Many parameters associated with asphalt concrete pavement can be utilized to establish its capability of catering to an adequate service life, good driving quality, and, most importantly, the safety of vehicular operators. One of the main parameters of interest associated with asphalt concrete is its frictional performance. Improved pavement frictional performance has been linked to a proportional increase in road safety. From the factors governing pavement friction, some can be characterized while others are particularly challenging to control and measure. Some challenging factors include but are not limited to temperature, speed, tire wearing, evenness and curviness, pavement surface texture, and tire properties. Two main factors of interest in relation to friction are surface texture and tire properties. These two factors have been utilized to quantify the frictional performance of the pavement through a contactless approach. Several studies have been dedicated to utilizing 3D scanners to study and characterize the surface texture in multiscale and correlated it to the pavement friction coefficient utilizing multiscale rubber friction models. However, the rubber friction model is not easily comprehended due to its complex nature. Thus, to bring the 3D laser surface texture friction evaluation into practical use, a simplified rubber friction model would be needed. Furthermore, the contact between the tire and pavement relies on the characterization of the pavement surface through a Power Spectral Density (PSD). The PSD yields key parameters, such as wavelengths, Hurts exponent, and surface roughness value; however, little study has shown a systematic and practical approach to determining these essential parameters. Therefore the primary objective of this research is to derive a simplified rubber pavement friction based on Persson's multiscale rubber friction model. In addition, an attempt was made to investigate the influence of different procedures for point cloud data analysis and extracting model parameters from PSD. In the research, twelve (12) cylindrical asphalt concrete samples in the laboratory to be used for data collection. A friction value, coefficient of friction, was acquired from the surface of the 12 samples. The coefficient of friction was measured using a British Pendulum Tester (BPT), and a 3D handheld laser scanner was utilized to quantify the surface texture of each sample. Spatial Filtering techniques were applied to point cloud data to enhance the image and further construct the PSD. Various procedures to analyze the PSD, to identify essential model parameters, were performed. The identified parameters were used in the simplified rubber friction model to calculate the friction coefficient of asphalt concrete. Statistical analysis was performed between the measured coefficient of friction and the predicted coefficient of friction. In the data processing stage, it was found that the right combination of spatial filtering techniques is vital in constructing the PSD. The best filtering technique was first to implement a linear filter, followed by a Gaussian filter, then median filtering, and finally wavelet denoising. The study also revealed that essential PSD parameters, such as the roll-off wavenumber, can be acquired using a trend line. The data point of interest is where the PSD deviates drastically from the trendline. The surface roughness value is also obtained from the data point, which corresponds to the roll-off wavenumber. The highest wavenumber measured by the 3D scanner can be used as the cutoff wavenumber. Lastly, a simplified rubber friction model has developed that correlated a predicted Coefficient of friction with the BPT-measured coefficient of friction, giving an R2 of 0.5. This result could be due to the scanner used in this study that measures macrotexture and partial microtexture surface data. The BPT also simulates the low-speed scenario where the COF is dominated mainly by the microtexture.

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