###### Abstract  
The objective of this study is to develop a monitoring algorithm that measures the displacement of concrete structures using light detection and ranging (LiDAR). The suggested method is based on non-contact measurements providing 3D point clouds of the scanning area with high resolution. This overcomes the limitation of traditional contact-type and point-based measurement methods such as linear variable differential transformer (LVDT) and strain gauge. The developed algorithm enables one to track the boundaries of a concrete specimen and measures the vertical or lateral displacement. To demonstrate that displacement in the horizontal and vertical direction can be measured irrespective of the field of view (FOV), two different concrete specimens were constructed where gradually increasing vertical or lateral loads were applied. Then, the displacements were monitored using the set of LVDT and LiDAR for the correlation analysis. The results demonstrated a high accuracy of 98-99% correlation in comparison between LVDT and LiDAR. 3D point clouds; light detection and ranging (LiDAR); structural health monitoring (SHM); concrete +
Footnote †: journal:  
A light detection and ranging (LiDAR) instrument emits a laser pulse to a target, measures the return time, and converts these data into point clouds represented as three-dimensional coordinate values. LiDAR is a contactless scan method, but it is often used in various fields due to its high resolution and short scan time. LiDAR is primarily used for perception and mapping. It helps a vehicle understand its surroundings by creating a detailed 3D representation of the environment, detecting objects (such as other vehicles, pedestrians, and obstacles), and providing depth information for localization and navigation. In the case of crack detection in concrete surfaces, LiDAR is employed as a non-destructive testing (NDT) method to identify cracks, fissures, or other irregularities in concrete. Its purpose is to assess the structural integrity of the concrete and identify areas that may require repair or maintenance. [PERSON] et al. (2018) introduced a method to calculate deformation and strain in steel plates using 3D coordinate data obtained from LiDAR. The estimated strains were compared to the measured values from strain gauges. The comparison results indicated that the strain trends for all cases were similar [9]. [PERSON] et al. (2015) developed a new algorithm for 3D modeling and deformation to verify the geometry of the beams or determine whether a beam is acceptable or not in terms of deformation. A realistic 3D modeling of the deformed beam was performed automatically and reliably. However, this approach is only valid for situations in which the modeled structures are assumed to behave elastically [10]. [PERSON] et al. (2016) presented non-contact optical sensing using 3D stereo vision to measure the deformation of asphalt mixture concrete. The results were validated with LVDT data. The absolute average differences among the measured data using LVDT, finite element analysis, and the proposed stereovision method exhibited no significant difference [11]. [PERSON] et al. (2020) investigated methods for estimating the deflection of structures based on LiDAR data. The proposed approach was validated with an indoor experiment by inducing artificial deflection on a simply supported beam. The deflection value at various points was validated using LVDT. The results indicated that the error between two method is negligible [12]. [PERSON] et al. (2019) showed a novel RGB-D SLAM using LiDAR point cloud data as the guidance for indoor 3D scene construction and navigation. The proposed method was tested for long-distance tracking in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth.