Automated modal identification in operational conditions and its application to bridges

2013 ◽  
Vol 46 ◽  
pp. 264-278 ◽  
Author(s):  
Filippo Ubertini ◽  
Carmelo Gentile ◽  
Annibale Luigi Materazzi
Keyword(s):  
Modal Identification ◽  
Operational Conditions ◽  
Automated Modal Identification

scholarly journals A new automated procedure of modal identification in operational conditions

2018 ◽  
Vol 211 ◽  
pp. 21003 ◽  
Author(s):  
Gabriele Marrongelli ◽  
Carmelo Gentile
Keyword(s):  
Parametric Identification ◽  
Modal Identification ◽  
Mode Shapes ◽  
Parameter Estimates ◽  
Reference List ◽  
Modal Parameter ◽  
Operational Conditions ◽  
Historic Masonry ◽  
Stabilization Diagram ◽  
Using Data

Structural Health Monitoring (SHM) strategies are aimed at the assessment of structural performance, using data acquired by sensing systems. Among the different available approaches, vibration-based methods - involving the automation of the modal parameter estimation (MPE) and modal tracking (MT) procedures - are receiving increasing attention. In the context of vibration-based monitoring, this paper presents an automated procedure of modal identification in operational conditions. The presented algorithms can be used to effectively manage the results obtained by any parametric identification method that involves the construction and the interpretation of stabilization diagrams. The implemented approach introduces improvements related to both the MPE and the MT tasks. The MPE procedure consists of three key steps aimed at: (1) filtering a high number of spurious poles in the stabilization diagram; (2) clustering the remaining poles that share same characteristics in term of modal parameters; (3) improving the accuracy of the modal parameter estimates. In the MT procedure the use of a simple statistical approach to define adaptive thresholds together with continuously updated dynamic reference list guarantee an efficient tracking of the most representative structural modes. The advantages obtained through the proposed procedures are exemplified using data continuously collected on the historic masonry tower of San Gottardo in Corte, located in the centre of Milan, Italy. In addition, the ability of the automated algorithms to identify contributions inherent to different vibration modes, even if they are characterized by closely-spaced frequencies and a low discriminant between mode shapes, will be described in details.

scholarly journals Automated band selection for Bayesian FFT modal identification Based on RetinaNet

2021 ◽  
Vol 2078 (1) ◽  
pp. 012058
Author(s):  
Chen Wang ◽  
Zhilin Xue ◽  
Yipeng Su ◽  
Binbin Li
Keyword(s):  
Operation Time ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Mode Shapes ◽  
Band Selection ◽  
Data Sets ◽  
Civil Structures ◽  
Frequency Bands ◽  
Operational Conditions ◽  
Vibration Data

Abstract Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.

scholarly journals Automated Modal Identification Based on Improved Clustering Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Gangrou Wu ◽  
Min He ◽  
Peng Liang ◽  
Chunsheng Ye ◽  
Yue Xu
Keyword(s):  
Structural Damage ◽  
Previous Method ◽  
Modal Identification ◽  
Modal Parameters ◽  
Clustering Method ◽  
Modal Data ◽  
Benchmark Experiment ◽  
Stabilization Diagram ◽  
Automated Modal Identification ◽  
Last Stage

The automated modal identification has been playing an important role in online structural damage detection and condition assessment. This paper proposes an improved hierarchical clustering method to identify the precise modal parameters by automatically interpreting the stabilization diagram. Two major improvements are provided in the whole clustering process. The modal uncertainty is first introduced in the first stage to eliminate as many as possible mathematical modal data to produce more precise clustering threshold, which helps to produce more precise clustering results. The boxplot is introduced in the last stage to assess the precision of the clustering results from a statistical perspective. Based on an iterative analysis of boxplot, the outliers of the clustering results are found out and eliminated and the precise modal results are finally produced. The Z24 benchmark experiment data are utilized to validate the feasibility of the proposed method, and comparison between the previous method and the improved method is also provided. From the result, it can be concluded that the modal uncertainty is more effective than the other modal criteria in distinguishing the mathematical modal data. The modal results by clustering process are not precise in statistic and the boxplot can find out the outliers of the clustering results and produce more precise modal results. The improved automated modal identification method can automatically extract the physical modal data and produce more precise modal parameters.

scholarly journals Automated modal identification and tracking: Application to an iron arch bridge

2016 ◽  
Vol 24 (1) ◽  
pp. e1854 ◽  
Author(s):  
Alessandro Cabboi ◽  
Filipe Magalhães ◽  
Carmelo Gentile ◽  
Álvaro Cunha
Keyword(s):  
Modal Identification ◽  
Arch Bridge ◽  
Automated Modal Identification

scholarly journals A Novel Approach for 3D-Structural Identification through Video Recording: Magnified Tracking

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1229 ◽  
Author(s):  
Yunus Harmanci ◽  
Utku Gülan ◽  
Markus Holzner ◽  
Eleni Chatzi
Keyword(s):  
Computer Vision ◽  
Motion Tracking ◽  
Video Recording ◽  
Video Camera ◽  
Diagnostic Techniques ◽  
Modal Identification ◽  
Spatial Density ◽  
Operational Conditions ◽  
Novel Approach ◽  
Motion Magnification

Advancements in optical imaging devices and computer vision algorithms allow the exploration of novel diagnostic techniques for use within engineering systems. A recent field of application lies in the adoption of such devices for non-contact vibrational response recordings of structures, allowing high spatial density measurements without the burden of heavy cabling associated with conventional technologies. This, however, is not a straightforward task due to the typically low-amplitude displacement response of structures under ambient operational conditions. A novel framework, namely Magnified Tracking (MT), is proposed herein to overcome this limitation through the synergistic use of two computer vision techniques. The recently proposed phase-based motion magnification (PBMM) framework, for amplifying motion in a video within a defined frequency band, is coupled with motion tracking by means of particle tracking velocimetry (PTV). An experimental campaign was conducted to validate a proof-of-concept, where the dynamic response of a shear frame was measured both by conventional sensors as well as a video camera setup, and cross-compared to prove the feasibility of the proposed non-contact approach. The methodology was explored both in 2D and 3D configurations, with PTV revealing a powerful tool for the measurement of perceptible motion. When MT is utilized for tracking “imperceptible” structural responses (i.e., below PTV sensitivity), via the use of PBMM around the resonant frequencies of the structure, the amplified motion reveals the operational deflection shapes, which are otherwise intractable. The modal results extracted from the magnified videos, using PTV, demonstrate MT to be a viable non-contact alternative for 3D modal identification with the benefit of a spatially dense measurement grid.

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