Enhancing Pavement Surface Macrotexture Characterization

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Virginia Tech

One of the most important objectives for transportation engineers is to understand pavement surface properties and their positive and negative effects on the user. This can improve the design of the infrastructure, adequacy of tools, and consistency of methodologies that are essential for transportation practitioners regarding macrotexture characterization. Important pavement surface characteristics, or tire-pavement interactions, such as friction, tire-pavement noise, splash and spray, and rolling resistance, are significantly influenced by pavement macrotexture.

This dissertation compares static and dynamic macrotexture measurements and proposes and enhanced method to quantify the macrotexture. Dynamic measurements performed with vehicle-mounted lasers have the advantage of measuring macrotexture at traffic speed. One drawback of these laser devices is the presence of 'spikes' in the collected data, which impact the texture measurements. The dissertation proposes two robust and innovative methods to overcome this limitation.

The first method is a data-driven adaptive method that detects and removes the spikes from high-speed laser texture measurements. The method first calculates the discrete wavelet transform of the texture measurements. It then detects (at all levels) and removes the spikes from the obtained wavelet coefficients (or differences). Finally, it calculates the inverse discrete wavelet transform with the processed wavelet coefficients (without outliers) to obtain the Mean Profile Depth (MPD) from the measurements with the spikes removed. The method was validated by comparing the results with MPD measurements obtained with a Circular Texture Meter (CTMeter) that was chosen as the control device.

Although this first method was able to successfully remove the spikes, it has the drawback that it depends on manual modeling of the distribution of the wavelet coefficients to correctly define an appropriate threshold. The next step of this dissertation proposes an enhanced to the spike removal methodology for macrotexture measurements taken with high-speed laser devices. This denoising methodology uses an algorithm that defines the distribution of texture measurements by using the family of Generalized Gaussian Distributions (GGD), along with the False Discovery Rate (FDR) method that controls the proportion of wrongly identified spikes among all identified spikes. The FDR control allows for an adaptive threshold selection that differentiates between valid measurements and spikes. The validation of the method showed that the MPD results obtained with denoised dynamic measurements are comparable to MPD results from the control devices. This second method is included as a crucial step in the last stage of this dissertation as explained following.

The last part of the dissertation presents an enhanced macrotexture characterization index based on the Effective Area for Water Evacuation (EAWE), which: (1) Estimates the potential of the pavement to drain water and (2) Correlates better with two pavement surface properties affected by macrotexture (friction and noise) that the current MPD method. The proposed index is defined by a three-step process that: (1) removes the spikes, assuring the reliability of the texture profile data, (2) finds the enveloping profile that is necessary to delimit the area between the tire and the pavement when contact occurs, and (3) computes the EAWE. Comparisons of current (MPD) and proposed (EAWE) macrotexture characterization indices showed that the MPD overestimates the ability of the pavement for draining the surface water under a tire.

Pavement Surface Properties, Macrotexture Characterization, High Speed Laser Device, Spike removal, False Discovery Rate, Effective Area for Water Evacuation.