Methodology and Validation of UAV-Based Video Analysis Approach for Tracking Earthquake-Induced Building Displacements

Wang, X.; Wittich, Christine E.; Hutchinson, Tara C.; Bock, Yehuda; Goldberg, D.; Lo, Eric; Kuester, Falko

doi:10.1061/(asce)cp.1943-5487.0000928

Cited by 22 publications

(16 citation statements)

References 25 publications

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“…ese methods have resulted in low monitoring frequency. X. Wang et al proposed a video analysis methodology for tracking the displacement response of buildings subject to dynamic loads using camera-equipped UAV platforms [23]. However, the study had not eliminated the errors induced by the camera movement.…”

Section: Detect Changes In Pointmentioning

confidence: 99%

Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

Yu²,

Chen

et al. 2021

Mathematical Problems in Engineering

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The height irregularity and complexity of steel structures bring difficulties to dynamic deformation monitoring. PDMS (photogrammetric dynamic monitoring system) can obtain the dynamic deformation of the steel structure, but the flexibility of monitoring is limited because the camera station can only be placed on the ground. In this study, UAV (unmanned aerial vehicle) -PDMS is innovatively proposed to be used in monitoring dynamic deformation of steel structures, and it is verified in the steel frame test and Jinan Olympic Sports Center Tennis Stadium test. To solve the problem that the attitude of UAV cannot be strictly maintained in the hovering process, the improved Z-MP (zero-centered motion parallax) method is used, and the monitoring results are compared with the original Z-MP method. The feasibility of UAV-PDMS applied to steel structure deformation monitoring and the feasibility of improving the Z-MP method to reduce UAV hovering error are verified. The monitoring results showed that the steel structures of the Jinan Olympic Sports Center Tennis Stadium were robust, and the deformations were elastic and within the permissible value.

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Section: Detect Changes In Pointmentioning

confidence: 99%

Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

Yu²,

Chen

et al. 2021

Mathematical Problems in Engineering

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“…This procedure is usually referred to as close-range photogrammetry or photogrammetry calibration. In most SHM applications using vision sensors, camera calibration is specifically dedicated to remove lens distortion [83,84]. The camera calibration in this study is in fact a photogrammetry deliverable with the final goal of defining the relationship between the two cameras by forming a full-projection matrix.…”

Section: Photogrammetry Proceduresmentioning

confidence: 99%

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Ngeljaratan

Moustafa

2020

Sensors

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Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

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“…Because of the flexibility of unmanned aerial vehicles (UAVs), researchers have conducted studies on using UAVs to monitor the health of structures (Ellenberg et al., 2015; Khaloo et al., 2018; Wang et al., 2020), most of them focused on the damage detection and the crack reconstruction for structure (Jiang & Zhang, 2020; Kang & Cha, 2018; Liu et al., 2020). Since the UAV is not stationary during hovering, it is necessary to eliminate the influence from the error of position and attitude of UAV when measuring the deflection of structures.…”

Section: Introductionmentioning

confidence: 99%

Noncontact deflection measurement for bridge through a multi‐UAVs system

Zhuge

Zhong

et al. 2021

Computer aided Civil Eng

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Deflection measurement is the research focus of health monitoring for bridges during the operation period. This study develops a contactless measurement technique to monitor the bridge deflection, leveraging visual information from a team of unmanned aerial vehicles (UAVs). On the basis of the collinearity of the laser spots projected on the plane by the coplanar laser indicator, we can eliminate the motion of UAV, and calculate the vertical displacement of the position to be measured relative to the bridge pier. In the proposed method, the center of the laser spot is extracted through a method based on deep learning, and an algorithm based on scale-invariant features registration was developed to track the feature points of the bridge in the image sequence. According to the algorithm, we demonstrate the accuracy and feasibility of our approach through simulation and simulated bridge experiments. The result shows that the root mean squared error (RMSE) of measurement through our technique is less than 0.5 mm in the laboratory conditions. In addition, the limits and scalability of the presented method have been explored through a field experiment.

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Methodology and Validation of UAV-Based Video Analysis Approach for Tracking Earthquake-Induced Building Displacements

Cited by 22 publications

References 25 publications

Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Noncontact deflection measurement for bridge through a multi‐UAVs system

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