Full-Scale Highway Bridge Deformation Tracking via Photogrammetry and Remote Sensing

Graves, William; Aminfar, Kiyarash; Lattanzi, David

doi:10.3390/rs14122767

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…Thanks to digital imaging, some studies recently proposed to exploit deep learning for quantitative assessment of visual detectability of different types of in-service damage in laminated composite structures such as aircraft and wind turbine blades [ 7 ]. Interestingly, photogrammetric point cloud analysis using remotely sensed point cloud data also possible to track full-scale 3D infrastructure deformation at the millimetre scale in a field environment [ 8 ].…”

Section: Surface Non-destructive Testing Techniquesmentioning

confidence: 99%

Non-Destructive Health Monitoring of Structural Polymer Composites: Trends and Perspectives in the Digital Era

Chaki

Krawczak

2022

Materials

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Non-destructive testing (NDT) techniques are usually used for the characterisation of defects arising in polymer composites during manufacturing or in-service use. However, each of these NDT techniques cannot always allow a full diagnosis of the material’s or component’s structural health. Thus, several techniques have to be combined in order to improve the diagnosis of the damaged state of composite structures and their evolution during the part’s life span. This opinion paper proposes a critical overview of the use and applicability of these NDT techniques for the detection and characterisation of damage to structural composite materials in view of in-service performance assessment and residual durability prognosis. It also addresses some current trends of structural health monitoring (SHM) of these materials, such as sensor–actuator embedding and NDT data fusion, and draws future perspectives on how composite SHM could evolve in the digital era, taking advantage of artificial intelligence, Internet of Things and big data to implement digital twins.

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Section: Surface Non-destructive Testing Techniquesmentioning

confidence: 99%

Non-Destructive Health Monitoring of Structural Polymer Composites: Trends and Perspectives in the Digital Era

Chaki

Krawczak

2022

Materials

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“…Bridge structures are often complicated objects for data collection [27] as they can be relatively long and tall, contain both horizontal and vertical elements, and often are curved in shape [28]. Importantly, considering extremely high requirements on the life span of bridges, the accuracy requirements on geodetic measurements of such objects are typically very high [29].…”

Section: Introductionmentioning

confidence: 99%

Determination of Accuracy and Usability of a SLAM Scanner GeoSLAM Zeb Horizon: A Bridge Structure Case Study

Urban,

Štroner,

Braun

et al. 2024

Applied Sciences

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The presented paper focuses on testing the performance of a SLAM scanner Zeb Horizon by GeoSLAM for the creation of a digital model of a bridge construction. A cloud acquired using a static scanner Leica ScanStation P40 served as a reference. Clouds from both scanners were registered into the same coordinate system using a Trimble S9 HP total station. SLAM scanner acquisition was performed independently in two passes. The data acquired using the SLAM scanner suffered from relatively high noise. Denoising using the MLS (Moving Least Squares) method was performed to reduce noise. An overall comparison of the point clouds was performed on both the original and MLS-smoothed data. In addition, the ICP (Iterative Closest Point) algorithm was also used to evaluate local accuracy. The RMSDs of MLS-denoised data were approximately 0.02 m for both GeoSLAM passes. Subsequently, a more detailed analysis was performed, calculating RMSDs for several profiles of the construction. This analysis revealed that the deviations of SLAM data from the reference data did not exceed 0.03 m in any direction (longitudinal, transverse, elevation) which is, considering the length of the bridge of 133 m, a very good result. These results demonstrate a high applicability of the tested scanner for many applications, such as the creation of digital twins.

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“…Shang et al [7] proposed a new flight planning method to enable efficient data for a truss bridge reconstruction. Graves et al [8] utilized 3D geometric measurements extracted from photogrammetric point clouds to assess the performance of highway bridges in static load testing.…”

Section: Introductionmentioning

confidence: 99%

A Combined Safety Monitoring Model for High Concrete Dams

et al. 2022

Applied Sciences

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When applying reliability analysis to the monitoring of structural health, it is very important that gross errors–which affect prediction accuracy–are included within the monitoring information. An approach using gross errors identification and a dam safety monitoring model for deformation monitoring data of concrete dams is proposed in this paper. It can solve the problems of strong nonlinearity and the difficulty of identifying and eliminating gross errors in deformation monitoring data in concrete dams. This new method combines the advantages of an incremental extreme learning machine (I-ELM) method to seek an optimal network structure, the Least Median Squares (LMS) method with strong robustness to multiple failure points, the robust estimation IGG method with the good robustness to outliers (gross errors) and extreme learning machine (ELM) method with high prediction efficiency and handling of nonlinear problems. The proposed method can eliminate gross errors and be utilized to predict the behavior of concrete dams. The deformation monitoring data of an existing 305 m-high concrete arch dam is acquired by combining remote sensing technology with other monitoring methods. The LMS-IGG-ELM method is utilized to eliminate outliers from the dam monitoring sequence and is compared with the processing result from a DBSCAN clustering algorithm, Romanovsky criterion and the 3σ method. The results show that the proposed method has the highest gross errors identification rate, the strongest generalization ability and the best prediction effect.

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Full-Scale Highway Bridge Deformation Tracking via Photogrammetry and Remote Sensing

Cited by 12 publications

References 56 publications

Non-Destructive Health Monitoring of Structural Polymer Composites: Trends and Perspectives in the Digital Era

Non-Destructive Health Monitoring of Structural Polymer Composites: Trends and Perspectives in the Digital Era

Determination of Accuracy and Usability of a SLAM Scanner GeoSLAM Zeb Horizon: A Bridge Structure Case Study

A Combined Safety Monitoring Model for High Concrete Dams

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