Study of Texture Indicators Applied to Pavement Wear Analysis Based on 3D Image Technology

Area of Science:

• Pavement Engineering
• Materials Science
• Geotechnical Engineering

Background:

• Traditional statistical texture indicators inadequately capture pavement performance nuances.
• Pavement texture is crucial for assessing early performance decay and skid resistance.

Purpose of the Study:

• To develop and validate advanced texture indicators for pavement analysis using 3D imaging.
• To assess the correlation and differentiation capabilities of various texture indicators.

Main Methods:

• Utilized 3D image cameras for laboratory and field pavement texture data acquisition.
• Applied pre-processing techniques including standardization, slope correction, and outlier removal.
• Calculated texture indicators via texture separation, power spectrum, GLCM, and fractal theory.

Main Results:

• Identified high correlations between energy (ENE), entropy (ENT), and Minkowski dimension (D).
• Contrast (CON) showed low correlation with macro-texture power spectrum area (HT), ENT, and D.
• Macro-texture indicators ENE, ENT, CON, and D demonstrated strong linear correlations with original texture.

Conclusions:

• Advanced texture indicators like D, WT, and ENT show promise for statistically distinguishing pavement performance decay.
• Micro-texture indicators exhibited weaker linear correlations with original texture data.
• The developed methods offer enhanced capabilities for pavement condition assessment.