The method for accurate acquisition of pavement macro-texture and corresponding finite element model based on three-dimensional point cloud data

Abstract To establish the accurate pavement macro-texture finite element (FE) model used for tire-pavement interaction FE analysis, a novel processing and modelling procedure was proposed based on point cloud data obtained by the laser texture scanner. At first, drop-outs were removed using the linear interpolation technique, and a binary linear function was fitted to correct the pavement texture tilt. Besides, the spikes were detected and removed via bilateral test and linear interpolation based on asymmetric generalized Gaussian distribution. The effectiveness of proposed tilt correction and the spike detection method were verified based on the reference plate and the comparison with existing spike detection methods respectively. Subsequently, the Delaunay triangulation algorithm was employed to triangulate the processed point cloud data. Then, the reverse engineering modelling technique was used to generate the accurate three-dimensional (3D) pavement macro-texture FE model. To evaluate the effectiveness of the established FE model, a revised sand patch method was proposed, and the correlation between the calculated mean texture depth ( MT D ′ ) and the measured MTD was analyzed. The results show that the mean relative error between MT D ′ and the measured MTD is 5.97%, and the correlation coefficient R is equal to 0.9873, demonstrating that the processed point cloud data could characterize the actual pavement macro-texture precisely. Moreover, the generated pavement macro-texture FE model could be easily imported to Abaqus as well. Therefore, it could be concluded that the processing and modelling methods proposed in this study are effective for the establishment of 3D pavement macro-texture FE model.