In this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

Point clouds were obtained after laser scanning of the concrete panel, SMW, and sheet pile which is most widely used in retaining structures. The surface condition of the point cloud affects the displacement calculation, and hence both local roughness and global curvature of each point cloud were analyzed using the different sizes of the kernel. The curvature of the three retaining structures was also analyzed by the azimuth angle. In this paper, artificial displacements are generated for the point clouds of 100%, 80%, 60%, 40%, and 20% of the retaining structures, and displacement and analysis errors were calculated using the C2C, C2M, and M3C2 methods. C2C method is affected by the resolution of the point cloud, and the C2M method underestimates the displacement by the location of the points in the curvature of the retaining structures. M3C2 method had the lowest error, and the optimized M3C2 parameters for analyzing the displacement were presented.

Point cloud registration is the process of aligning point clouds collected at different locations of the same scene, which transforms the data into a common coordinate system and forms an integrated dataset. It is a fundamental task before the application of point cloud data. Recent years have witnessed the rapid development of various deep-learning-based global registration methods to improve performance. Therefore, it is appropriate to carry out a comprehensive review of the more recent developments in this area. As the developments require access to large benchmark point cloud datasets, the most widely used public datasets are also reviewed. The performance of deep-learning-based registration methods on the benchmark datasets are summarized using the reported performance metrics in the literature. This forms part of a critical discussion of the strengths and weaknesses of the various methods considered in this article, which supports presentation of the main challenges currently faced in typical global point cloud registration tasks that use deep learning methods. Recommendations for potential future studies on this topic are provided.

In this paper, a three-dimensional laser scanner was used to monitor the displacement of retaining structures for excavation, including a ring beam and a reinforced soil mixing wall (SMW) at an open excavation site. Eight scans of the retaining structures were taken before and after the excavation. Three-dimensional point clouds of the retaining structures produced with these scans are registered and analyzed to determine displacements of the retaining structures. Cloud to Mesh (C2M) method is used to identify displacement along the length of ring beam and depth of the SMW. The displacement obtained is then validated against displacement measured by the total station. The surface of the ring beam is flat so that the displacement of the ring beam can be estimated by the C2M. There was soil collapse and deposition on the surface of the SMW, which can affect the estimation of displacement in the cloud comparison. Therefore, a CANUPO machine-learning algorithm is applied to detect these areas and to remove the point clouds affected by soil collapse and deposition. The displacement is re-estimated based on the revised point clouds. The re-estimated displacement by the CANUPO method is more uniformly changed with the depth of the SMW than results without the CANUPO method, and the horizontal displacement due to excavation can be calculated.

The measurement of displacement is an important factor to evaluate the stability of a retaining structure. In this paper, a large-scale retaining structure with a width of 70 m and a height of 6 m was monitored using 3-D laser scanning. Displacement mapping was proposed to globally monitor the entire retaining structure. The point cloud obtained immediately after the excavation was converted into the mesh, and the point clouds obtained on the second and seventh days after excavation were compared to the mesh using the Cloud to Mesh (C2M) comparison method. Since the C2M displacement can be underestimated in the inclined section of the sheet pile, after filtering using the azimuth and element angles automatically, only the flat sections of the sheet pile can be segmented from original point clouds collectively and consistently. The displacement mapping results identify not only the local behavior of the sheet pile, such as the bending point and the maximum displacement position, but also the global behavior, such as the expansion of the maximum displacement in the retaining structure. The results of the displacement mapping were confirmed through site investigation as well. In the displacement mapping result, the maximum displacement was found around rows 2 and 3 of the 37th sheet pile, and the analyzed result of the load cell installed on the anchor indicated that the anchor constructed in the 37th sheet pile shows the plastic behavior. It was established that other sheet piles are affected by the damage to the local anchor through the H-beam and the maximum displacement in the displacement mapping is expanded horizontally in a positive parabola shape. Therefore, it was confirmed that displacement mapping using laser scanning can complement existing monitoring techniques and can contribute to evaluating the behavior of a large-scale retaining structure during excavation.

Frozen soils are encountered on construction sites in the polar regions or regions where artificial frozen ground (AFG) methods are used. Thus, efficient ways to monitor the behavior and potential failure of frozen soils are currently in demand. The advancement of thermographic technology presents an alternative solution as deformation occurring in frozen soils generate heat via inter-particle friction, and thus a subsequent increase in temperature. In this research, uniaxial compression tests were conducted on cylindrical frozen soil specimens of three types, namely clay, sand, and gravel. During the tests, surface temperature profiles of the specimens were recorded through an infrared video camera. The thermographic videos were analyzed, and subsequent results showed that temperature increases caused by frictional heat could be observed in all three frozen soil specimens. Therefore, increases in temperature can be deemed as an indicator for the potential failure of frozen soils and this method is applicable for monitoring purposes.