UAS-Based Inspection of the Placer River Trail Bridge:A Data-Driven Approach

Cunningham, Keith; Lattanzi, David; Dell’Andrea, Rodney; Riley, Matt; Huette, T; Goetz, Rich; Wilson, Ronald W.

doi:10.1061/9780784479117.053

Cited by 6 publications

(4 citation statements)

References 3 publications

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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%

“…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%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Bridge Inspection Approach with Joint UAV and DIC System

Jeong¹,

Seo²,

Wacker³

2020

Structures Congress 2020

View full text Add to dashboard Cite

show abstract

“…Besides the traditional bridge inspection methods, in recent years the use of UAVs for bridge inspections has been recognized as a promising alternative method ( 7 , 8 ). There are several proposed UAV research solutions to monitor infrastructure.…”

mentioning

confidence: 99%

Developing Enhanced Unmanned Aerial Vehicle Sensing System for Practical Bridge Inspections Using Field Experiments

Nasimi

Moreu

Nasimi

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Unmanned aerial vehicles (UAVs) have transitioned from a futuristic research concept to becoming a reality for practical, safe, cost-effective bridge inspections. Several studies have used UAVs to capture images from bridges and infrastructure to assess their condition. However, measuring the dynamic responses of bridges using a UAV involves the integration of UAV, sensors, and the use of dynamic equations. Measurement of dynamic transverse displacement especially is a difficult task in the field given the actual constraints of bridges, flights, and sensing under loading events. If transverse displacements could be measured easily, bridge owners could prioritize maintenance operations more cost-effectively by selecting to repair those bridges that move the most under trains. This paper discusses new requirements and solutions for fabricating an enhanced UAV to obtain dynamic transverse displacement benefiting from experiences gained from several field tests. This work follows the regulations of railroad bridge inspection guidelines and considers the aspects for an implementable system in its development. The paper first introduces the preliminary system that has been developed to this end and discusses potential improvements to this system that are identified through multiple field tests. The preliminary UAV system was developed using an algorithm combining the signals from sensors mounted on the UAV to measure dynamic displacements. This paper explains the step-by-step improvement of the existing system which resulted in a successful field test on a real bridge. Subsequently, some modifications and enhancements for the algorithm are proposed which are compatible with the new system data.

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“…Most importantly, the safety of rope access inspectors is uncertain (1). To resolve these issues, bridge inspections using drones have been frequently adopted by many researchers (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). Particularly, drones can provide easier accessibility and straightforward maneuverability to the inspectors to record a high volume of videos and images for visual defect detection of a bridge.…”

mentioning

confidence: 99%

Grayscale Drone Inspection Image Enhancement Framework for Advanced Bridge Defect Measurement

Jeong

Seo

Wacker

2021

Transportation Research Record: Journal of the Transportation R

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This paper presents a framework to better identify and measure defects in a bridge using drone-based inspection images integrated with grayscale image enhancement techniques. For this study, a DJI Matrice 210 drone was used for the inspection of a three-span timber bridge with concrete decking located in Keystone, South Dakota. During the inspection, the drone recorded a series of videos of the bridge using the MOVie (MOV, video file extension) video format. MOV-based image analysis was conducted to identify a variety of defect types (i.e., efflorescence, water leakage, spalling, and discoloration) on the bridge. For improvement of defect visibility, the grayscale image enhancement technique was applied to determine visually enhanced images for the individual defect. The technique used grayscale image histogram processing that can adjust images using realignment of contrast histograms, in which contrasts of each pixel of the grayscale images have their own number from 0 for black to 255 for white in the image. With the enhanced images, pixel-based measurement was conducted to quantify the defects, including efflorescence (3.75 m2), water leakage (4.21 m2), spalling (0.74 m2), and discoloration (2.12 m2). Based on these findings, the grayscale drone inspection image enhancement technique enabled the demonstration of defect visibility adjustment and improvement for more reliable identification and measurement of the defects in the bridge.

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UAS-Based Inspection of the Placer River Trail Bridge:A Data-Driven Approach

Cited by 6 publications

References 3 publications

New Bridge Inspection Approach with Joint UAV and DIC System

New Bridge Inspection Approach with Joint UAV and DIC System

Developing Enhanced Unmanned Aerial Vehicle Sensing System for Practical Bridge Inspections Using Field Experiments

Grayscale Drone Inspection Image Enhancement Framework for Advanced Bridge Defect Measurement

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