Debonding Detection in Reinforced Concrete Beams with the Use of Guided Wave Propagation

Zima, Beata

doi:10.1007/978-981-13-8331-1_35

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…To date, a wide variety of techniques have been developed for structural health monitoring. These techniques include ultrasonic/acoustic-based techniques [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ], eddy-current-based techniques [ 10 , 11 , 12 ], and microwave-based techniques [ 13 , 14 , 15 ]. These technologies are promising for specific structural health monitoring applications, however, they are not readily suited to being scaled up for applications such as monitoring the structural health of an entire city.…”

Section: Introductionmentioning

confidence: 99%

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

Martinez

Green

Silva

et al. 2020

Sensors

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Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

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