Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan, Sattar; Maguire, Marc

doi:10.1007/s13349-018-0285-4

Cited by 124 publications

(83 citation statements)

References 134 publications

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“…A recent review by Dorafshan and Maguire [52] provides an overview of the specific challenges of bridge inspection and maintenance, and how UAVs, both with active and passive sensors, are starting to become a commonly used tool. In an early study by Whang et al [22], a UAV with two coaxial rotors was developed to perform somewhat autonomous bridge inspection, within limits even in GPS-denied areas beneath the bridge.…”

Section: The Special Case Of Infrastructure Damage Mappingmentioning

confidence: 99%

UAV-Based Structural Damage Mapping: A Review

Kerle

Nex

Gerke

et al. 2019

IJGI

134

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Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities.

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Section: The Special Case Of Infrastructure Damage Mappingmentioning

confidence: 99%

UAV-Based Structural Damage Mapping: A Review

Kerle

Nex

Gerke

et al. 2019

IJGI

134

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“…The computational efficiency of this method resulted from the fact that LoG used only one kernel, as opposed to x and y component kernels in the other spatial domain methods. This reduced the number of convolution operations (Equation (2)) from two to one, which obviated the use of Equation (3). Computational efficiencies of the other spatial domain methods did not follow the expected trend.…”

Section: Comparisonmentioning

confidence: 99%

“…The most common inspection type is routine inspection, wherein the inspector scans the bridge deck to identify surface degradation or surface cracking. 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].…”

Section: Introductionmentioning

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%

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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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“…The U.S. National Bridge Inspection (NBI) program was established in 1968 as a result of the Silver Bridge collapse in 1967. Accordingly, the National Bridge Inspection Standards (NBIS) were established in 1971 [1]. The NBIS set a uniform inspection interval of two years for most bridges, regardless of bridge age or past performance.…”

Section: Introduction and Purposementioning

confidence: 99%

An Integrated Uncertainty-Based Bridge Inspection Decision Framework with Application to Concrete Bridge Decks

et al. 2019

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The limitations of the standard two-year interval for the visual inspection of bridges required by the U.S. National Bridge Inspection Standards have been well documented, and alternative approaches to bridge inspection planning have been presented in recent literature. This paper explores a different strategy for determining the interval between inspections and the type of inspection technique to use for bridges. The foundational premise of the proposed approach is that bridge inspections are conducted to increase knowledge about the bridge’s current condition, and therefore, are only required when uncertainty about the knowledge of the bridge condition is too high. An example case of a reinforced concrete bridge deck was used to demonstrate how this approach would work. The method utilized deterioration models for predicting corrosion and crack initiation time, considering the uncertainty in the models’ parameters. Bridge inspections were used to update the current condition information and model parameters through Bayesian updating. As this paper presents a new idea for inspection planning, not all of the data or models necessary to fully develop and validate the approach currently exist. Nonetheless, the method was applied to a simulated example which demonstrates how the timing and means of bridge inspection can be tailored to provide the required data about individual bridges needed for effective bridge management decision making.

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Bridge inspection: human performance, unmanned aerial systems and automation

Cited by 124 publications

References 134 publications

UAV-Based Structural Damage Mapping: A Review

UAV-Based Structural Damage Mapping: A Review

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

An Integrated Uncertainty-Based Bridge Inspection Decision Framework with Application to Concrete Bridge Decks

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