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Pavement macro-texture analysis using wavelets

2012; Taylor & Francis; Volume: 14; Issue: 8 Linguagem: Inglês

10.1080/10298436.2012.705004

1477-268X

Habtamu Zelelew, A. T. Papagiannakis, Edgar David de León Izeppi,

Image and Signal Denoising Methods

This paper utilises a wavelet approach to interpret the macro-texture data collected on asphalt and concrete pavement surfaces with a wide range of macro-texture properties. The experimental data were obtained using a circular track meter (CTMeter) device on pavements built at the Virginia's Smart Road test facility. The size of the data-set allowed nine levels of wavelet decomposition with wavelengths ranging from 1.7 to 435 mm. The extent of macro-texture variation was summarised using the normalised wavelet energy metric defined as the sum of the squares of the detailed wavelet coefficients for the sub-bands that correspond to the macro-texture range of wavelengths divided by the length of the test section expressed in mm2/m. This metric was found highly correlated with the empirical mean profile depth measurements. Hence, the wavelet approach can be used to objectively analyse CTMeter measurements of pavement texture.