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To further facilitate the automation and computational efficiency in asphalt mixture micromechanical simulation using finiteelement methods, this paper develops a three-dimensional (3D) microstructural modeling method for asphalt mixture to automatically generate the adaptive 3D finite-element mesh. The methodology contains three main steps, as follows: (1) two-dimensional (2D) enclosed outlines in every X-ray computed tomography (CT) image of the specimens are detected with shape features of outlines and grayscale discrepancy between each two different phase pixels; (2) for each independent geometric solid such as aggregate particle and air void in a specimen, the mapping between the solid and its outlines on various layers is established, so that the 3D model of each solid is constructed; and (3) models of the same phase are assembled into a specific substructure model. The specimen microstructure model is finally obtained with appropriate Boolean operations towards specific substructure models. Experiment results indicate that the specimen microstructure model not only has an excellent accuracy in modeling and simulation, but also supports adaptive meshing toward each specific substructure model, which significantly reduces the computational cost and can be applied to asphalt mixture numerical simulation. [ABSTRACT FROM AUTHOR]

The global scale of point cloud data (PCD) generated through monocular photography and videogrammetry is unknown and can be calculated using at least one known dimension of the scene. Measuring one or more dimensions for this purpose induces a manual step in the three-dimensional reconstruction process; this increases the effort and reduces the speed of reconstructing scenes, and induces substantial human error in the process due to the high level of measurement accuracy needed. Other ways of measuring such dimensions are based on acquiring additional information by either using extra sensors or specific classes of objects existing in the scene; it was found that these solutions are not simple, cost effective, or general enough to be considered practical for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue, this paper proposes a novel method for automatically calculating the absolute scale of built infrastructure PCD. A premeasured cube for outdoor scenes and a sheet of paper for indoor environments are used as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects' corner points in two-dimensional video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real-world scale. To evaluate the performance of the method, 10 indoor and 10 outdoor cases were selected and the absolute-scale PCD for each case was computed. Results illustrated the proposed algorithm is able to reconstruct the predefined objects with a high success rate, while the generated absolute-scale PCD is sufficiently accurate. [ABSTRACT FROM AUTHOR]