Prediction of asphalt mixture surface texture level and its distributions using mixture design parameters

ABSTRACT

Pavement skid resistance plays a key role in traffic safety. Meanwhile, tire-pavement noise is a major source of traffic noise in urban areas. Current asphalt mixture design methods, however, mostly focus on volumetric and mechanical properties and pay little attention to the skid resistance and noise reduction performance of asphalt mixtures, which are significantly affected by the surface textures of asphalt mixtures. Incorporating the evaluation of surface texture into the mixture design would aid in a more rational selection of materials considering both mechanical and functional properties of asphalt mixtures. In this paper, the surface texture properties of several types of asphalt mixtures are measured using a recently developed 2-Dimensional Image Texture Analysis Method. A prediction model correlating the mixture surface texture levels at different central texture wavelength in octave band with the important mix design parameters is established using a multivariate non-linear regression analysis. The model is validated through laboratory test and imaging measurement indicating its capability of predicting the level and distributions of mixture surface texture. The prediction model is anticipated to provide a basis of optimised mixture design considering the skid resistance and noise reduction performances of asphalt pavement.

KEYWORDS:

• Surface texture
• mixture design parameters
• image texture analysis method
• prediction model

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Acknowledgements

This research was sponsored by The National Natural Science Foundation of China under Grant No. 51578076 and 5178467; the Fundamental Research Funds for the Central Universities under Grant No. 2682016CX009; and the Research Fund of “Key Laboratory for Special Area Highway Engineering of Ministry of Education” under Grant No. 310821171103. These supports are gratefully acknowledged. The authors also would like to acknowledge the contribution to Professor Hussain Bahia and technical support by Modified Asphalt Research Center in the University of Wisconsin-Madison.

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Funding

This work was supported by The National Natural Science Foundation of China [grant number 51578076], [grant number 5178467]; the Fundamental Research Funds for the Central Universities [grant number 2682016CX009]; and the Research Fund of “Key Laboratory for Special Area Highway Engineering of Ministry of Education” [grant number 310821171103].