This research conducts a quantitative analysis on the macrotexture of asphalt concrete pavement based on three-dimensional (3D) point cloud data. A binocular stereovision-based 3D point cloud data collection system is developed. The system is composed of a packaged component that includes a lighting source and two cameras, a dark shading cloth, and the computer control side with the configuration of the operation interface. Meanwhile, specimens of both asphalt concrete and open graded friction course (OGFC) are prepared as the test specimens. Next, 3D point cloud data of the specimens are collected using the proposed system. The macrotexture information is then extracted using the robust Gaussian method. The macrotextures of the pavement surface are characterized by 10 indicators; profile arithmetic average deviation, profile root mean square difference, mean texture depth, profile skewness value, profile steepness, profile unevenness distance, profile peak distance, profile root mean square slope, profile root mean square wavelength, and surface roughness area ratio. At the same time, the friction coefficients of these specimens are measured by British Pendulum Number. Finally, the correlations between each indicator and the friction conditions of different specimens are assessed. Results demonstrate that the proposed macrotexture indicators are reliable for evaluating the friction conditions because significant correlations have been observed. Meanwhile, the correlations for the OGFC gradations are always higher than the asphalt concrete gradations. All the findings prove that the proposed quantitative indicators are effective for the characterization of the macrotexture of asphalt concrete pavement.
In this paper, a new method based on circle and linear projection is proposed. In a straight line and a circle on the target space as the main object recognition. In the case of knowing the radius of the space circle and coordinate of a point on the space line: First of all, we can use the space circle projection to solve all possible solutions, there are two groups. Secondly, determine the rotation angle of space circle around normal vector in the two groups by using the relative position relation of space straight line and circle. Then, we can get the rotation relation between the object coordinate system and the camera coordinate system and get the coordinate representation of circle and straight line in the camera coordinate system in two groups of solutions. Finally, project the point on the space line onto the image plane in two groups of solutions. In the correct solution, the distance between the projection point and the projection line is small. The experimental results show that the method can find the correct solution in all solutions and determine the rotation angle of the space circle around the normal vector. The method can get the correct result under the condition of noise, so the method has strong robustness.
To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.
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