Estimating small structural motions from multi-view video measurement

Cai, Enjian; Zhang, Yi; Ji, Xiaodong; Lu, Xinzheng; Xie, Liji; Zhuang, Yuncheng; Zhao, Taisen; Lin, Guangwei

doi:10.1016/j.engstruct.2022.115259

Cited by 13 publications

(5 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the tests, the interstorey drift measurement is implemented by the computer vision-based measurement approach proposed by Cai et al [73], as Figure 3 shows. A sampling frequency of 50 Hz was set in the cameras.…”

Section: Case Study: Shaking Table Experimentsmentioning

confidence: 99%

A Bayesian Structural Modal Updating Method Based on Sparse Grid and Ensemble Kalman Filter

Lin,

Zhang,

Cai

et al. 2024

Structural Control and Health Monitoring

Self Cite

View full text Add to dashboard Cite

This study presents a sparse grid interpolation and ensemble Kalman filter (EnKF)-based Markov Chain Monte Carlo (MCMC) method (SG-EnMCMC). Initiating with the formulation of a recursive equation for the state space vector, derived from the structural dynamic equation, this study adopts a dimensionality reduction strategy. This approach involves a separation of physical parameters and the state space vector. The acquisition of physical parameters is accomplished through sampling, utilizing sample moments to substitute population moments, thereby mitigating the need for computationally high-dimensional covariance matrix calculations. To further streamline the recursive equation of the state space vector, a sparse grid method is employed for interpolation. This step simplifies the process while ensuring superior accuracy compared to the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Subsequent to this, acceptance rates and the final parameter posterior distribution within the MCMC framework are derived. The efficiency of the proposed method is assessed through validation in two shaking table experiments.

show abstract

Section: Case Study: Shaking Table Experimentsmentioning

confidence: 99%

A Bayesian Structural Modal Updating Method Based on Sparse Grid and Ensemble Kalman Filter

Lin,

Zhang,

Cai

et al. 2024

Structural Control and Health Monitoring

Self Cite

View full text Add to dashboard Cite

show abstract

“…23,25,26 In addition, the videos recorded by ground-mounted cameras could identify the top displacement of buildings. 27,28 These technologies can capture the top displacement and the lower-order modes of a tall building. Let x top be the top lateral displacement of the building, then the top-displacement-based error J d p ð Þ can be defined as follows:…”

Section: Different Sources Of Monitoring Data and Their Associated Ob...mentioning

confidence: 99%

“…As mentioned in Section 1, certain GNSSs could measure displacement with a frequency up to 20 Hz and a movement up to millimeter precision 23,25,26 . In addition, the videos recorded by ground‐mounted cameras could identify the top displacement of buildings 27,28 . These technologies can capture the top displacement and the lower‐order modes of a tall building.…”

Section: Model Updatingmentioning

confidence: 99%

“…Cheng and Kawaguchi 27 used a surveillance camera to measure the vibration of 10 buildings under 2011 Great East Japan Earthquake. Cai et al 28 used multiple cameras to determine the displacement of structures under earthquakes and demonstrated promising results. The aforementioned technology allows the use of various vibration monitoring data for improving the accuracy and robustness of model updating.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model updating of a shear‐wall tall building using various vibration monitoring data: Accuracy and robustness

Shan,

Zhuang,

Chao

et al. 2024

Structural Design Tall Build

View full text Add to dashboard Cite

SummaryAcceleration measurements are often used for model updating of civil engineering structures, especially in the case of seismic monitoring. It is yet unclear if accelerations alone would generate an accurate and robust finite‐element (FE) model. This study examines this notion and analyzes the possibility of using other vibration monitoring data for model updating of shear‐wall tall buildings. This study compares the accuracy and robustness of the FE models being optimized via accelerations, roof displacement, wall rotations, interstory drift ratios, and the linear combination of these measurements. A numerical case study is analyzed using Timoshenko beams for modeling the lateral vibration of a benchmark 42‐story building under seismic excitations. Results show that the acceleration response of the examined building is mostly governed by its higher vibration modes. Depending on the characteristics of ground motions, using accelerations alone may generate an FE model biased towards higher‐order modes without effectively capturing the lower‐order modes. For instance, the first modal frequency of the updated FE model could be 12.0% lower than the true value, and the reconstructed displacement and rotation responses are noticeably inaccurate. Employing multi‐source monitoring data for model updating, for example, the combinations of roof displacement and acceleration measurements, could reduce the normalized root‐mean‐square errors in displacements by more than 70%. This study also quantifies the robustness of the FE model under various measurement noise levels and 50 pairs of earthquake records. Finally, the effects of multi‐source data on FE model updating are validated via experiments on a 7‐story shear wall building. Analysis reveals that a more accurate and robust FE model can be determined via a combination of accelerations and top displacement than via acceleration alone.

show abstract

“…Luo et al [21] introduced a novel image-processing technique that addresses the challenge vision sensors face in outdoor structural displacement monitoring, as the conventional approach is susceptible to noise and limited in its measurement range. To enhance accuracy, Cai et al [22] addressed the limitations of phase-based estimation and proposed a novel multi-view measurement method. Miao et al [23], by optimizing the Gabor filter parameters, introduced a robust phase-based displacement measurement technique to capture vibration responses.…”

Section: Motion and Phase Relationmentioning

confidence: 99%