Full-Field Bridge Deflection Monitoring with Off-Axis Digital Image Correlation

Experimental Study on Measuring and Tracking Structural Displacement Based on Surveillance Video Image Analysis

The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment.

Fiber Optic Sensing Technology and Vision Sensing Technology for Structural Health Monitoring

Structural health monitoring is currently a crucial measure for the analysis of structural safety. As a structural asset management approach, it can provide a cost-effective measure and has been used successfully in a variety of structures. In recent years, the development of fiber optic sensing technology and vision sensing technology has led to further advances in structural health monitoring. This paper focuses on the basic principles, recent advances, and current status of applications of these two sensing technologies. It provides the reader with a broad review of the literature. It introduces the advantages, limitations, and future directions of these two sensing technologies. In addition, the main contribution of this paper is that the integration of fiber optic sensing technology and vision sensing technology is discussed. This paper demonstrates the feasibility and application potential of this integration by citing numerous examples. The conclusions show that this new integrated sensing technology can effectively utilize the advantages of both fields.

Generalized Scale Factor Calibration Method for an Off-Axis Digital Image Correlation-Based Video Deflectometer

When using off-axis digital image correlation (DIC) for non-contact, remote, and multipoint deflection monitoring of engineering structures, accurate calibration of the scale factor (SF), which converts image displacement to physical displacement for each measurement point, is critical to realize high-quality displacement measurement. In this work, based on the distortion-free pinhole imaging model, a generalized SF calibration model is proposed for an off-axis DIC-based video deflectometer. Then, the transversal relationship between the proposed SF calibration method and three commonly used SF calibration methods was discussed. The accuracy of these SF calibration methods was also compared using indoor rigid body translation experiments. It is proved that the proposed method can be degraded to one of the existing calibration methods in most cases, but will provide more accurate results under the following four conditions: (1) the camera's pitch angle is more than 20°, (2) the focal length is more than 25 mm, (3) the pixel size of the camera sensor is more than 5 um, and (4) the image y-coordinate corresponding to the measurement point after deformation is far from the image center.

The Simulation-Based Approach for Random Speckle Pattern Representation in Synthetically Generated Video Sequences of Dynamic Phenomena

Structural health monitoring systems that employ vision data are under constant development. Generating synthetic vision data is an actual issue. It allows, for example, for obtention of additional data for machine learning techniques or predicting the result of observations using a vision system with a reduced number of experiments. A random speckle pattern (RSP) fixed on the surface of the observed structure is usually used in measurements. The determination of displacements of its areas using digital image correlation (DIC) methods allows for extracting the structure's deformation in both static and dynamic cases. An RSP modeling methodology for synthetic image generation is developed within this paper. The proposed approach combines the finite element modeling technique and simulation results with the Blender graphics environment to generate video sequences of the mechanical structure with deformable RSP attached to it. The comparative analysis showed high compliance of the displacement between the synthetic images processed with the DIC method and numerical data.

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.

Development of a Remote Displacement Measuring Laser System for Bridge Inspection

Measuring displacement is essential for assessing the safety of bridges. Non-contact sensors such as vision sensors can precisely measure displacement but may be expensive or incapable of micro-scale measurement at a low cost, unlike contact displacement sensors, which are economical but challenging to install. This study proposes an economical, remote non-contact sensor system. The system comprises a laser beam transmitter and a light receiver, deriving the displacement based on the position where the laser beam is irradiated to the light-receiving surface. To measure this, the light receiver was installed at the measurement point and included a wireless communicator to transmit the displacement data. A displacement experiment was conducted to evaluate the performance. The results confirmed that precise displacement measurements were possible at a resolution of 100 µm. For bridge load tests, a light receiver under a bridge was installed, laser beams irradiated to the light-receiving surface from a distance, and the displacement was measured for each test and compared with the values measured by a conventional contact sensor. The results were highly consistent with those of the existing sensor, indicating that the proposed sensor system applies to bridge loading tests and the safety diagnosis for various structures.

Application of Digital Image Correlation in Structural Health Monitoring of Bridge Infrastructures: A Review

A vision-based approach has been employed in Structural Health Monitoring (SHM) of bridge infrastructure. The approach has many advantages: non-contact, non-destructive, long-distance, high precision, immunity from electromagnetic interference, and multiple-target monitoring. This review aims to summarise the vision- and Digital Image Correlation (DIC)-based SHM methods for bridge infrastructure because of their strategic significance and security concerns. Four different bridge types were studied: concrete, suspension, masonry, and steel bridge. DIC applications in SHM have recently garnered attention in aiding to assess the bridges’ structural response mechanisms under loading. Different non-destructive diagnostics methods for SHM in civil infrastructure have been used; however, vision-based techniques like DIC were only developed over the last two decades, intending to facilitate damage detection in bridge systems with prompt and accurate data for efficient and sustainable operation of the bridge structure throughout its service life. Research works reviewed in this article demonstrated the DIC capability to detect damage such as cracks, spalling, and structural parameters such as deformation, strains, vibration, deflection, and rotation. In addition, the reviewed works indicated that the DIC as an efficient and reliable technique could provide sustainable monitoring solutions for different bridge infrastructures.

Indirect Measurement of Loading Forces with High-Speed Camera

In the last few decades, there has been a significant increase in interest in developing, constructing, and using structural health monitoring (SHM) systems. The classic monitoring system should, by definition, have, in addition to the diagnostic module, a module responsible for monitoring loads. These loads can be measured with piezoelectric force sensors or indirectly with strain gauges such as resistance strain gauges or FBG sensors. However, this is not always feasible due to how the force is applied or because sensors cannot be mounted. Therefore, methods for identifying excitation forces based on response measurements are often used. This approach is usually cheaper and easier to implement from the measurement side. However, in this approach, it is necessary to use a network of response sensors, whose installation and wiring can cause technological difficulties and modify the results for slender constructions. Moreover, many load identification methods require the use of multiple sensors to identify a single force history. Increasing the number of sensors recording responses improves the numerical conditioning of the method. The proposed article presents the use of contactless measurements carried out with the help of a high-speed camera to identify the forces exiting the object.

Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study

Vibrational measurements play an important role for structural health monitoring, e.g., modal extraction and damage diagnosis. Moreover, conditions of civil structures can be mostly assessed by displacement responses. However, installing displacement transducers between the ground and floors in real-world buildings is unrealistic due to lack of reference points and structural scales and complexity. Alternatively, structural displacements can be acquired using computer vision-based motion extraction techniques. These extracted motions not only provide vibrational responses but are also useful for identifying the modal properties. In this study, three methods, including the optical flow with the Lucas–Kanade method, the digital image correlation (DIC) with bilinear interpolation, and the in-plane phase-based motion magnification using the Riesz pyramid, are introduced and experimentally verified using a four-story steel-frame building with a commercially available camera. First, the three displacement acquiring methods are introduced in detail. Next, the displacements are experimentally obtained from these methods and compared to those sensed from linear variable displacement transducers. Moreover, these displacement responses are converted into modal properties by system identification. As seen in the experimental results, the DIC method has the lowest average root mean squared error (RMSE) of 1.2371 mm among these three methods. Although the phase-based motion magnification method has a larger RMSE of 1.4132 mm due to variations in edge detection, this method is capable of providing full-field mode shapes over the building.