Investigation of vibration serviceability of a footbridge using computer vision-based methods

Dong, Chuan‐Zhi; Baş, Selçuk; Çatbaş, F. Necati

doi:10.1016/j.engstruct.2020.111224

Cited by 44 publications

(31 citation statements)

References 22 publications

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“…In this way, Tables 1 and 2 are supposed to serve as a guide to the following paragraphs, each dedicated to one of the six structural groups, presented in the same order used in the tables. It is anticipated that comparisons in all cases provided good correlations between vision-based monitoring and the other considered technologies, with one exception being the steel footbridge (vertical truss frames) tested by Dong et al [199], where differences between accelerometers and vision-based measurements were not negligible. It should be remarked that, in four cases, no direct comparisons were made: Shariati and Schumacher [183], as well as Feng and Feng [187], compared the magnitudes of the measurements to those obtained in previous tests, concluding that such comparisons were favorable; in Dhanasekar et al [195], the outcomes of the experimental monitoring were satisfactory compared with numerical simulations in terms of magnitude of the monitored structural parameters; and in Lydon et al [196], vision-based monitoring was part of an integrated monitoring system that included fiber optics with the objectives to complement the two systems.…”

Section: General Overviewmentioning

confidence: 93%

“…Attention in this review article is given to the analysis of recent results obtained in vibration (displacement time histories) monitoring of civil engineering structures and infrastructures in the field, as documented in refereed journal articles published in the last four years [183][184][185][186][187][188][189][190][191][192][193][194][195][196][197][198][199][200]. The results presented are subdivided into six structural groups: steel bridges, steel footbridges, steel structures for sport stadiums, reinforced concrete structures, masonry structures, and timber footbridge.…”

Section: General Overviewmentioning

confidence: 99%

“…Dong et al [199] presented the field vision-based monitoring of a footbridge on a campus in the southeast of the US.A, made by vertical truss frames connected via splice connection in the middle and spanning an entire length of 39 m over a pond; the deck width is 4.17 m, and it serves light pedestrian traffic and small vehicles such as golf carts. A single video camera with resolution of 1920 × 1080 pixels and rate speed of 60 FPS was located near one of the abutments and employed to monitor the vertical vibration of the mid-span.…”

Section: Steel Footbridgesmentioning

confidence: 99%

See 2 more Smart Citations

Vision-Based Vibration Monitoring of Structures and Infrastructures: An Overview of Recent Applications

Zona

2020

Infrastructures

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Contactless structural monitoring has in recent years seen a growing number of applications in civil engineering. Indeed, the elimination of physical installations of sensors is very attractive, especially for structures that might not be easily or safely accessible, yet requiring the experimental evaluation of their conditions, for example following extreme events such as strong earthquakes, explosions, and floods. Among contactless technologies, vision-based monitoring is possibly the solution that has attracted most of the interest of civil engineers, given that the advantages of contactless monitoring can be potentially obtained thorough simple and low-cost consumer-grade instrumentations. The objective of this review article is to provide an introductory discussion of the latest applications of vision-based vibration monitoring of structures and infrastructures through an overview of the results achieved in full-scale field tests, as documented in the published technical literature. In this way, engineers new to vision-based monitoring and stakeholders interested in the possibilities of contactless monitoring in civil engineering could have an outline of up-to-date achievements to support a first evaluation of the feasibility and convenience for future monitoring tasks.

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Section: General Overviewmentioning

confidence: 93%

Section: General Overviewmentioning

confidence: 99%

Section: Steel Footbridgesmentioning

confidence: 99%

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Vision-Based Vibration Monitoring of Structures and Infrastructures: An Overview of Recent Applications

Zona

2020

Infrastructures

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“…With the development of advanced imaging devices and computer vision, vision-based structural monitoring and inspection have gained increasing attention in the field of civil infrastructure monitoring [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Computer vision techniques can be implemented into civil infrastructure applications to track the global motions/deformations of structures [ 17 , 18 , 19 , 20 , 21 ] and detect the local changes/damages [ 22 ] with the advantages of being non-contact, long-distance, low-cost, and less time-consuming and allowing automatic monitoring and inspection. In this study, the scope and objectives are: (1) to propose a computer vision-based method for fatigue crack detection of steel bridges; (2) to achieve pixel-level crack segmentation from large-scale images (larger than 4K images) collected by consumer-grade cameras; and (3) to provide helpful considerations and recommendations for researchers and engineering practitioners in the field of civil infrastructure engineering.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Dong

Jin

et al. 2021

Sensors

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Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.

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“…(10)(11)(12)(13) Computer vision technology has been applied to the detection of construction deformation and movement using data from site images and videos as an alternative to time-consuming and manual traditional practices. (14)(15)(16)(17)(18) Thus, the relative displacements between the camera and the chessboard in the x-, y-, and z-directions can be measured using computer vision technology. This computervision-based crackmeter, called the 3D optical crackmeter in this study, can be employed for measuring 3D displacements of cracks.…”

Section: Introductionmentioning

confidence: 99%

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

Chen¹,

Chen²,

Chen³

et al. 2021

Sensors and Materials

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An innovative 3D optical crackmeter employing computer vision technology is used for displacement monitoring in a crack of a retaining wall automatically and remotely. The 3D optical crackmeter is composed of a Raspberry Pi device and a digital camera in a box, and a fixed chessboard on the two sides of a crack. A network with LoRa wireless communication can be connected as an IoT system to provide automatic remote functions. The OpenCV library is employed to analyze changes in chessboard imaging so that relative displacements of the crack in the retaining wall can be measured in a landslide area. Through laboratory and field testing, the resolution and accuracy of the 3D optical crackmeter were determined as 0.04 and 0.1 mm, respectively. Using the crackmeter, we observed significant displacements in the xand z-directions of the crack in a retaining wall of 0.067 and 0.060 cm, respectively, in the Jhongsinlun landslide area of Taiwan over three months. Overall, the 3D optical crackmeter with computer vision technology can accurately measure the 3D displacement of cracks in a retaining wall. Moreover, the IoT-based 3D optical crackmeter is more cost-effective than traditional crackmeters used in landslide areas.

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Investigation of vibration serviceability of a footbridge using computer vision-based methods

Cited by 44 publications

References 22 publications

Vision-Based Vibration Monitoring of Structures and Infrastructures: An Overview of Recent Applications

Vision-Based Vibration Monitoring of Structures and Infrastructures: An Overview of Recent Applications

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Optical Crackmeter for Retaining Wall in a Landslide Area Using Computer Vision Technology

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