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To address the limitations of current sensor systems for field applications, the research community has been actively exploring new technologies that can advance the state-of-the-practice in structural health monitoring (SHM). Thanks to the rapid advances in computer vision, the camera-based noncontact vision sensor has emerged as a promising alternative to conventional contact sensors for structural dynamic response measurement and health monitoring. Significant advantages of the vision sensor include its low cost, ease of setup and operation, and flexibility to extract displacements of any points on the structure from a single video measurement. This review paper is intended to summarize the collective experience that the research community has gained from the recent development and validation of the vision-based sensors for structural dynamic response measurement and SHM. General principles of the vision sensor systems are firstly presented by reviewing different template matching techniques for tracking targets, coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements, measurements by tracking artificial targets vs. natural targets, measurements in real time vs. by post-processing, etc. Then the paper reviews laboratory and filed experimentations carried out to evaluate the performance of the vision sensors, followed by a discussion on measurement error sources and mitigation methods. Finally, applications of the measured displacement data for SHM are reviewed, including examples of structural modal property identification, structural model updating, damage detection, and cable force estimation. [ABSTRACT FROM AUTHOR]

The emerging noncontact vision-based displacement sensor system offers a promising alternative to the conventional sensors for quantitative structural integrity assessment. Significant advantages of the noncontact vision-based sensor include its low cost, ease of operation, and flexibility to extract structural displacement responses at multiple points. This study aims to link the measured displacement data to the quantification of the structural health condition, by validating the feasibility of simultaneous identification of structural stiffness and unknown excitation forces in time domain using output-only vision-based displacement measurement. Numerical analysis are first carried out to investigate the accuracy, convergence and robustness of identified results to different noise levels, sensor numbers, and initial estimates of structural parameters. Then, experiment on a laboratory scaled beam structure is conducted. Results show that the global stiffness of the beam specimen as well as external hammer excitation forces can be successfully and accurately identified from displacement measurement at two points using one camera. The proposed output-only time-domain identification procedure utilizing vision-based displacement measurement represents a low-cost method for either periodic or long-term bridge performance assessment. [ABSTRACT FROM AUTHOR]

Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conventional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing. [ABSTRACT FROM AUTHOR]

A novel noncontact vision sensor for simultaneous measurement of structural displacements at multiple points using one camera is developed based on two advanced template matching techniques: the upsampled cross correlation (UCC) and the orientation code matching (OCM). While existing studies on vision sensors are mostly focused on the time-domain performance evaluation, this study investigates the performance in both time and frequency domains through a shaking table test of a three-story frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Excellent agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. The results of structural modal analysis based on the measurements by the vision sensor and reference accelerometers also agree well. Moreover, the identified modal parameters are used to update the finite element model of the structure, demonstrating the potential of the vision sensor for structural health monitoring applications. This study further examines the robustness of the proposed vision sensor against ill environmental conditions such as dim light, background image disturbance, and partial template occlusion, which is important for future implementation in the field. Significant advantages of the proposed vision sensor include its low cost (a single camera to remotely measure structural displacements at multiple points without installing artificial targets) and flexibility to extract structural displacements at any point from a single measurement. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To address the need for monitoring aging railway bridges, this paper presents a finite-element (FE) model updating approach using time-domain optimization based on in situ measurement of the bridge's dynamic displacement histories under trainloads. Field tests are performed on a short-span plate girder railway bridge to measure its displacement response to freight trainloads with different speeds using a low-cost remote vision sensor recently developed by the authors' group. A FE model of the bridge is developed considering train-track-bridge dynamic interactions. Sensitivity analysis is carried out to investigate the intrinsic effects of parameters of the train, track, and bridge subsystems on the dynamic response of the bridge. It is found that the bridge displacement response is primarily sensitive to the stiffness of the bridge, whereas the acceleration response is affected by many other parameters. This justifies that the bridge dynamic displacement is more suited than acceleration for updating the railway bridge stiffness. Consequently, a model updating approach is proposed using the measurement of bridge displacement response to trainloads. This approach is applied to the short-span bridge to first identify the train speed, followed by the bridge stiffness by minimizing the error between the measured and computed displacement time histories. Furthermore, this paper investigates the frequency characteristics from both the measured and computed displacements. The analysis shows that the trainload does not excite the bridge's natural modes of vibration because the frequency is much lower than the bridge's natural frequencies. Therefore, it is difficult to use the modal identification-based FE model updating methods based on the dynamic response measurement. This is commonly the case for short-span railway bridges, which tend to have high natural frequencies. The proposed periodic displacement measurement and time-domain FE model updating can be developed into an effective tool for long-term structural health monitoring of short-span railway bridges. [ABSTRACT FROM AUTHOR]

Displacements of railroad bridges under trainloads need to be closely monitored, but conventional displacement sensors have limitations for use in the field. This paper presents a new vision-based sensor system developed for remote measurement of structural dynamic displacements without requiring a specially installed target-marker panel. By implementing a robust object-search algorithm, the displacement can be accurately measured by tracking existing bridge surface features from a remote distance. The accuracy of measured dynamic displacements was first evaluated using a shaking table test. Then field tests were carried out on two railroad bridges subjected to freight trainloads traveling at various speeds. Measurements were taken remotely during the daytime and also at night from different distances with and without a target panel. Through comparison with a conventional contact-type displacement sensor, the high accuracy of the proposed nontarget remote-sensor system was demonstrated in the realistic field environments. From the measured displacement time histories, frequency-domain characteristics associated with the train-bridge systems were further analyzed, confirming the capability of the vision system in measuring high-frequency components. By targeting existing features on a structure without requiring a target panel installed on a fixed location of the structure, the vision sensor system developed in this study provides the flexibility to easily change displacement measurement locations, in addition to other advantages, such as easy setup and no need to access the structure. [ABSTRACT FROM AUTHOR]

This paper develops a vision-based displacement measurement system for remote monitoring of vibration of large-size structures such as bridges and buildings. The system consists of one or multiple video cameras and a notebook computer. With a telescopic lens, the camera placed at a stationary point away from a structure captures images of an object on the structure. The structural displacement is computed in real time through processing the captured images. A robust object search algorithm developed in this paper enables accurate measurement of the displacement by tracking existing features on the structure without requiring a conventional target panel to be installed on the structure. A sub-pixel technique is also proposed to further reduce measurement errors cost-effectively. The efficacy of the vision system in remote measurement of dynamic displacements was demonstrated through a shaking table test and a field experiment on a long-span bridge. [ABSTRACT FROM PUBLISHER]

Summary: Computer vision‐based displacement measurement techniques can be affected by the optical turbulence in the field in hot weather. The optical turbulence can be observed naturally when viewing a thick layer of heated air. The nonuniform density distribution of the air due to heat creates spatial variations in refraction indexes, resulting in distortions in video images and measurement errors in displacements obtained from distorted videos. The effect of optical turbulence on vision‐based displacement sensing is first illustrated in the field test conducted in hot weather. Then, the statistical characteristics of the optical‐turbulence‐induced measurement errors are analyzed and confirmed. And a comprehensive optical‐turbulence error alleviation technique, which has two steps, is designed based on the statistical studies. A previously developed high‐performance multitarget vision‐based displacement measurement technique is utilized in the first step to track multiple targets. Distortions of multiple targets are estimated simultaneously continuously. Targets with the least distortions in each frame are identified and their displacements are extracted as the primary displacement. To further alleviate errors in the primary displacement, an adaptive optical‐turbulence error filter is formulated in the second step based on the statistical characteristics of the optical‐turbulence errors. Validations of the optical‐turbulence error alleviation technique are performed in both laboratory and field tests. Field tests are conducted on the Williamsburg Bridge in the natural environment. After alleviating optical‐turbulence and camera vibration errors, frequency domain analyses are conducted on vibrational displacements of the Williamsburg Bridge in response to train passing. [ABSTRACT FROM AUTHOR]