UAV-Based Non-Contact Fatigue Crack Monitoring of Steel Structures

Taher, Sdiq Anwar; Li, Jian; Collins, William; Bennett, Caroline

doi:10.12783/shm2019/32477

Cited by 3 publications

(4 citation statements)

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“…where 1 1−v can be treated as a gauge factor, and v is the Poisson ratio of the dielectric [29]. Equation (5) indicates that ∆C has a linear relationship with the total in-plane strain under the SEC sensor.…”

Section: Sec For Large-area Strain Sensingmentioning

confidence: 99%

“…Repetitive service loads produce fatigue cracks in metallic civil infrastructures, e.g., steel bridges, which pose great threats to their structural integrity and may lead to catastrophic failures. The current de facto method for detecting and monitoring fatigue cracks is visual inspection carried out by trained inspectors, which may be inaccurate and susceptible to errors due to the small size and randomness of fatigue cracks [ 1 ]. To improve accuracy and prevent catastrophic failures, a more effective and efficient technique for monitoring those fatigue cracks is critical to ensure timely actions.…”

Section: Introductionmentioning

confidence: 99%

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Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors

Taher

Jeong

et al. 2022

Sensors

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This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.

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Section: Sec For Large-area Strain Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Fatigue Cracks for Steel Bridges with Wireless Large-Area Strain Sensors

Taher

Jeong

et al. 2022

Sensors

Self Cite

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“…Many vision-based algorithms have been suggested, which include edge detector [31], thresholding [32], segmentation [33], and filter-based algorithms [34]. These techniques have been utilized in assessing damage in wood samples [35][36][37], bridge and concrete surfaces [38][39][40], pavement [34,41,42], and steel [43][44][45]. Other applications of DIC include biomechanical applications [46], biological tissues and biomaterials [47][48][49], aerospace [50,51], computer vision [52], medical appli-cations [53], industrial and automotive applications [54].…”

Section: Digital Image Correlationmentioning

confidence: 99%

“…ment [34,41,42], and steel [43][44][45]. Other applications of DIC include biomechanical applications [46], biological tissues and biomaterials [47][48][49], aerospace [50,51], computer vision [52], medical applications [53], industrial and automotive applications [54].…”

Section: Digital Image Correlationmentioning

confidence: 99%

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

et al. 2021

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A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, long-distance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

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