Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement algorithm along with three data format conversion methods (aligned point cloud, voxel, and depth image) were proposed to preprocess the original collected point clouds. Subsequently, different neural network models were designed for each data format to extract gradation. Finally, a multi-feature fusion network was developed, which using extraction network as the backbone and additional auxiliary information. In the case study, the MAE loss of multi-feature fusion networks with PointNet, Vox-ResNet34 and GoogLeNet-v4 as the backbone respectively achieved 0.202, 0.142 and 0.046 on the test set, which means an estimation accuracy of more than 95% for the pavement aggregate gradation.

This Special Issue "Sustainable Designed Pavement Materials" has been proposed and organized as a means to present recent developments in the field of environmentally-friendly designed pavement materials. For this reason, articles included in this special issue relate to different aspects of pavement materials, from industry solid waste recycling to pavement materials recycling, from pavement materials modification to asphalt performance characterization, from pavement defect detection to pavement maintenance, and from asphalt pavement to cement concrete pavement, as highlighted in this editorial.

Most of previous studies optimize maintenance time window scheduling problem under a given train schedule, leading to a relatively poor quality of maintenance time window schedule, increasing the influence on traffic assignment. In order to reduce the negative effects on maintenance schedule and improve the utilization of railway resources, we consider integrating maintenance time window scheduling and train timetabling. In this way, more reasonable maintenance time window schedule can be obtained. We propose a mixed integer programming model and in particular we focus on the characteristics of the problem, including the speed limits affected by maintenance tasks on a double-track railway line. The benefits of the proposed integrated optimization model are demonstrated by numerical experiments.