Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach

Zhan, Shaodong; Li, Zhi; Hu, Jianmin; Liang, Yiping; Zhang, Guanglie

doi:10.3390/s18124187

Cited by 6 publications

(5 citation statements)

References 23 publications

(24 reference statements)

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“…For long‐span bridges, inclined cables and vertical hangers are essential components for supporting the bridge, therefore tension forces of them need to be measured accurately. Traditional cable forces measurement methods are mainly dependent on contact‐type measurement device, such as attached accelerometers (Zhan, Li, Hu, Liang, & Zhang, 2018) or Elastomagnetic (EM) sensors (Yim et al., 2013), from which time‐averaged cable force (Zhan et al., 2018) and time‐varying cable tension force (Bao, Shi, Beck, Li, & Hou, 2016) were computed. However, these methods are time‐consuming and labor‐intensive because of installing sensors and moving data acquisition system on long‐span bridges.…”

Section: Introductionmentioning

confidence: 99%

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Tian

Zhang

Jiang

et al. 2020

Computer aided Civil Eng

119

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Cables and hangers are critical components of long-span bridges, tension forces of them are needed to be accurately measured for ensuring the safety of bridges. Traditionally, cable tension forces are measured by attached accelerometers or elastomagnetic (EM) sensors, however, applying these sensors into engineering practice are time-consuming, labor-intensive, and highly dangerous. To address these problems, an unmanned aerial vehicle (UAV)-based noncontact cable force estimation method with computer vision technologies was proposed in this article. Basic concept of the proposed method is to use the UAV-installed camera for capturing vibration images of cables from a certain distance and cable dynamic properties are extracted by analyzing captured images. It includes two aspects: (a) a line segments detector (LSD) was employed for detecting cable edges from captured video and a line matching algorithm was further proposed for extracting dynamic displacements; (b) the frequency differ-Frequency Difference/ Hz Cable Force / kN F I G U R E 14 Results of the studied suspension bridge: (a) Fourier spectrum by the proposed method; (b) Fourier spectrum by using attached accelerometer; (c) frequency difference; and (d) cable force How to cite this article: Tian Y, Zhang C, Jiang S, Zhang J, Duan W. Noncontact cable force estimation with unmanned aerial vehicle and computer vision.

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Section: Introductionmentioning

confidence: 99%

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Tian

Zhang

Jiang

et al. 2020

Computer aided Civil Eng

119

View full text Add to dashboard Cite

show abstract

“…Many scholars have studied the frequency-based tension identification method under complex boundaries (Ma et al, 2023;Syamsi et al, 2022) by using the dynamic model (Huang et al, 2015;Xu et al, 2019), particle swarm optimization algorithm (Zhang et al, 2021), and other methods. Zhan et al (2018) proposed a Bayesian method based on Markov chain Monte Carlo, which realized the determination of frequency corresponding modal order in the frequency method. Gai et al (2023) used the neural network model and the finite element model as the basis to establish the correlation between cable structure characteristics such as cable length and input frequency and cable force.…”

Section: Introductionmentioning

confidence: 99%

“…Zhan et al. (2018) proposed a Bayesian method based on Markov chain Monte Carlo, which realized the determination of frequency corresponding modal order in the frequency method. Gai et al.…”

Section: Introductionmentioning

confidence: 99%

A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension

Xu,

Gao,

Liu

2024

Computer aided Civil Eng

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In the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders.

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“…Most contact measurement methods rely on the utilization of accelerometers for monitoring purposes [5,6]. Nevertheless, structural health monitoring systems employing contact monitoring methods necessitate a substantial number of connection wires due to the extended distances and numerous inclined cables involved, resulting in elevated costs [7].…”

Section: Introductionmentioning

confidence: 99%

Complex Background Removal Method for Video of an Inclined Cable Based on UAV Detection of Structural Vibration Frequencies

Li,

Cao,

et al. 2024

Structural Control and Health Monitoring

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The safety of inclined cables is fundamental to the integrity of cable-stayed bridges. The vibrational frequencies of these cables form the foundation for assessing the cable force. Traditional contact measurement methods necessitate the installation of sensors on each cable, incurring substantial costs. In scenarios where camera placement adjacent to an inclined cable is impractical, noncontact approaches such as video capture via unmanned aerial vehicles prove effective. However, unmanned aerial vehicle-captured videos present a challenge due to their complex background, impeding cable feature recognition. In our study, we initially utilized the Region Growing algorithm for background subtraction. To enhance this method, we integrated it with the unique structural characteristics of cables, leading to the creation of the RGv2 algorithm. This novel algorithm offers increased processing speed and improved accuracy. Furthermore, we combined our method with empirical mode decomposition for effective detection of cable frequency characteristics. We also implemented a hybrid method, combining the K-Means and line segment detector algorithms with empirical mode decomposition. Compared to deep learning techniques for background subtraction, our proposed method demonstrates superior computational efficiency and promising potential for measuring vibrational frequencies of inclined cables.

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Model Order Identification for Cable Force Estimation Using a Markov Chain Monte Carlo-Based Bayesian Approach

Cited by 6 publications

References 23 publications

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension

Complex Background Removal Method for Video of an Inclined Cable Based on UAV Detection of Structural Vibration Frequencies

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