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To ensure the safety of tunnel construction, it is necessary to accurately and timely measure the deformation of the shotcrete liner during the management of the construction process. The light detection and ranging (LiDAR) technique, known as the reality reproduction technology, provides a new solution for deformation measurement. However, the nonsmooth defects of shotcrete liner and the scattered noise points caused by the nearby dust, as well as a large number of nontunnel structures such as pipes, construction machinery, and vehicles in the obtained tunnel point cloud datasets, hinder the application of point clouds in deformation measurement of shotcrete liner. In this paper, a ring simulation sampling (RSS) method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds is proposed. This method constructs the virtual ring according to shape of the tunnel cross section and emits the rays from the ring to the center of each tunnel cross section. The length difference of the rays arriving at the outer wall of the multiepoch tunnel point clouds is calculated to achieve the analysis of overall and local deformations of the shotcrete liner. The shotcrete liner point clouds generated by the manual modeling and terrestrial laser scanning are tested to validate the effectiveness of the method in extracting the deformation of shotcrete liner. Compared with the conventional total station monitoring, the mean absolute deviation and root mean square error are 1.39 and 1.93 mm, respectively, which indicates that the RSS method could be effectively applied to the deformation measurement of tunnel shotcrete liner. Deformation measurement of tunnel shotcrete liner is crucial to safety of tunnel construction and implementation of dynamic parameter design. This study recommends the light detection and ranging (LiDAR) technique. The main work of this paper is to determine whether the tunnel shotcrete lining has been deformed as a whole or locally by comparing the obtained multiepoch high-precision 3D point clouds. Furthermore, the authors propose a method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds. Compared with conventional deformation measurement methods (i.e., the total station and fiber optics) that capture deformation information from a limited number of monitoring points, LiDAR technology enables the high-precision, omnidirectional, and noncontact acquisition of spatial data within tunnels. This serves as a complementary and enhanced tool to tunnel safety monitoring techniques and methodologies. In addition, the adoption of the method proposed in this paper facilitates the quality check of shotcrete liner, including thickness assessment and smoothness detection. [ABSTRACT FROM AUTHOR]

Shake-table tests of a full-scale 3-story reinforced concrete frame building were performed at the E-Defense facility (Miki City, Japan) as part of the Tokyo Metropolitan Resilience Project. Building data were collected before and after each shake-table experiment, using light detection and ranging (lidar) scanning technology. Three-dimensional point clouds were generated and processed to investigate the damage features that can be extracted, which are important for postdisaster structural assessment purposes. To this end, this paper discusses aspects pertaining to lidar data processing and registration, calculation of residual displacements and interstory drifts, and concrete cracking and spalling identification and measurement. Most notably, this paper deals with very large data sets, processed to extract structural response information at both global and local scales. Limited multiscale work has been done in the past, and the different sets of challenges that arise have typically been addressed separately. Evidently, this gap needs to be filled before lidar can be effectively used in the field for structural assessment applications. A series of examples in which lidar data are processed and analyzed to assess specific structural components is illustrated, showing that point cloud data can indeed be processed to obtain measurements of global residual drifts or of the extent of visible damage (e.g., crack width and spalling). Although preliminary in nature, the results presented in this paper indicate that collecting postdisaster data using terrestrial lidar scanners has the potential to contribute improving how buildings are assessed in the aftermath of seismic events. [ABSTRACT FROM AUTHOR]

Due to the progress in light detection and ranging (LiDAR) technology, the collection of road point cloud data containing depth information and spatial coordinates has become more accessible. Consequently, utilizing point cloud data for pavement distress detection and quantification emerges as a crucial approach to improving the precision and reliability of road maintenance procedures. This paper aims to automatically detect and visualize pavement distress using LiDAR, deep learning-based 3D object detection method, and building information modeling (BIM). A pavement distress data set is first established using the point cloud data obtained from LiDAR. Then, the 3D object detection network, namely PointPillar, is employed for pavement distress detection, and the detection results will be quantified at a region-level. Finally, pavement BIM model integrating parametrically modeled distress families is built to visually manage the detected distress. After training and validating the model with the pavement distress data set, a detection performance index of recall is 78.5%, mean average precision (mAP) is 62.7%, which is better than other compared point cloud-based methods though the detection performance can be further improved. In addition, a newly untrained section of road is applied for the experiment. The detected distress is integrated in BIM environment for a visual management, providing a better maintenance guidance. [ABSTRACT FROM AUTHOR]