Comparison of Non-Destructive Techniques for Technological Bridge Deflection Testing

Kwiatkowski, Jacek; Anigacz, W.; Bęben, Damian

doi:10.3390/ma13081908

Cited by 26 publications

(21 citation statements)

References 28 publications

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“…Jiang et al [8] concluded that close-range photogrammetry technology is a powerful measurement method that can provide unique solutions for a variety of bridge engineering applications. Kwiatkowski et al [9] compared and evaluated the usefulness of various measurement techniques (terrestrial laser scanning (TLS), tachymetry, photogrammetry) applied to establish the behavior of suspension bridges under various loads. Gawronek et al [10] measures the vertical displacement of railroad bridges using terrestrial laser scanning (TLS) technology, and confirmed that this technology can provide reasonable results for quick assessment of railway infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021

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Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

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Section: Introductionmentioning

confidence: 99%

Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021

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“…Computer vision, a field that enables computers to recognize and analyze visual information, has been continuously developed [17][18][19][20][21]. Deep learning techniques have recently introduced numerous applications in computer vision [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Semantic Structure from Motion for Railroad Bridges Using Deep Learning

2021

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Current maintenance practices consume significant time, cost, and manpower. Thus, a new technique for maintenance is required. Construction information technologies, including building information modeling (BIM), have recently been applied to the field to carry out systematic and productive planning, design, construction, and maintenance. Although BIM is increasingly being applied to new structures, its application to existing structures has been limited. To apply BIM to an existing structure, a three-dimensional (3D) model of the structure that accurately represents the as-is status should be constructed and each structural component should be specified manually. This study proposes a method that constructs a 3D model and specifies the structural component automatically using photographic data with a camera installed on an unmanned aerial vehicle. This procedure is referred to as semantic structure from motion because it constructs a 3D point cloud model together with semantic information. A validation test was carried out on a railroad bridge to validate the performance of the proposed system. The average precision, intersection over union, and BF scores were 80.87%, 66.66%, and 56.33%, respectively. The proposed method could improve the current scan-to-BIM procedure by generating the as-is 3D point cloud model by specifying the structural component automatically.

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“…In the case of laser scanning, a target point and separate data network are not required for data acquisition. The necessary equipment for this procedure is a laser scanning device and a laptop computer, which allows the researcher to obtain data on the entire surface of the structure [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization

Cha

Sim

Park

et al. 2020

Sensors

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As civil engineering structures become larger, non-contact inspection technology is required to measure the overall shape and size of structures and evaluate safety. Structures are easily exposed to the external environment and may not be able to perform their original functions depending on the continuous load for a long time. Therefore, in this study, we propose a method for estimating the vertical displacement of structures using light detection and ranging, which enables non-contact measurement. The point cloud acquired through laser scanning was rearranged into a three-dimensional space, and internal nodes were created by continuously dividing the space. The generated node has its own location information, and the vertical displacement value was calculated by searching for the node where the deformation occurred. The performance of the proposed displacement estimation technique was verified through static loading experiments, and the octree space partitioning method is expected to be applied and utilized in structural health monitoring.

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Comparison of Non-Destructive Techniques for Technological Bridge Deflection Testing

Cited by 26 publications

References 28 publications

Vision-Based Structural FE Model Updating Using Genetic Algorithm

Vision-Based Structural FE Model Updating Using Genetic Algorithm

Semantic Structure from Motion for Railroad Bridges Using Deep Learning

LiDAR-Based Bridge Displacement Estimation Using 3D Spatial Optimization

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