Steel slag (SS) is regarded as a potentially promising alternative material for road construction, which could ameliorate the shortage of high-quality aggregate and environmental pollution of SS accumulation simultaneously. However, except for some developed countries, the comprehensive utilization rate of SS in developing countries is particularly limited, especially in road construction. This is attributed to the concerns and uncertainties of environmental and mechanical properties caused by the incorporation of SS into pavement. Therefore, a systematic literature review is conducted to explore the status quo and challenges of SS application in road domain to help develop appropriate utilization strategies. Thanks to the excellent antiabrasion, rich angularity, rough texture, and high hardness, SS aggregate possesses favorable interlocking capability that can be utilized in subgrade, base, and surface course. Additionally, when used in surface, it can be considered as a functional material, for instance for antiskidding, noise reduction, and conductive concrete for crack healing and ice melting. The current investigations are still widely interested in performance verification and expansion treatment, but lack relevant mechanism-based analysis. Further rational studies are required in terms of gelation activation technology, adhesion injury, mesoscale skeleton optimization, evaluation of healing efficiency, and long-term performance. Moreover, the environmental assessments including heavy metal and leachate contaminations are necessary for the adoption of SS in pavement.

Road geometric information and a digital model based on light detection and ranging (LiDAR) can perform accurate geometric inventories and three-dimensional (3D) descriptions for as-built roads and infrastructures. However, unorganized point clouds and complex road scenarios would reduce the accuracy of geometric information extraction and digital modeling. There is a standardization need for information extraction and 3D model construction that integrates point cloud processing and digital modeling. This paper develops a framework from semantic segmentation to geometric information extraction and digital modeling based on LiDAR data. A semantic segmentation network is improved for the purpose of dividing the road surface and infrastructure. The road boundary and centerline are extracted by the alpha-shape and Voronoi diagram methods based on the semantic segmentation results. The road geometric information is obtained by a coordinate transformation matrix and the least square method. Subsequently, adaptive road components are constructed using Revit software. Thereafter, the road route, road entity model, and various infrastructure components are generated by the extracted geometric information through Dynamo and Revit software. Finally, a detailed digital model of the road scenario is developed. The Toronto-3D and Semantic3D datasets are utilized for analysis through training and testing. The overall accuracy (OA) of the proposed net for the two datasets is 95.3 and 95.0%, whereas the IoU of segmented road surfaces is 95.7 and 97.9%. This indicates that the proposed net could accomplish superior performance for semantic segmentation of point clouds. The mean absolute errors between the extracted and manually measured geometric information are marginal. This demonstrates the effectiveness and accuracy of the proposed extraction methods. Thus, the proposed framework could provide a reference for accurate extraction and modeling from LiDAR data.

X-ray computed tomography (CT) as a non-destructive testing method has been used to evaluate the air voids and void-related distresses in asphalt mixtures. To evaluate the air void development in asphalt mixtures under loading, this study proposed a volume-equivalent maximal ball model based on CT to characterise the changes of void connectivity. The void distribution and shape features were described using the variation coefficient of modified annular-sector segmentation, positional eccentricity ratio, and sphericity. The results indicate that voids are relatively heterogeneous and discrete in stone mastic asphalt (SMA-13) mixture compared with dense-graded asphalt concrete (AC-13), particularly for those with volume smaller than 0.01 mm³. The void connectivity and distribution in SMA-13 seem to be more susceptible to load as its coarse aggregates tend to shift their positions during deformation. Rutting reduces the vertical inhomogeneity of AC-13 but increases that of SMA-13. Moreover, loading complicates the void geometry of AC-13 whereas the opposite is true for SMA-13, foreboding that SMA mixtures maintain the potential to resist rutting failure after 1 h of loading compared to AC types. In general, the topological characteristics of air voids within asphalt mixtures and their dynamic response during permanent deformation are principally dependent on mixture gradations.