Nondestructive evaluation inspection of the Arlington Memorial Bridge using a robotic assisted bridge inspection tool (RABIT)

Gucunski, Nenad; Boone, Shane D.; Zobel, Rob; Ghasemi, Hamid; Parvardeh, Hooman; Kee, Seong‐Hoon

doi:10.1117/12.2063963

Cited by 12 publications

(12 citation statements)

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“…The onboard sensors mounted on RABIT were: impact echo, ultrasonic surface wave testing, GPR, electrical resistivity, and a high-resolution digital camera. The RABIT was able to collect data of bridge decks at a rate of 372 square meter per hour, longer than a typical visual inspection, but acquiring considerably more data [22]. RABIT was able to successfully characterize and detect the most common deterioration types in concrete decks including rebar corrosion, delamination, and concrete degradation [23].…”

Section: Unmanned/automated Inspectionsmentioning

confidence: 99%

Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan

Maguire

2018

J Civil Struct Health Monit

132

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Unmanned Aerial Systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving imageprocessing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification.

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Section: Unmanned/automated Inspectionsmentioning

confidence: 99%

Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan

Maguire

2018

J Civil Struct Health Monit

132

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“…Passive phased array provides local phase delay by steering the wave front from a single source. Apart from shaping the phase and achieving fine spatial resolution, overcoming the local impedance mismatch is required when using acoustic metamaterials [ 252 , 253 , 264 , 283 , 293 , 395 , 396 ] and metasurfaces [ 265 , 397 , 398 , 399 , 400 , 401 ]. Li et al [ 296 ] presented a passive screen stacked up by an array of passive elements forming a hybrid structure.…”

Section: Acoustic Matching For Specific Transducer or Sensor Typementioning

confidence: 99%

A Review of Acoustic Impedance Matching Techniques for Piezoelectric Sensors and Transducers

Rathod

2020

Sensors

173

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The coupling of waves between the piezoelectric generators, detectors, and propagating media is challenging due to mismatch in the acoustic properties. The mismatch leads to the reverberation of waves within the transducer, heating, low signal-to-noise ratio, and signal distortion. Acoustic impedance matching increases the coupling largely. This article presents standard methods to match the acoustic impedance of the piezoelectric sensors, actuators, and transducers with the surrounding wave propagation media. Acoustic matching methods utilizing active and passive materials have been discussed. Special materials such as nanocomposites, metamaterials, and metasurfaces as emerging materials have been presented. Emphasis is placed throughout the article to differentiate the difference between electric and acoustic impedance matching and the relation between the two. Comparison of various techniques is made with the discussion on capabilities, advantages, and disadvantages. Acoustic impedance matching for specific and uncommon applications has also been covered.

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“…This gave the edge detectors an advantage over human inspectors. 4 False Negative Rate, 5 Accuracy, 6 Precision, and 7 Missed Crack Width…”

Section: Spatial Domain Roberts Filtermentioning

confidence: 99%

“…Such inspections are costly, time-consuming, and labor-intensive [2,3]. Autonomous inspection could be a cost-effective solution to these problems if the accuracy of human inspection can be matched [3][4][5][6][7][8][9]. Image-based inspection of infrastructure for concrete delamination [10][11][12][13], cracks [14][15][16][17], and spalls [18,19] using unmanned aerial systems (UASs) have been proven effective based on previous literature [20].…”

Section: Introductionmentioning

confidence: 99%

“…Even in emerging applications of supervised machine learning methods, edge detectors are still considered in practice since they do not require expensive annotated training datasets. Using edge detectors for crack detection is computationally fast, making it an appealing option for real-time crack detection during UAS inspections; they have been implemented in ground-based robotic inspections in the past [4,18,38]. Edge detectors can have different sizes, shapes, and values.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

2019

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This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

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Nondestructive evaluation inspection of the Arlington Memorial Bridge using a robotic assisted bridge inspection tool (RABIT)

Cited by 12 publications

References 0 publications

Bridge inspection: human performance, unmanned aerial systems and automation

Bridge inspection: human performance, unmanned aerial systems and automation

A Review of Acoustic Impedance Matching Techniques for Piezoelectric Sensors and Transducers

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

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