Non-contact strain measurement for laterally loaded steel plate using LiDAR point cloud displacement data

Jo, Hyeon Cheol; Kim, Junhwi; Lee, Kangwon; Sohn, Hong Gyoo; Lim, Yun Mook

doi:10.1016/j.sna.2018.09.012

Cited by 18 publications

(17 citation statements)

References 19 publications

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“…The results are shown in Figure 17. The polynomial strain curve [15], shown in Figure 18a, is a two-dimensional analysis by FEM using a high-order polynomial function, one of the regression methods, based on the point cloud in the center of the steel plate. The reason for the strain curves to fluctuate, as shown in Figure 18 is due to the tie-rods.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…Additionally, these kinds of sensors have a short lifespan, which requires a large amount of human and economic cost for maintenance [14]. Nowadays, as structures are becoming taller and larger, the measurement range is increasing, and the installation of wired-based sensors is becoming more difficult and more limited [15]. In order to overcome and improve the chronic limitations of these conventional sensors, research on non-contact remote measurement methods for structural behavior analysis is increasing [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Also, it can remotely acquire 3D coordinate information data for behavior monitoring of buildings, and infrastructure, along with traditional uses such as geography and terrain modeling, geological exploration and surveying, and forest management [24][25][26][27][28]. Unlike conventional wire-based attached or installed sensors, an advantage of a LiDAR system is that it can measure the shape of structures before and after deformation due to the actual external loading [15]. By contrast, conventional sensors can only provide measurements at certain fixed positions or increments.…”

Section: Introductionmentioning

confidence: 99%

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A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure

Sohn

Lim

2020

Sensors

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Structural health monitoring (SHM) and safety assessment are very important areas for evaluating the behavior of structures. Various wired and wireless sensors can measure the physical responses of structures, such as displacement or strain. One recently developed wireless technique is a light imaging detection and ranging (LiDAR) system that can remotely acquire three-dimensional (3D) high-precision coordinate information using 3D laser scanning. LiDAR systems have been previously used in geographic information systems (GIS) to collect information on geography and terrain. Recently, however, LiDAR is used in the SHM field to analyze structural behavior, as it can remotely detect the surface and deformation shape of structures without the need for attached sensors. This study demonstrates a strain evaluation method using a LiDAR system in order to analyze the behavior of steel structures. To evaluate the strains of structures from the initial and deformed shape, a combination of distributed 3D point cloud data and finite element methods (FEM) was used. The distributed 3D point cloud data were reconstructed into a 3D mesh model, and strains were calculated using the FEM. By using the proposed method, the strain could be calculated at any point on a structure for SHM and safety assessment during construction.

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Section: Results and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure

Sohn

Lim

2020

Sensors

Self Cite

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“…The integration of point measurement and surface measurement can not only accurately measure the deformation of a single monitoring point, but also obtain the overall deformation of the slope surface [32][33][34]45]. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed.…”

Section: Methodsmentioning

confidence: 99%

“…Slope deformation measurement based on TLS determines the spatial position of the comparison point relative to the reference point by processing the reference point cloud (point cloud of the first phase) and the comparison point cloud (point cloud of the second phase) [32]. It includes four types of methods: point cloud superposition method, point cloud comparison method, center of gravity method, and fitting method [33]. Among them, the point cloud superposition method and point cloud comparison method belong to surface measurement as their monitoring points do not necessarily correspond to one-to-one, and the center of gravity method and fitting method can achieve one-to-one correspondence of monitoring points, equivalent to conventional feature point measurement [34].…”

Section: Introductionmentioning

confidence: 99%

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

Yin

Li²,

et al. 2020

Applied Sciences

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Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.

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3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

Gao,

Guo,

Wang

et al. 2024

Advcd Theory and Sims

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Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state.

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Non-contact strain measurement for laterally loaded steel plate using LiDAR point cloud displacement data

Cited by 18 publications

References 19 publications

A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure

A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure

Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example

3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi‐Source Spatial Data

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