A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system

Measurement ◽  
2021 ◽  
pp. 109847
Author(s):  
Jinsong Zhu ◽  
Chi Zhang ◽  
Ziyue Lu ◽  
Xingtian Li
Keyword(s):  
Tracking System ◽  
Displacement Measurement ◽  
Dynamic Displacement ◽  
Deep Feature

scholarly journals Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5092
Author(s):  
Kiyoung Kim ◽  
Hoon Sohn
Keyword(s):  
Kalman Filter ◽  
Large Scale ◽  
Reduction Method ◽  
Estimation Method ◽  
Sampling Frequency ◽  
Displacement Measurement ◽  
Dynamic Displacement ◽  
Displacement Estimation ◽  
Gnss Receiver ◽  
Civil Infrastructures

In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

Accuracy Enhancement of Videogrammetrey for Structural Dynamic Displacement Measurement with Adaptive Filtering

2014 ◽  
Vol 578-579 ◽  
pp. 1053-1058
Author(s):  
Liang Zhong Qin ◽  
Hua Fei Zhou ◽  
Zi Ling Xie ◽  
Cheng Yuan Lu
Keyword(s):  
Adaptive Filter ◽  
Digital Camera ◽  
Displacement Component ◽  
Displacement Measurement ◽  
Displacement Monitoring ◽  
Long Distance ◽  
Structural Dynamic ◽  
Primary Input ◽  
Dynamic Displacement ◽  
Dynamic Excitations

Displacement is a good descriptor of the structural behavior and safety status. However, measuring displacement of structures under dynamic excitations is still a challenging task. Videogrammetry shows great potential for dynamic displacement measurement, benefiting from its non-contact and long-distance characteristics. Nevertheless, its all-weather performance has to be fully evaluated before gaining wide applications. This study therefore carried out an investigation into the environmental effects of the all-weather videogrammetry for structural dynamic displacement monitoring. First, long-term outdoor dynamic displacement monitoring tests were carried out. Virtual structural displacement was generated by a motion simulation device and monitored by a commercialized industrial digital camera. The adaptive filter was then employed to filter out noises, which had the primary input of the major displacement component and the reference input of the minor displacement component. The results show that the adaptive filter is well capable of filtering out noises and the measurement accuracy of videogrammetry is significantly enhanced.

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