Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

2021 ◽  
Author(s):  
Cheng Zhang ◽  
Yongding Tian ◽  
Jian Zhang
Keyword(s):  
Deep Learning ◽  
Force Estimation ◽  
Complex Image ◽  
Background Segmentation ◽  
Cable Force ◽  
Cable Force Estimation

scholarly journals Cable force estimation of cables with small sag considering inclination angle effect

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Wen-Yu He ◽  
Fan-Cheng Meng ◽  
Wei-Xin Ren
Keyword(s):  
Boundary Conditions ◽  
Inclination Angle ◽  
Estimation Methods ◽  
Unknown Boundary ◽  
Force Estimation ◽  
Numerical Examples ◽  
Cable Force ◽  
Angle Effect ◽  
Unknown Boundary Conditions ◽  
Cable Force Estimation

AbstractCable force estimation is essential for security assessment of cable-stayed bridges. Cable force estimation methods based on the relationship between cable force and frequency have been extensively studied and used during both construction phase and service phase. However, the effect induced by inclination angle of the cable is not included in the establishment of frequency-cable force relationship as horizontal cable model is normally employed. This study aims to investigate the influence of the inclination angle on vibration based cable force estimation and provide practical formulas accordingly. Firstly numerical examples of fixed-fixed and hinged-hinged cables are simulated to illustrate the necessity of considering the inclination angle effect on the modal parameters and cable force estimation for inclined cables with small sag. Then practical formulas considering the inclination angle effect to estimate the cable force of fixed-fixed and hinged-hinged cables via the fundamental frequency are established accordingly. For the inclined cables with unknown boundary conditions, the coefficients reflecting boundary condition are predicted via the practical formulas for fixed-fixed and hinged-hinged cables. And the cable force considering the influence of inclination angle and unknown boundary conditions is obtained by iteration method. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed method.

Noncontact cable force estimation with unmanned aerial vehicle and computer vision

2020 ◽  
Vol 36 (1) ◽  
pp. 73-88 ◽  
Author(s):  
Yongding Tian ◽  
Cheng Zhang ◽  
Shang Jiang ◽  
Jian Zhang ◽  
Wenhui Duan
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicle ◽  
Force Estimation ◽  
Cable Force ◽  
Aerial Vehicle ◽  
Cable Force Estimation

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

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4187 ◽  
Author(s):  
Shaodong Zhan ◽  
Zhi Li ◽  
Jianmin Hu ◽  
Yiping Liang ◽  
Guanglie Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Flexural Rigidity ◽  
Tensile Force ◽  
Suspension Bridge ◽  
Force Estimation ◽  
Model Order ◽  
Measured Frequency ◽  
Cable Force ◽  
Cable Force Estimation

The tensile force on the hanger cables of a suspension bridge is an important indicator of the structural health of the bridge. Tensile force estimation methods based on the measured frequency of the hanger cable have been widely used. These methods empirically pre-determinate the corresponding model order of the measured frequency. However, because of the uncertain flexural rigidity, this empirical order determination method not only plays a limited role in high-order frequencies, but also hinders the online cable force estimation. Therefore, we propose a new method to automatically identify the corresponding model order of the measured frequency, which is based on a Markov chain Monte Carlo (MCMC)-based Bayesian approach. It solves the limitation of empirical determination in the case of large flexural rigidity. The tensile force and the flexural rigidity of cables can be calculated simultaneously using the proposed method. The feasibility of the proposed method is validated via a numerical study involving a finite element model that considers the flexural rigidity and via field application to a suspension bridge.

scholarly journals Experimental Verification for Cable Force Estimation Using Handheld Shooting of Smartphones

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xuefeng Zhao ◽  
Kwang Ri ◽  
Niannian Wang
Keyword(s):  
Dynamic Characteristics ◽  
Natural Frequencies ◽  
Force Measurement ◽  
Moving Average ◽  
Rapid Development ◽  
Practical Significance ◽  
Force Estimation ◽  
Cable Stayed Bridge ◽  
Cable Force ◽  
Cable Force Estimation

Currently, due to the rapid development and popularization of smartphones, the usage of ubiquitous smartphones has attracted growing interest in the field of structural health monitoring (SHM). The portable and rapid cable force measurement for cable-supported structures, such as a cable-stayed bridge and a suspension bridge, has an important and practical significance in the evaluation of initial damage and the recovery of transportation networks. The extraction of dynamic characteristics (natural frequencies) of cable is considered as an essential issue in the cable force estimation. Therefore, in this study, a vision-based approach is proposed for identifying the natural frequencies of cable using handheld shooting of smartphone camera. The boundary of cable is selected as a target to be tracked in the region of interest (ROI) of video image sequence captured by smartphone camera, and the dynamic characteristics of cable are identified according to its dynamic displacement responses in frequency domain. The moving average is adopted to eliminate the noise associated with the shaking of smartphone camera during measurement. A laboratory scale cable model test and a pedestrian cable-stayed bridge test are carried out to evaluate the proposed approach. The results demonstrate the feasibility of using smartphone camera for cable force estimation.

scholarly journals Force estimation from 4D OCT data in a human tumor xenograft mouse model

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Maximilian Neidhardt ◽  
Nils Gessert ◽  
Tobias Gosau ◽  
Julia Kemmling ◽  
Susanne Feldhaus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mouse Model ◽  
Haptic Feedback ◽  
Human Tumor ◽  
Tumor Xenograft ◽  
Human Tumor Xenograft ◽  
Force Estimation ◽  
Xenograft Mouse Model ◽  
Dead Tissue

AbstractMinimally invasive robotic surgery offer benefits such as reduced physical trauma, faster recovery and lesser pain for the patient. For these procedures, visual and haptic feedback to the surgeon is crucial when operating surgical tools without line-of-sight with a robot. External force sensors are biased by friction at the tool shaft and thereby cannot estimate forces between tool tip and tissue. As an alternative, vision-based force estimation was proposed. Here, interaction forces are directly learned from deformation observed by an external imaging system. Recently, an approach based on optical coherence tomography and deep learning has shown promising results. However, most experiments are performed on ex-vivo tissue. In this work, we demonstrate that models trained on dead tissue do not perform well in in vivo data. We performed multiple experiments on a human tumor xenograft mouse model, both on in vivo, perfused tissue and dead tissue. We compared two deep learning models in different training scenarios. Training on perfused, in vivo data improved model performance by 24% for in vivo force estimation.

scholarly journals Spatio-temporal deep learning models for tip force estimation during needle insertion

2019 ◽  
Vol 14 (9) ◽  
pp. 1485-1493 ◽  
Author(s):  
Nils Gessert ◽  
Torben Priegnitz ◽  
Thore Saathoff ◽  
Sven-Thomas Antoni ◽  
David Meyer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Needle Insertion ◽  
Learning Models ◽  
Force Estimation ◽  
Spatio Temporal

scholarly journals Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

2020 ◽  
Vol 64 ◽  
pp. 101730 ◽  
Author(s):  
Nils Gessert ◽  
Marcel Bengs ◽  
Matthias Schlüter ◽  
Alexander Schlaefer
Keyword(s):  
Deep Learning ◽  
Temporal Data ◽  
Force Estimation ◽  
Data Representations ◽  
Spatio Temporal
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