Prediction of tire–pavement friction based on asphalt mixture surface texture level and its distributions

Abstract

Tire–pavement friction plays a key role in traffic safety. With the development of auto vehicle industry, most of the new vehicles are equipped with Anti Braking System (ABS). Therefore, a prediction model representing the braking process of vehicles equipped with ABS is deemed necessary. In this paper, the tire–pavement friction is measured by Dynamic Friction Tester (DFT) and Hand Friction Tester. The surface texture of asphalt pavement is acquired using a recently developed programme named as 2-Dimensional Image Texture Analysis Method (2D-ITAM). Tire–pavement friction at optimum design slip speed corresponding to the maximum tire–pavement friction is calculated with a widely used model. Then a prediction model correlating the tire–pavement friction at optimum design slip speed with the macro-texture and micro-texture of pavement is established using multivariate non-linear regression analysis. This prediction model is validated through laboratory test indicating its effectiveness of predicting the tire–pavement friction. The model is anticipated to be an improved tool which can be considered by practitioners in an optimised asphalt mixture design including the evaluation of skid resistance of pavement.

Keywords:

• asphalt pavement
• tire–pavement friction
• pavement surface texture
• image texture analysis method
• prediction model

Acknowledgements

These supports are gratefully acknowledged. Authors would also like to acknowledge the contributions from Professor Hussain U. Bahia, Professor Massimo Losa and Dr. Cheng Ling and Dr. Nima Roohi Sefidmazgi. The results and opinions presented are those of the authors and do not necessarily reflect those of the sponsoring agencies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was sponsored by the National Natural Science Foundation of China (Grant No. 51808462 and 51578076), the Research Fund of “State Key Laboratory of Rail Transit Engineering Informatization (FSDI) (grant number SKLK2018-09)”, the Research Fund of “China Railway Siyuan Survey and Design Group Co., LTD.” (grant number 2018H0580), the Research Fund of “Key Laboratory for Special Area Highway Engineering of Ministry of Education” (grant number 310821171103), and the Fundamental Research Funds for the Central Universities (grant number 2682016CX009).