Unmanned aerial vehicle acquisition of three-dimensional digital image correlation measurements for structural health monitoring of bridges

Reagan, Daniel; Sabato, Alessandro; Niezrecki, Christopher

doi:10.1117/12.2259985

Cited by 38 publications

(42 citation statements)

References 15 publications

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“…With the rapid development of image processing algorithms, displacement could be determined through image processing. Among these algorithms, feature point matching, digital image correlation (DIC), and optical flow have demonstrated relevance to many engineering applications . However, artificial speckle or texture patterns are needed for DIC, which may not be feasible in many situations.…”

Section: Introductionmentioning

confidence: 99%

“…Among these algorithms, feature point matching, 11 digital image correlation (DIC), 12 and optical flow 13 have demonstrated relevance to many engineering applications. [14][15][16] However, artificial speckle or texture patterns are needed for DIC, which may not be feasible in many situations. Additionally, optical flow is influenced by changes in brightness.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Xie

Zhao

2020

Struct Control Health Monit

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Summary When an earthquake strikes an area, unless the local infrastructure has a structural health monitoring system, it is difficult to obtain its health condition due to a lack of data. A smartphone could be competently used for many monitoring tasks to gather large amounts of data at a low cost. In this study, a smartphone is used as a sensor to obtain the interstory drift of buildings by recording a video of the ceiling with the front camera using a feature point matching algorithm. In addition, static and dynamic tests were carried out. In the static tests, the precision and error were investigated and compared with the reference sensor. In the dynamic tests, the displacement under a seismic wave was acquired by a smartphone and compared with the reference sensor, and the illumination condition was varied to test the robustness of the algorithm. The results indicated that feature point matching was suitable for calculating the displacement, and the smartphone was competent in monitoring the interstory drift during an earthquake.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Xie

Zhao

2020

Struct Control Health Monit

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“…To overcome these challenges, robot-assisted inspection apparatus coupled with various inspection techniques (Lee et al 2011(Lee et al , 2008Lim et al 2011;Mazumdar and Asada 2009;Murphy et al 2011;Oh et al 2007Oh et al , 2009Ward et al 2014), has been used for the bridge inspection. Recently, Unmanned Aerial Vehicles (UAVs) have been considered as a promising bridge inspection tool for the bridge inspection (Cunningham et al 2015;Otero et al 2015;Seo et al 2018b;c;Zink and Lovelace 2015;Hallermann and Morgenthal 2014;Khaloo et al 2018;Reagan et al 2017a;. A variety of researchers have demonstrated from different bridge inspection applications that UAV was a useful supplementary damage identification tool (Cunningham et al 2015;Duque et al 2018a;Otero et al 2015;Seo et al 2018a;c;Zink and Lovelace 2015).…”

Section: Introductionmentioning

confidence: 99%

New Bridge Inspection Approach with Joint UAV and DIC System

Jeong¹,

Seo²,

Wacker³

2020

Structures Congress 2020

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This research aims to develop a new bridge inspection approach using unmanned aerial vehicle (UAV) coupled with digital image correlation (DIC) system. The DIC system incorporating UAV images can measure displacements or strains by analyzing patterns of reference and deformed images. As part of this research, a commercially available UAV, DJI Matrice 210, was integrated with the DIC system using a 3D printed mounting plate, and the joint UAV-DIC system was utilized to inspect a timber bridge girder in the Structure Lab. Then, the UAV-DIC system inspected an existing timber slab bridge in Pipestone, Minnesota, but the system was not able to efficiently identify critical damage due to its instability caused by windy conditions. Therefore, only the UAV equipped with a gimbal camera was operated to perform the bridge inspection. A significant number of images from the UAV were used and analyzed through a conventional image analysis algorithm within ImageJ software for damage quantification. The major conclusion from this research was that the UAV-DIC system was only able to detect and quantify damage (i.e., crack) on the considered girder under almost zero ambient wind conditions, and the UAV integrated with the image analysis algorithm was capable of damage identification and quantification for the inspected bridge.

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“…Reagan et al. () and Yoon et al. () introduced a method to measure relative displacement of structures using an UAS, but use of the UAS to measure absolute dynamic displacement of a structure has not been reported.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies employing an UAS (Metni and Hamel, 2007;Chen et al, 2011;Zhang and Elaksher, 2012;Ellenberg et al, 2014;Brooks et al, 2015;Yeum and Dyke, 2015;Gillins et al, 2016) have been reported which mostly focus on monitoring of civil infrastructure to find static deformation, local cracks, and corrosions. Reagan et al (2017) and Yoon et al (2017) introduced a method to measure relative displacement of structures using an UAS, but use of the UAS to measure absolute dynamic displacement of a structure has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Structural Displacement Measurement Using an Unmanned Aerial System

Yoon

Shin

Spencer

2017

Computer aided Civil Eng

193

104

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Vibration‐based Structural Health Monitoring (SHM) is one of the most popular solutions to assess the safety of civil infrastructure. SHM applications all begin with measuring the dynamic response of structures, but displacement measurement has been limited by the difficulty in requiring a fixed reference point, high cost, and/or low accuracy. Recently, researchers have conducted studies on vision‐based structural health monitoring, which provides noncontact and efficient measurement. However, these approaches have been limited to stationary cameras, which have the challenge of finding a location to deploy the cameras with appropriate line‐of‐sight, especially to monitor critical civil infrastructures such as bridges. The Unmanned Aerial System (UAS) can potentially overcome the limitation of finding optimal locations to deploy the camera, but existing vision‐based displacement measurement methods rely on the assumption that the camera is stationary. The displacements obtained by such methods will be a relative displacement of a structure to the camera motion, not an absolute displacement. Therefore, this article presents a framework to achieve absolute displacement of a structure from a video taken from an UAS using the following phased approach. First, a target‐free method is implemented to extract the relative structural displacement from the video. Next, the 6 degree‐of‐freedom camera motion (three translations and three rotations) is estimated by tracking the background feature points. Finally, the absolute structural displacement is recovered by combining the relative structural displacement and the camera motion. The performance of the proposed system has been validated in the laboratory using a commercial UAS. Displacement of a pinned‐connected railroad truss bridge in Rockford, IL subjected to revenue‐service traffic loading was reproduced on a hydraulic simulator, while the UAS was flown from a distance of 4.6 m (simulating the track clearance required by the Federal Railroad Administration), resulting in estimated displacements with an RMS error of 2.14 mm.

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Unmanned aerial vehicle acquisition of three-dimensional digital image correlation measurements for structural health monitoring of bridges

Cited by 38 publications

References 15 publications

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

New Bridge Inspection Approach with Joint UAV and DIC System

Structural Displacement Measurement Using an Unmanned Aerial System

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