Mariusz Ostrowski

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.

In this paper, a semi-active energy transfer between vibration modes of frame structures is considered. This mechanism, enabled by lockable rotational joints, is relevant to vibration mitigation and energy harvesting. Analytical derivation of globally optimal control is found to be intractable, while greedy strategies under forced excitation may achieve only limited performance. To overcome these limitations, a reinforcement learning (RL) framework is developed. In the RL approach, control actions are evaluated based on discounted future rewards. This horizon-aware policy is shown to accumulate energy in the mode resonant with the external excitation and subsequently transfer it to a target mode, outperforming greedy control. Sensitivity analyses are conducted to identify the components that enable efficient and robust energy-transfer strategies.

The efficiency of vibration-based energy harvesters is often constrained by low vibration amplitudes and limited responsiveness beyond the resonant frequency. To overcome these limitations, mechanical amplifiers and spring bumpers can be employed to enhance excitation amplitude and magnet...

Visual inspection of civil infrastructure for structural health assessment, as performed by structural engineers, is expensive and time-consuming. Therefore, automating this process is highly attractive, which has received significant attention in recent years. With the increasing capabilities of computers, deep neural networks have become a standard tool and can be used for structural health inspections. A key challenge, however, is the availability of reliable datasets. In this work, the U-net and DeepLab v3+ convolutional neural networks are trained on a synthetic Tokaido dataset. This dataset comprises images representative of data acquired by unmanned aerial vehicle (UAV) imagery and corresponding ground truth data. The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). Data augmentation, including both image quality degradation (e.g., brightness modification, added noise) and image transformations (e.g., image flipping), is applied to the synthetic dataset. The selected neural network architectures achieve excellent performance, reaching values of 97% for accuracy and 87% for Mean Intersection over Union (mIoU) on the validation data. It also demonstrates promising results in the semantic segmentation of real-world structures captured in photographs, despite being trained solely on synthetic data. Additionally, based on the obtained results of semantic segmentation, it can be concluded that DeepLabV3+ outperforms U-net in structural component identification. However, this is not the case in the damage identification task.

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Structural vibrations have adverse effects and can lead to catastrophic failures. Among various methods for mitigation of vibrations, the semi-active control approaches have the advantage of not requiring a large external power supply. In this paper, we propose and test a sliding mode control method for the semi-active mitigation of vibrations in frame structures. The control forces are generated in a purely dissipative manner by means of on/off type actuators that take the form of controllable structural nodes. These nodes are essentially lockable hinges, modeled as viscous dampers, which are capable of the on/off control of the transmission of bending moments between the adjacent beams. The control aim is formulated in terms of the displacement of a selected degree of freedom. A numerically effective model of such a node is developed, and the proposed control method is verified in a numerical experiment of a four-story shear structure subjected to repeated random seismic excitations. In terms of the root-mean-square displacement, the control reduced the response by 48.4–78.4% on average, depending on the number and placement of the applied actuators. The peak mean amplitude at the first mode of natural vibrations was reduced by as much as 70.6–96.5%. Such efficiency levels confirm that the proposed control method can effectively mitigate vibrations in frame structures.