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.

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.

This study explores and compares several classical computer-vision sub-pixel algorithms for real-time extraction of displacements, with the aim of enabling comprehensive and cost-effective structural monitoring systems that are easy to install and operate. A preliminary analysis is carried out to evaluate the robustness of each algorithm against different types of noise, which can strongly influence measurement accuracy. Algorithm performance is quantified using statistical error metrics, such as mean error, standard deviation, and maximum error amplitude. The evaluation proceeds in three phases. First, synthetic videos are generated to reproduce controlled noise scenarios, allowing a systematic assessment of the influence of lens blur, motion blur, and camera sensor electronic noise. Afterwards, in the second phase experimental measurements made in controlled laboratory conditions are illustrated to validate the results obtained through synthetic videos for a given hardware configuration. Finally, a third phase uses experimental measurements made in an external environment (bridge deck under vehicular traffic), to provide an analysis of the effect of noise in complex real-world conditions.

Abstract: Structural modal parameters such as natural frequency, damping ratio, and mode shape are essential for assessing structural integrity. While accelerometer-based monitoring methods are reliable, their single-point nature limits spatial information. As a non-contact alternative, camera-based computer vision approaches have gained attention. However, long measurement distances and the high stiffness of civil structures often result in extremely small observable motions, making traditional visual methods (e.g., optical flow, edge or feature tracking) ineffective.

This study proposes an enhanced phase-based motion estimation approach, termed AS-2DHPME (Angular Steerable 2D Hilbert Phase-Based Motion Estimation), to accurately extract subtle motions from multiple angular directions in videos. Furthermore, two new concepts—Directional Deflection Shape (DMS) and Synthetic Deflection Shape (SMS)—are introduced to represent mode shapes aligned with dominant vibration directions at individual and combined frequencies. Experiments on a cantilever beam under hammer excitation demonstrate that AS-2DHPME effectively identifies full-field DDS and SDS without prior knowledge of excitation direction or magnitude. The results highlight the method’s potential for high-resolution, full-field structural vibration analysis in real-world monitoring applications.

This study presents a UAV-based vision system for high-precision, non-contact identification of cable forces in long-span bridges. The system employs a hovering UAV equipped with high-resolution cameras to capture detailed vibration videos of bridge cables, enabling flexible and efficient data collection without the need for physical contact or complex installation. The recorded videos are processed through a phase-based motion estimation algorithm that extracts sub-pixel displacement signals by analyzing temporal phase variations in frequency domain, accurately reconstructing the true dynamic response of cables even under small-amplitude oscillations. To eliminate motion artifacts caused by UAV instability, background vibrations, and sampling noise, a combined filtering framework based on Butterworth band-pass filtering and Singular Spectrum Analysis (SSA) is implemented. This hybrid approach effectively suppresses non-structural disturbances and yields stable, high-fidelity displacement signals suitable for further frequency-domain analysis. Subsequently, high-resolution spectral estimation is performed using the Amplitude and Phase Estimation (APES) method, which minimizes spectral leakage and enhances frequency resolution compared with conventional Fourier techniques. To further refine the spectral representation, sparse spectrum reconstruction (SSR) is incorporated, enabling accurate extraction of multiple modal frequencies within narrow frequency bands. These extracted frequencies are then integrated into a mechanical vibration model to estimate the corresponding cable forces with high precision. The proposed workflow eliminates the need for contact sensors, reduces field deployment costs, and supports simultaneous monitoring of multiple cables via coordinated UAV operation. Field experiments conducted on full-scale cable-stayed bridges demonstrate that the estimated cable forces deviate by less than 0.5% from sensor-based measurements and by less than 4% from design values, confirming the accuracy, reliability, and practical applicability of the method. Overall, this UAV-based vision system establishes a scalable and efficient framework for structural health monitoring of long-span bridges, integrating advanced optical measurement, signal processing, and mechanical modeling into a unified workflow. The proposed approach provides a robust and practical tool for cable force estimation and dynamic characterization, contributing to intelligent maintenance, safety assessment, and early warning of structural anomalies in large-scale bridge engineering.

Space structures must be designed to endure both quasi-static and vibro-acoustic loads encountered during launch. For solar arrays in particular, engineers must ensure that the panels remain undamaged during this critical phase, since any failure would compromise the spacecraft’s power supply once in orbit. While the design and validation of models under static loads are well established, validating equivalent models under dynamic conditions remains a major challenge.

This difficulty arises primarily from the limited accuracy in modelling interface forces, which hinders reliable simulation of forced vibrations. The main scientific challenge lies in characterising and modelling interface forces at vibration levels where multiple nonlinear phenomena coexist. For instance, interface friction and contact stiffness can vary with amplitude and time, making their modelling inherently complex.

Over the past decades, experimental techniques for characterising nonlinear joint behaviour have advanced considerably, with researchers developing sophisticated tools to measure and analyse nonlinear vibrations. Nonetheless, most state-of-the-art approaches rely on contact sensors, as large vibration amplitudes can restrict noncontact methods such as laser Doppler vibrometry. Conversely, the transient nature of strongly nonlinear vibrations can present new challenges for vibration-based digital cameras. In addition, solar arrays cannot be speckled, which further constrains the application of digital image correlation (DIC) techniques.

This study investigates the integration of a 3D Scanning Laser Doppler Vibrometer (SLDV) and Digital Image Correlation to analyse the nonlinear dynamic behaviour of a solar array prototype. A comprehensive experimental campaign using accelerometers was first conducted, effectively capturing nonlinear vibrations arising from frictional forces. One resonance mode exhibited a nonlinear response typical of snap-through systems, which proved to be highly sensitive to small variations in excitation levels. The present work focuses on this resonance, combining accelerometers with noncontact measurement techniques to capture its complex transient behaviour.

Tests are carried out using stepped-sine excitation, combined with a train of TTL signals to trigger digital camera acquisitions. The 3D SLDV operates in continuous scanning mode, performing fast line scans along the digital camera’s line of sight. The integration of these three sensors enables the measurement of both in-plane and out-of-plane motions, providing complementary insights into the system’s dynamic response.

The primary objective is to characterise the unstable deformation associated with the observed snap-through behaviour and to generate reference data useful for engineers developing solar array structures. This type of nonlinearity can be induced by pre-stress in the stowed configuration typical of launch conditions. Understanding and mitigating such behaviour is essential to prevent stress peaks that may damage the solar cells and to enhance the reliability of solar array systems during the launch phase.