Vision‐based vibration monitoring using existing cameras installed within a building

Harvey, P. Scott; Elisha, Guy

doi:10.1002/stc.2235

Cited by 28 publications

(16 citation statements)

References 35 publications

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“…AI-based disaster management has attracted much interest from researchers and practitioners in earthquake engineering. This includes CV-based damage detection and quantification, [94][95][96] CV-based vibration extraction, 97,98 and rapid damage inference and condition assessment for a single structure and a large number of structures in a zone in an urban area. 99,100 Disaster management (e.g.…”

Section: Ai-based Disaster Managementmentioning

confidence: 99%

Machine learning paradigm for structural health monitoring

Bao

2020

Structural Health Monitoring

193

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Structural health diagnosis and prognosis is the goal of structural health monitoring. Vibration-based structural health monitoring methodology has been extensively investigated. However, the conventional vibration–based methods find it difficult to detect damages of actual structures because of a high incompleteness in the monitoring information (the number of sensors is much fewer with respect to the number of degrees of freedom of a structure), intense uncertainties in the structural conditions and monitoring systems, and coupled effects of damage and environmental actions on modal parameters. It is a truth that the performance and conditions of a structure must be embedded in the monitoring data (vehicles, wind, etc.; acceleration, displacement, cable force, strain, images, videos, etc.). Therefore, there is a need to develop completely novel structural health diagnosis and prognosis methodology based on the various monitoring data. Machine learning provides the advanced mathematical frameworks and algorithms that can help discover and model the performance and conditions of a structure through deep mining of monitoring data. Thus, machine learning takes an opportunity to establish novel machine learning paradigm for structural health diagnosis and prognosis theory termed the machine learning paradigm for structural health monitoring. This article sheds light on principles for machine learning paradigm for structural health monitoring with some examples and reviews the existing challenges and open questions in this field.

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Section: Ai-based Disaster Managementmentioning

confidence: 99%

Machine learning paradigm for structural health monitoring

Bao

2020

Structural Health Monitoring

193

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“…The results indicated that EEM was more accurate than orientation‐code‐matching, DIC, and SURF. Harvey and Elisha used existing surveillance cameras to monitor interstory drift. Additionally, many applications were introduced in the referenced review papers .…”

Section: Introductionmentioning

confidence: 99%

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Xie

Zhao

2020

Struct Control Health Monit

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Summary When an earthquake strikes an area, unless the local infrastructure has a structural health monitoring system, it is difficult to obtain its health condition due to a lack of data. A smartphone could be competently used for many monitoring tasks to gather large amounts of data at a low cost. In this study, a smartphone is used as a sensor to obtain the interstory drift of buildings by recording a video of the ceiling with the front camera using a feature point matching algorithm. In addition, static and dynamic tests were carried out. In the static tests, the precision and error were investigated and compared with the reference sensor. In the dynamic tests, the displacement under a seismic wave was acquired by a smartphone and compared with the reference sensor, and the illumination condition was varied to test the robustness of the algorithm. The results indicated that feature point matching was suitable for calculating the displacement, and the smartphone was competent in monitoring the interstory drift during an earthquake.

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“…To overcome these problems, some scholars have proposed SHM methods using image-based measurement techniques [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Image-based measurement methods have been widely used in various experimental mechanics studies.…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments

Liu²,

Chung³

et al. 2020

Sensors

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In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

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Vision‐based vibration monitoring using existing cameras installed within a building

Cited by 28 publications

References 35 publications

Machine learning paradigm for structural health monitoring

Machine learning paradigm for structural health monitoring

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments

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