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Browsing by Author "Mogrovejo Carrasco, Daniel Estuardo"

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    Effect of Air Temperature, Vehicle Speed, and Pavement Surface Aging on Tire/Pavement Noise Measured with On-Board Sound Intensity Methodology
    Mogrovejo Carrasco, Daniel Estuardo (Virginia Tech, 2013-02-01)
    The study of the traffic noise as an environmental impact, the search for solutions to this problem, and the development of noise measurement methodologies that help in the search of these solutions, is now a fundamental responsibility for the transportation industry.   So, in line with this responsibility, consistent work was made with focus on tire/pavement noise measured over different pavement surfaces, and under different environmental conditions, and different speeds. In a parallel way, work was conducted for the development, improvement, and practical use of the On- Board Sound Intensity (OBSI) methodology for tire/pavement noise measurements.   The first part of this thesis shows the results of field experimentation about the influence of external factors like air temperature and vehicle speed over the tire/pavement noise measured with the OBSI methodology. Temperatures from 40 to 90"F were targeted, and speeds from 35 mph to 60 mph (range in which tire/pavement noise becomes predominant for the overall vehicle noise) were tested. For this work a series of seasonal field tests were conducted on a primary road in Virginia over various months.  The results were analyzed to quantify the variation of tire/pavement noise with respect to the air temperature and test speed, and therefore to find correction factors for this variables in order to normalize the data taken under different conditions. In the second part of this thesis, the study of tire/pavement noise over different surfaces and measured over a timeframe of three seasons is presented. This part presents results about noise reduction potentials of two proposed "quiet" concrete technologies and 3 proposed "quiet" asphalt surfaces when compared with one another, and with control sections. Also the second part of the thesis includes results about the susceptibility of the proposed surfaces to external factors such as: aging (three seasons involved), air temperature differentials and winter maintenance. In general, the findings show trends that tire/pavement noise slightly decreases as air temperature increases. A gradient of approximately -0.05 dBA/"F was found. It was found as well that tire/pavement noise increases an average of 2.5 dBA for every 10 mph of increased speed.   The statistical analysis results for the second part of the thesis shows that all proposed concrete surfaces and asphalt surfaces present benefits in terms of noise reduction, For the asphalt surfaces, it was found that more voids in the surface helps to absorb the noise. In addition, the rubber modified mixes show an improved noise reduction potential. Air temperature normalization was performed an a statistical analysis was conducted; it was found that air temperature has a significant influence in the noise measurements especially for the first months of use. Finally it was found that there is a slightly increase in noise over time after the first months of use.
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    Enhancing Pavement Surface Macrotexture Characterization
    Mogrovejo Carrasco, Daniel Estuardo (Virginia Tech, 2015-04-30)
    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.