicle Interactions: Key Parameters and Impact on Vehicle Fuel Consumption
• Study of Influence of Operating Parameters on Braking Friction and Rolling Resistance