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

Jo, Hyeon Cheol; Sohn, Hong Gyoo; Lim, Yun Mook

doi:10.3390/s20030721

Cited by 6 publications

(4 citation statements)

References 34 publications

(34 reference statements)

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“…The deflection was estimated and compared between LVDTs. The strain and deformation of a steel plate under lateral pressure using LiDAR data were evaluated by [143]. Through a specific interpolation procedure, the point cloud was converted into a 3D mesh model, and the displacements from the initial shape were computed, followed by the strain calculations.…”

Section: Other Structural Membersmentioning

confidence: 99%

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Kaartinen

Dunphy

Sadhu

2022

Sensors

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As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail.

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Section: Other Structural Membersmentioning

confidence: 99%

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Kaartinen

Dunphy

Sadhu

2022

Sensors

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“…Jo et al [11] and Jo et al [12] demonstrated that it is possible to approximate displacements across an entire area by approximating the PCD cross-section of a curved steel plate subjected to lateral pressure with a polynomial function and creating a 3D grid model for the entire surface using kriging. This method has the advantage of being applicable even in cases where no measurement targets are available.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing

Seo

Yoon

Lee

2023

Sensors

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This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.

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“…Since then field validations have been made by various researchers. [45][46][47][48][49] Depending on hardware specification, some Lidars are known to have long-distance measurement and high precision ranging 3-5 mm. 50,51 However, those precise Lidars require a long scanning time and adjustment processes, resulting in their popular usage in geomatics and static measurements and limited application in dynamic measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The first attempt for measuring the static deformation of a structure using a Lidar was made by Park et al 44 When compared with LVDT, the Lidar showed a maximum error of 1.6%. Since then field validations have been made by various researchers 45–49 . Depending on hardware specification, some Lidars are known to have long‐distance measurement and high precision ranging 3–5 mm 50,51 .…”

Section: Introductionmentioning

confidence: 99%

Noncontact dynamic displacements measurements for structural identification using a multi‐channel Lidar

Lee

Kim

2022

Structural Contr & Hlth

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Identifying fundamental characteristics of a structure provide key information for structural health monitoring (SHM). To date, numerous researchers have reported tools and algorithms that can perform system identification, with a large portion of their application being contact-based sensors. Although dynamic responses of structures can be directly measured from contact-based sensors, the lifespan of those sensors being much shorter than that of the structure, requiring labor to deploy and maintain the sensors, etc., has led to the use of non-contact-based sensors. Among various non-contact-based sensors, some researchers have investigated the use of light detection and ranging (Lidar) sensors, which remotely acquire three-dimensional ranging information, mostly for static displacement measurement during construction. Thus, this paper presents an approach for system identification of structures using dynamic displacement measured from a multi-channel Lidar sensor. Hardware and mechanical attributes that limit the direct use of raw data from the Lidar are explored. Then, strategies to adjust the tilt axes and reduce the range uncertainties and data synchronization are proposed. Subsequently, two types of laboratory-scale structures are prepared for validation: a flexible cantilever beam and a four-story shear building. In both of the structures, the Lidar showed less than 5% error in estimating the first natural frequencies. Also, the mode shape has been estimated with high precision. The results demonstrate the ability of the Lidar for identifying dynamic characteristics of a structure.The potable feature of the Lidar will further allow full-scale monitoring of a large-scale civil infrastructure for SHM.

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

Cited by 6 publications

References 34 publications

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing

Noncontact dynamic displacements measurements for structural identification using a multi‐channel Lidar

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