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Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios. [ABSTRACT FROM AUTHOR]

This study employs advanced techniques, namely the improved multi-synchrosqueezing transform (IMSST) and the Fouriersynchrosqueezed transform (FSST), to extract the instantaneous frequency of the Humber Bridge, in the UK, in operational conditions. First, a brief overview of the relevant theories behind the techniques used is provided. These techniques are then applied to track the instantaneous frequencies within the monitored vertical acceleration data of the bridge. The instantaneous frequencies extracted by both techniques are verified against each other. The investigation further encompasses the development of three distinct regression models: a simple regression model, an expanded regression model and a regression model based on principal component analysis (PCA). The efficacy of the PCA-based model is scrutinised by generating and testing datasets that mirror the monitored data, with the Kolmogorov-Smirnov (KS) test confirming theirvalidity. The findings of this research indicate a statistically significant inverse relationship between modal frequency and acceleration intensity. Furthermore, by incorporating wind effect considerations into the regression analysis, the R-squared values in both the expanded and PCA-based models exhibit substantial improvements. This demonstratesthe performance of the enhanced model in capturing the dynamics of the bridge. The robustness of the PCA-based model is furthercorroborated by its performance during testing, proving its potential as a reliable tool in structural health monitoring (SHM). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of tuned liquid dampers (TLDs) as an alternative to reduce the response of flexible structures with a low amount of structural damping is a viable option. The correct characterization of the dynamic properties of the TLD plays an important role in the performance of the TLD-main structure system. This work presents the results of an experimental study to evaluate the dynamic properties of a scaled rectangular TLD using high-speed videos. For the experimental investigation, a scaled rectangular TLD is subjected to lateral displacement of the sinusoidal type with amplitudes that range from 5 to 40 mm and frequency equal to 0.625 Hz. The dynamic properties of the TLD system are identified with the use of high-speed videos with a duration of 28.96 s and recorded at 500 frames per second (fps). The recorded videos are analyzed with the software Tracker to extract time histories of wave elevation at predefined locations. The frequency and damping of the TLD system are identified from the time histories of wave elevation through Fourier analysis and free-vibration decay. The findings of this study revealed that the identified dynamic properties of the TLD by using high-speed videos presented small differences with respect to the target values, with errors that range from 0.93 to 2.9% for frequency and from 1.6 to 8.8% for damping, indicating that the use of high-speed videos can be an alternative to evaluate the dynamic properties of TLD systems. [ABSTRACT FROM AUTHOR]

Purpose: The real-time dynamics of the single-link flexible manipulator is a challenging problem due to its inherent instability and in situ vibration. In order to add the criticalities to these real-time dynamics, run-time vibration does play a pivotal role in designing a robust control system for the flexible robotic manipulator. Methodology: Governing differential equations and the boundary conditions are usually considered exact of the single-link manipulator and the formulation leads to an Eigenvalue problem where the elements of the matrices are in exact form. Double parametric form has been used to solve the fuzzy differential equation. Results and Conclusions: In this paper, a new idea has been introduced in the above problem for a representative single-link robotic manipulator considering the uncertainty in the associated parameter(s) in the governing differential equation, which may mimic the actual scenario of the real environment. The uncertainty has been considered in terms of a novel fuzzy model which agrees with the crisp case model too. [ABSTRACT FROM AUTHOR]

The use of tuned liquid dampers (TLDs) as an alternative to reduce the response of flexible structures with a low amount of structural damping is a viable option. The correct characterization of the dynamic properties of the TLD plays an important role in the performance of the TLD-main structure system. This work presents the results of an experimental study to evaluate the dynamic properties of a scaled rectangular TLD using high-speed videos. For the experimental investigation, a scaled rectangular TLD is subjected to lateral displacement of the sinusoidal type with amplitudes that range from 5 to 40 mm and frequency equal to 0.625 Hz. The dynamic properties of the TLD system are identified with the use of high-speed videos with a duration of 28.96 s and recorded at 500 frames per second (fps). The recorded videos are analyzed with the software Tracker to extract time histories of wave elevation at predefined locations. The frequency and damping of the TLD system are identified from the time histories of wave elevation through Fourier analysis and free-vibration decay. The findings of this study revealed that the identified dynamic properties of the TLD by using high-speed videos presented small differences with respect to the target values, with errors that range from 0.93 to 2.9% for frequency and from 1.6 to 8.8% for damping, indicating that the use of high-speed videos can be an alternative to evaluate the dynamic properties of TLD systems.

Freezing weather can introduce challenges in long-term structural health monitoring of civil structures, particularly bridges. A noticeable impact of freezing temperature is the emergence of sudden and sharp increases in structural modal frequencies, causing false alarm and mis-detection errors in change detection of civil structures. This paper proposes an innovative unsupervised data normalization method to mitigate freezing effects. The proposed method integrates locally robust principal component analysis (LRPCA) with Gaussian density distance (GDD) clustering, called GDD-LRPCA, which automatically determines the number of clusters. Initially, a training set of original modal frequencies is partitioned via the GDD clustering. Subsequently, an individual LRPCA model is fitted to each partition to extract new normalized modal frequencies insensitive to freezing effects. The groundbreaking nature of this research relies on developing an integrated unsupervised data normalizer by leveraging advanced machine learning algorithms such as local learning, robust learning, and hybrid unsupervised learning. The major advantage of the proposed method is its non-parametric nature obviating any supplementary technique for hyperparameter optimization. The validity of this method is benchmarked by real-world bridge structures along with several comparative analyses. Results demonstrate that GDD-LRPCA effectively removes the freezing effects from structural modal frequencies and outperforms its counterparts in unsupervised data normalization. • Proposing an unsupervised data normalizer for removing freezing effects from modal frequencies under statistical learning. • Leveraging cutting-edge machine learning algorithms including local learning, robust learning, and hybrid learning. • Suggesting a non-parametric framework for unsupervised data normalization without any hyperparameter optimization • Lacking the need for temperature sensor installation and measurement for removing freezing effects. [ABSTRACT FROM AUTHOR]