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Special Issue "Bridge Damage Detection with Sensing Technology"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 August 2019).

Special Issue Editors

Guest Editor
Prof. Eugene OBrien

Full Professor of Civil Engineering, in School of Civil Engineering, University College Dublin, Newstead, Belfield, Dublin D04 V1W8, Ireland
Website | E-Mail
Interests: Dynamic Interaction of Trucks with Bridges and Roads; Bridge damage detection and SHM; Weigh-in-Motion of Road Vehicles; Bridge Traffic Loading
Guest Editor
Prof. Mustafa Gül

Associate Professor, Dept. of Civil & Environmental Engineering, University of Alberta 9211 116 St NW Edmonton, Alberta, Canada T6G 1H9
Website | E-Mail
Interests: Smart Infrastructure Systems; Crowdsourcing-based monitoring technologies; Bridge damage detection and SHM

Special Issue Information

Dear Colleagues,

There is considerable interest in bridge damage detection and bridge health monitoring and considerable progress has been made in this field in recent years. However, several challenges remain, and road/rail infrastructure owners are hesitant to invest in the levels of instrumentation often envisaged by researchers. Many bridges are small and/or located in remote regions and there may not be access to electrical power. There is considerable interest in damage detection methods that can overcome these issues. With traditional visual inspection, monitoring only occurs occasionally—there is a need to improve on this with systems that monitor frequently or continuously. But perhaps one of the greatest challenges is sensitivity—there is a need for systems and methods that are sensitive to low levels of damage and that can distinguish between damage and other sources of interference such as environmental changes (e.g. temperature changes), operational changes and surface profile deterioration. This Special Issue will address such challenges, publishing papers on bridge damage detection methods that require no electrical power, monitor frequently/continuously, or are highly sensitive to low levels of damage. It will establish the current state-of-the-art and will identify future challenges in bridge damage detection.

Prof. Eugene OBrien
Prof. Mustafa Gül
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bridge
  • health monitoring
  • SHM
  • BHM
  • damage detection
  • drive-by
  • low-energy

Published Papers (3 papers)

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Research

Open AccessArticle
A Robust Vision-Based Method for Displacement Measurement under Adverse Environmental Factors Using Spatio-Temporal Context Learning and Taylor Approximation
Sensors 2019, 19(14), 3197; https://doi.org/10.3390/s19143197
Received: 31 May 2019 / Revised: 12 July 2019 / Accepted: 17 July 2019 / Published: 20 July 2019
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Abstract
Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect [...] Read more.
Currently, the majority of studies on vision-based measurement have been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect measurement accuracy. This paper developed a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal contextual aspects. To validate the feasibility, stability, and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination changes and fog interference were simulated experimentally in the laboratory. The results of the proposed method were compared to conventional displacement sensor data and current vision-based method results. It was demonstrated that the proposed method gave better measurement results than the current ones under illumination change and fog interference. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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Open AccessArticle
Extraction of Bridge Fundamental Frequencies Utilizing a Smartphone MEMS Accelerometer
Sensors 2019, 19(14), 3143; https://doi.org/10.3390/s19143143
Received: 25 June 2019 / Revised: 10 July 2019 / Accepted: 15 July 2019 / Published: 17 July 2019
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Abstract
Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer [...] Read more.
Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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Open AccessArticle
Scour Damage Detection and Structural Health Monitoring of a Laboratory-Scaled Bridge Using a Vibration Energy Harvesting Device
Sensors 2019, 19(11), 2572; https://doi.org/10.3390/s19112572
Received: 7 May 2019 / Revised: 27 May 2019 / Accepted: 31 May 2019 / Published: 6 June 2019
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Abstract
A vibration-based bridge scour detection procedure using a cantilever-based piezoelectric energy harvesting device (EHD) is proposed here. This has an advantage over an accelerometer-based method in that potentially, the requirement for a power source can be negated with the only power requirement being [...] Read more.
A vibration-based bridge scour detection procedure using a cantilever-based piezoelectric energy harvesting device (EHD) is proposed here. This has an advantage over an accelerometer-based method in that potentially, the requirement for a power source can be negated with the only power requirement being the storage and/or transmission of the data. Ideally, this source of power could be fulfilled by the EHD itself, although much research is currently being done to explore this. The open-circuit EHD voltage is used here to detect bridge frequency shifts arising due to scour. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. The method would work best with an initial modal analysis of the bridge structure in order to identify frequencies that may be sensitive to scour. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilised towards structural health monitoring of a model bridge affected by scour. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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