Sparse accelerometer-aided computer vision technology for the accurate full-field displacement estimation of beam-type bridge structures

Wu, Tong; Tang, Liang; Li, Xinyu; Zhang, Xiangyu; Liu, Yijun; Zhou, Zhixiang

doi:10.1016/j.measurement.2023.112532

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…However, the above methods are only suitable for obtaining high-fidelity displacements at sparse locations and cannot be used to obtain the full-field displacement of the bridge. To address this problem, Wu et al [ 183 ] proposed a sparse accelerometer-aided CV technique and used the modal superposition method to obtain the full-field dynamic displacement through acceleration at sparse locations.…”

Section: Cv-based Vibration Measurementmentioning

confidence: 99%

Computer Vision-Based Bridge Inspection and Monitoring: A Review

Luo,

Kong,

Zhang

et al. 2023

Sensors

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Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

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Section: Cv-based Vibration Measurementmentioning

confidence: 99%

Computer Vision-Based Bridge Inspection and Monitoring: A Review

Luo,

Kong,

Zhang

et al. 2023

Sensors

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“…Terefore, it is generally considered that the high-frequency displacement estimated by the measured acceleration is more reliable than the lowfrequency displacement. To make full use of the advantages of acceleration-based measurement, data fusion of the highfrequency displacement estimated by acceleration and lowfrequency displacement measured by other sensors has been proposed to obtain an accurate displacement [22][23][24][25][26][27][28][29][30]. Ma et al [22] estimated displacement using measured strains and proposed an FIR-based displacement estimation technique by fusing strain and acceleration measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al [28,29] fused the GPS-based and vision-based displacements with the acceleration measurements using a multirate Kalman flter, and the efciencies were validated on a railway bridge. Wu et al [30] estimated the full-feld displacements of beam structures by data fusion of visionbased displacement and accelerations and modal expansion.…”

Section: Introductionmentioning

confidence: 99%

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Tang

Zhang

et al. 2023

Structural Control and Health Monitoring

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Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

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