Reconstruction and evolution of 3D model on asphalt pavement surface texture using digital image processing technology and accelerated pavement testing

Abstract

Asphalt pavement surface texture is the main factor affecting pavement function. Reconstruction of the asphalt pavement surface texture is needed to accurately reveal its evolutionary characteristics for pavement performance and quality evaluation. To reconstruct an optimised 3D model of the asphalt pavement surface texture and to study its evolutionary properties, digital image processing technique and accelerated pavement testing system were used. First, the asphalt pavement surface texture 3D model optimised by three camera parameters and their thresholds. Next, the accelerated pavement testing system simulated traffic loadings on dense-gradation asphalt mixtures to investigate the pavement surface texture evolution properties. Finally, predictive models are developed for the asphalt pavement surface texture evolution. Results show that the optimised pavement texture 3D model resembles actual pavement structure. The surface texture evolutionary characteristics of asphalt pavement can be divided into three periods and six stages. The evolution model can accurately characterise the evolution of the surface texture of asphalt pavement.

Abbreviations: MLS11: Accelerated pavement testing system; H_P: Mean pixel difference; D_f: Fractal Dimension; MTD: Mean Texture Depth; BPN: British Pendulum Number

KEYWORDS:

• Asphalt pavement
• surface texture
• digital image processing technology
• accelerated pavement testing
• evolutionary characteristics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Fundamental Research Funds for the Central Universities, CHD under project No. 300102212906: [Grant Number 300102212906]; Innovation Capability Support Program of Shaanxi: [Grant Number 2022TD-07]; National Natural Science Foundation of China under project No. 51908460: [Grant Number No. 51908460].