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Graybeal, Benjamin A.; Phares, Brent; Rolander, D D; Moore, Mark E.; Washer, Glenn

doi:10.1023/a:1022508121821

Cited by 149 publications

(33 citation statements)

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“…Furthermore, knowledge transfer from one inspection period to another becomes difficult when successive investigations are performed by different inspectors. Graybeal et al (2002) noted that routine inspections have relatively poor accuracy, with the following factors affecting the reliability of these results: the inspector's fear of traffic, near visual acuity, and color vision, as well as the accessibility and complexity of the structure. In addition, traffic volumes have increased considerably, meaning that track/road possession must be minimised (Hugenschmidt, 2002).…”

Section: Introductionmentioning

confidence: 99%

3D reconstruction of existing concrete bridges using optical methods

Popescu

Täljsten

Blanksvärd

et al. 2019

Structure and Infrastructure Engineering

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Routine bridge inspections usually consist of visual observations. These inspections are time-consuming and subjective. There is a need to identify new inspection techniques for infrastructure that reduce traffic disturbance, and improve the efficiency and reliability of the acquired data. This study compared the performance of three different imaging technologies for the three-dimensional (3D) geometric modeling of existing structures: terrestrial laser scanning, close-range photogrammetry, and infrared scanning. Each technology was used to assess six existing concrete railway bridges. The technologies were compared in terms of geometric deviations, visualization capabilities, the level of the inspector's experience, and degree of automation. The results suggest that all methods investigated can be used to create 3D models, however, with different level of completeness. Measurements such as span length, deck widths, etc. can be extracted with good accuracy. Although promising, a full off-site inspection is currently not feasible as some areas of the bridges were difficult to capture mainly due to restricted access and narrow spaces. Measurements based on terrestrial laser scanning were closer to the reality compared to photogrammetry and infrared scanning. The study indicates the no special training is needed for photogrammetry and infrared scanning to generate a 3D geometric model.

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Section: Introductionmentioning

confidence: 99%

3D reconstruction of existing concrete bridges using optical methods

Popescu

Täljsten

Blanksvärd

et al. 2019

Structure and Infrastructure Engineering

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“…The coefficients of variation of a rated bridge deck by different inspectors in visual inspections were between 57% and 96%. Detection of concrete deck cracks can be considered a "typical" decision in bridge inspections, and considering a normal distribution for all the calls, it is acceptable to have 67.5% of correct calls, i.e., ± one standard deviation, [23,26,48]. This meant each of the edge detectors, except for Roberts, had surpassed the acceptable accuracy of the visual inspections.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, SDNET2018 [21], with more than 56,000 labeled images of concrete structures, covers three small lab-made bridge decks, walls of a building, and several paved sidewalks, which are significantly smaller than common inspected infrastructures in practice. Manual identification of flaws in such large image sets is time consuming and prone to inaccuracy because of inspector fatigue or human error [22][23][24][25][26]. Image processing algorithms can improve the accuracy and efficiency of autonomous inspections by either (a) enhancing images to improve ease of human detection of defects or (b) autonomously identifying defects.…”

Section: Introductionmentioning

confidence: 99%

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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“…Bridge-inspection program regulations were developed based on the Federal-Aid Highway Act of 1968 (Graybeal et al 2002), which required the Secretary of Transportation to establish the National Bridge Inspection Standards (NBIS). This catastrophic event resulted in 46 deaths and prompted several congressional hearings.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Structural Deficiency Ratio of Bridges Based on Multiway Data Factorization

Adarkwa

Attoh-Okine

2017

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Prediction of future bridge conditions is essential in bridge management as this provides guidance for strategic planning. The National Bridge Inventory (NBI) database has been used by researchers for this purpose by utilizing Markov Chains and regression approaches. This paper introduces a hybrid method for predictions based on multiway data decomposition and simple exponential smoothing (SES). Multiway decomposition is used due to the multidimensional nature of the utilized bridge data. The simple exponential smoothing technique is utilized to predict the future proportions of structurally deficient bridges, known as the structural deficiency ratio (SD ratio) across the United States. The mean absolute error (MAE) values declined with increasing smoothing factor values, which implies bridge conditions can be reliably predicted based on recent SD ratios and not on distant past ones. With prediction metrics yielding relatively low values, this hybrid approach involving multiway data decomposition and simple exponential smoothing presents a promising tool for bridge database data analysis. individual papers. This paper is part of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, © ASCE, ISSN 2376-7642. © ASCE F4016002-1 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-3 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-5 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16. Copyright ASCE. For personal use only; all rights reserved. © ASCE F4016002-7 ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng. ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng., F4016002 Downloaded from ascelibrary.org by University Of Kentucky on 06/26/16.

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Cited by 149 publications

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3D reconstruction of existing concrete bridges using optical methods

3D reconstruction of existing concrete bridges using optical methods

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

Prediction of Structural Deficiency Ratio of Bridges Based on Multiway Data Factorization

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