Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor

To further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, o...

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Main Authors: Shuai Shao, Zhixiang Zhou, Guojun Deng, Peng Du, Chuanyi Jian, Zhongru Yu
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/4/1187
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author Shuai Shao
Zhixiang Zhou
Guojun Deng
Peng Du
Chuanyi Jian
Zhongru Yu
author_facet Shuai Shao
Zhixiang Zhou
Guojun Deng
Peng Du
Chuanyi Jian
Zhongru Yu
author_sort Shuai Shao
collection DOAJ
description To further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer-vision-based full-field non-contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full-field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification.
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spelling doaj.art-33db5574bb0c42d08041c2bc30632cf22022-12-22T04:19:46ZengMDPI AGSensors1424-82202020-02-01204118710.3390/s20041187s20041187Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual SensorShuai Shao0Zhixiang Zhou1Guojun Deng2Peng Du3Chuanyi Jian4Zhongru Yu5School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaTo further improve the precision and efficiency of structural health monitoring technology and the theory of large-scale structures, full-field non-contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer-vision-based full-field non-contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full-field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification.https://www.mdpi.com/1424-8220/20/4/1187structural geometry monitoringcomputer-vision-based measurement technologybridge safetyholographic visual sensordense full-field measurementdigital twins
spellingShingle Shuai Shao
Zhixiang Zhou
Guojun Deng
Peng Du
Chuanyi Jian
Zhongru Yu
Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
Sensors
structural geometry monitoring
computer-vision-based measurement technology
bridge safety
holographic visual sensor
dense full-field measurement
digital twins
title Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
title_full Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
title_fullStr Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
title_full_unstemmed Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
title_short Experiment of Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor
title_sort experiment of structural geometric morphology monitoring for bridges using holographic visual sensor
topic structural geometry monitoring
computer-vision-based measurement technology
bridge safety
holographic visual sensor
dense full-field measurement
digital twins
url https://www.mdpi.com/1424-8220/20/4/1187
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AT pengdu experimentofstructuralgeometricmorphologymonitoringforbridgesusingholographicvisualsensor
AT chuanyijian experimentofstructuralgeometricmorphologymonitoringforbridgesusingholographicvisualsensor
AT zhongruyu experimentofstructuralgeometricmorphologymonitoringforbridgesusingholographicvisualsensor