Computer-vision-based structural health monitoring of a truss structure subjected to unknown excitations: a robust framework

Abstract

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: CV measurement of structural displacements, modal data extraction, and 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.