Displacement mapping of point clouds: application of retaining structures composed of sheet piles

Abstract

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.

Multi-Label Classification and Automatic Damage Detection of Masonry Heritage Building through CNN Analysis of Infrared Thermal Imaging

“…Recently, studies on detecting structural displacement and damage based on 3D point cloud data have been conducted [28,29]. Structure stability analysis of 3D data enables damage detection in extensive areas of structures [30][31][32]. However, it does not have high accuracy as a monitoring technique because of errors in the data acquisition process [33][34][35].…”

Automatic Pavement Defect Detection and Classification Using RGB-Thermal Images Based on Hierarchical Residual Attention Network

“…This study also has limitations in that it is not possible to inspect all pavements at the national level. 3D laser scanning has also been used recently to detect the displacement and damage of infrastructure [ 8 , 9 , 10 , 11 , 12 , 13 ]. Zhao et al, (2022) used a machine learning algorithm to detect the movement of the retaining structure [ 14 ].…”

Crack Detection in Frozen Soils Using Infrared Thermographic Camera

“…Recently, deep learning and machine learning analyses were applied to find a crack in infrastructures [ 14 , 15 ]. Moreover, the three-dimensional monitoring system was applied for monitoring the displacement and tilt of infrastructure using laser scanning [ 16 ]. In this paper, an experimental study was conducted by simulating frozen soils.…”