Surface Texture Reconstruction and Mean Texture Depth Prediction Model of Asphalt Pavement

To achieve the desired pavement mean texture depth by adjusting the gradation design of an asphalt mixture, high-precision three-dimensional laser scanning technology was used to collect the surface texture feature information of three typical gradation asphalt mixture specimens: Asphalt Concrete, Stone Matrix Asphalt, and Open Graded Friction Course. After the exception values and outliers were processed by neighborhood interpolation and the sampled data was denoised by mean filtering, the sample surface was reconstructed in three dimensions. A band-pass filter was designed according to the frequency corresponding to the wavelength of the macro texture based on the frequency domain information of the reconstructed surface obtained by the Fourier transform; the macro texture of the pavement was separated and extracted from the reconstructed surface. A Monte Carlo algorithm was used to calculate the mean texture depth of the pavement. The influence of the mixture particle size and passing rate of the sieve on the mean texture depth was considered by using the product of the mass ratio on the sieve and particle size. The prediction models of the product of the mass ratio on sieve and particle size, and the mean texture depth were established using multiple linear regression analysis, random forest, and artificial neural networks; the influence of the mixture gradation on the mean texture depth of the asphalt pavement was studied. The results show that mean filtering not only removes the noise signal but also retains the elevation profile features. The surface features of the three-dimensional reconstructed specimen are consistent with the original surface features. The mean texture depth is affected by other particle sizes in the grading curve, except for the maximum nominal particle size and passing rate of the sieve. The regression model was established using multiple linear regression, random forest, and an artificial neural network, which takes the product of the mass ratio and particle size on the sieve of each mesh size as the independent variable and the mean texture depth as the dependent variable, has an R2 of more than 0.95. © 2023 Xi'an Highway University. All rights reserved.