Hydraulic structures subjected to prolonged operational conditions inevitably experience damage and potential structural resonance, which can affect their stability and safety. Structural health monitoring (SHM) is a well-established approach to ensuring safety. However, conventional methods primarily depend on point sensors-insufficient for comprehensive monitoring of large and complex structures. Video-based non-contact monitoring techniques effectively address this limitation. This study investigates the applicability of such methods for monitoring large-scale hydraulic structures in outdoor environments under complex loading conditions. Specifically, a video-based micro-vibration monitoring method utilizing phase-based motion extraction is proposed. First, a video phase-based optical flow method is introduced to extract vibration time histories from any selected pixels or pixel groups in the video, overcoming the time-intensive nature of traditional full-frame image batch processing when capturing large-scale structures with high-speed cameras. Second, the primary factors influencing data accuracy are analyzed by evaluating various shooting angles and scales, with an aging aqueduct serving as the test subject. The results demonstrate that the proposed vibration information extraction method efficiently captures vibration time histories from high-frame-rate videos. The correlation coefficient with sensor data reaches up to 0.96, accurately representing the main frequency bands within the 0-100 Hz range, with particularly strong robustness in the 0-20 Hz range. However, lighting conditions, shooting scales, and angles can affect data accuracy.

For structural dynamics and health monitoring, vision-based vibration measurement has become an attractive approach thanks to its full-field, non-contact, and cost-effective aspect. However, conventional frame-based cameras are inherently constrained by frame rate, motion blur, and exposure duration, while producing vast amounts of redundant data. These limitations restrict their ability to capture high-frequency vibrations over extended periods or under low illumination.
In contrast, event cameras asynchronously record pixel-level brightness changes with microsecond latency and a high dynamic range. This enables high-frequency motion tracking under ambient light or uneven light condition and ensures efficient data use, as events are generated only where motion occurs.
This study presents a unified event-based framework for estimating vibration and modal parameters within a dynamic state-space formulation. Displacement information is extracted from images reconstructed from event streams, while velocity information is obtained directly from the events. By combining these complementary motion signals with a physics-based vibration model via data assimilation, the method enables accurate and continuous estimation of structural vibrations with strong robustness to noise.
Experiments were performed on a cantilever beam with an event camera and a reference accelerometer synchronized in operation, including both free and forced vibration tests. In the free-vibration tests, modal parameters were identified from the decaying response, whereas the forced-vibration setup used a shaker to apply harmonic excitation, enabling the estimation of the operational deflection shapes under steady-state conditions. The extracted modal properties closely matched the accelerometer measurements, validating the proposed approach.
The proposed framework demonstrates the capability of the event-camera-based approach to measure structural vibrations with high temporal fidelity and strong robustness to noise. It combines event data with a physics-based vibration model through data assimilation. This integration enables accurate modal identification and efficient motion estimation without the motion blur, saturation, or illumination constraints inherent to frame-based systems. These findings highlight the potential of event cameras as a robust and data-efficient sensing modality for continuous, high-frequency monitoring in structural dynamics and health assessment.

Over the past decade, phase-based motion magnification has gained widespread use for its ability to qualitatively visualize the dynamic behaviour of stiff structures by amplifying their small deformations, particularly the deflection shapes at their eigenfrequencies. Compared with image intensity-based methods such as Digital Image Correlation (DIC) and feature tracking, which rely on sufficient surface texture to function effectively, the phase-based method can still infer motion in regions with weak texture as the 2D Fourier decomposition produces a spatially coherent phase field, in which phase information is smoothly coupled across the image domain. This work presents a quantitative analysis to examine the relationship between displacement and phase. Starting from the Fourier Shift Theorem, which states that phase variations can be directly interpreted as measures of displacement, the governing equations are derived. Since phase values are inherently bounded to a range of -π to π, discontinuities occur when the true phase exceeds this range —a phenomenon known as phase wrapping. Different Fourier transform configurations and image processing parameters are explored to address the phase wrapping issue. Within the image field-of-view, phase information is extracted over time at multiple locations —each representing a distinct measurement location. The displacements derived from the image phases are then used as structural responses to perform experimental modal analysis. This approach is applied in a modal analysis on two experimental setups: a cantilever beam and a clamped–clamped beam. The subjects’ modal parameters are calculated through the PolyMAX algorithm. These results are benchmarked against reference accelerometer data, showing minimal differences —with eigenfrequency errors below 0.4% and damping ratio differences under 0.35 percentage points between the phase-based and accelerometer-based methods. Furthermore, the mode shapes obtained from the phase-based analysis exhibit significantly higher spatial resolution, owing to the full-field nature of the measurement. Finally, the analysis also highlights the trade-off between spatial resolution and spectral accuracy in the phase-based method. This can be seen in the gradual decline in accuracy of the proposed method with increasing frequency. Despite remaining challenges —such as spectral noise and limited high-frequency performance imposed by camera sampling rates— the results demonstrate that phase-based motion extraction provides a viable, low-cost, and high-resolution alternative for non-contact modal analysis.

Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a possibility of reducing the costs of the CVSHM system. However, computer-vision-based (CV) vibration measurement can be significantly contaminated by measurement errors at higher vibration frequencies and low amplitudes.

A framework for CVSHM is proposed, which allows for robust detection, localization, and assessment of the damage [1]. As illustrated in the figure, the framework consists of: (1) CV measurement of structural displacements, (2) modal data extraction, and (3) modal sensitivity-based model updating by changing the stiffness of monitored structural members. Displacement measurement is realized with the template matching technique, maximizing the zero-normalized cross-correlation function. Later, modal parameters are identified using the data-driven stochastic subspace identification method (SSI-DATA). Degrees of freedom suspected to be source of gross errors are removed. The last framework component is inspired by the augmented inverse estimate described in [2]. The proposed method additionally employs: (a) weighting of errors between the identified and model modal parameters, and (b) constraints imposed on the model updating procedure that structural stiffness parameters can only decrease with respect to the initial values. Such a method of damage assessment is termed weighted negative least square inverse estimate (WNLSIE). This method is physics-based and avoids the problems typical of machine-learning approaches, such as the need for the collection of training data or generalization of the trained model.

The proposed framework is tested using realistic synthetic videos representing vibrating truss structure. These videos are generated with physics-based graphical models (PBGM) and allow for employing the displacement ground truth data for comparison purposes [3]. Displacements of 19 truss nodes are measured and 29 truss members are monitored. Sampling frequency is 120 frames per second (fps). Excitations are unknown. The proposed framework allows for prediction of damaged truss member and its damage level, when error of measured displacement is at the level of 50 %.

The proposed framework for CVSHM is easy to implement and allows for detection, localization, and assessment of structural damage even for highly contaminated displacement data.

Bibliography

1. M. Ostrowski, B. Blachowski, B. Wójcik, M. Żarski, P. Tauzowski, Ł. Jankowski, A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations, Earthquake Engineering and Engineering Vibration 22 (2023) 1–17. https://doi.org/10.1007/s11803-023-2154-3.
2. X. Peng, F.J. Qin, Q.W. Yang, H. Chen, A Robust Estimate Method for Damage Detection of Concrete Structures Using Contaminated Data, Advances in Civil Engineering 2021 (2021) 6669958. https://doi.org/https://doi.org/10.1155/2021/6669958.
3. Y. Narazaki, F. Gomez, V. Hoskere, M.D. Smith, B.F. Spencer, Efficient development of vision-based dense three-dimensional displacement measurement algorithms using physics-based graphics models, Structural Health Monitoring 20 (2021) 1841–1863. https://doi.org/https://doi.org/10.1177/1475921720939522.