Accurate Deformation Monitoring on Bridge Structures Using a Cost-Effective Sensing System Combined with a Camera and Accelerometers: Case Study

Abstract

Information on deformation is critical for bridge condition evaluation but accurate characterisation, usually via discrete displacement measurements, remains a challenging task. Vision-based systems are promising tools, possessing advantages of easy installation, low cost and adequate resolution in time and frequency domains. However, vision-based monitoring faces several field challenges and might fail to achieve the required level of working performance in some real-world test conditions e.g. involving low-contrast patterns and mounting instability of optical sensors. To make the best use of the potential of vision-based systems, a mixed sensing system consisting of a consumer-grade camera and an accelerometer is proposed in this study for accurate displacement measurement. The system considers automatic compensation of camera shake and involves autonomous data fusion process for noise reduction. The proposed system is demonstrated through a field monitoring test on a short-span railway bridge and is validated to offer higher accuracy and wider frequency range than using a camera alone. Displacement data by the mixed system are demonstrated to be viable for estimating bridge influence line, indicating the potential for bridge condition assessment.

Measuring the interstory drift of buildings by a smartphone using a feature point matching algorithm

“…As the light condition was worst in test D‐5, there were more outliers in the y ‐direction as obtained by the HUAWEI Mate 10 Pro and white iPhone 6. Given this condition, a low‐pass filter with a cutoff frequency of 0.25 Hz was used on the raw data . As a result, the video taken under the low light condition was monitored by adjusting the parameters in the SURF code.…”

“…Given this condition, a low-pass filter with a cutoff frequency of 0.25 Hz was used on the raw data. 27 As a result, the video taken under the low light condition was monitored by adjusting the parameters in the SURF code. If many outliers are observed in the results, a low-pass filter could be used to reduce the noise.…”

“…They then compared the pros and cons of the three methods under different environments, including low‐contrast patterns and lighting changes. Xu et al used the correlation‐based template matching method to measure the displacement of the railway bridge, and data fusion with the collocated acceleration data was used to denoise the image measurement caused by the camera vibration and low‐contrast patterns. Lydon et al used a modification kit for a GoPro camera to monitor the displacement of a bridge, and SURF was used as the monitoring theory along with the Capture App that was designed to synchronize the time between different cameras.…”

Estimation of the Lateral Dynamic Displacement of High-Rise Buildings under Wind Load Based on Fusion of a Remote Sensing Vibrometer and an Inclinometer

“…The third category of approaches for displacement measurement relates to fusing data from multiple sensors, such as accelerometer combined with GPS [28,29,32,33], vision system [34], radar system [35], laser system [36], strain gauge [37], or inclinometer [38]. However, the acceleration integration error still exists and influence the displacement estimation [28,29].…”

Noncontact Sensing Technologies for Bridge Structural Health Assessment

“…Papers focused on DIC include studies using two-dimensional (2D) DIC on the precision of strains on surfaces measured using a wide-angle camera by Halding et al (2019), strain fields on masonry rail bridges by Dhanasekar et al (2019), and deflection measurements in bridges by Alipour et al (2019). A hybrid system consisting of a consumergrade camera and an accelerometer was presented by Xu et al (2019) to overcome sensing errors due to camera shake and poor resolution when tracking low-contrast natural patterns in pursuit of bridge displacement measurements. Chen and Chang (2019) present an enhanced digital sampling moiré method using the phase distribution of moiré fringes in successive images to measure both in-plane translation and rotation.…”