An Innovative Structural Dynamic Identification Procedure Combining Time Domain OMA Technique and GA

Russotto, Salvatore; Matteo, Alberto Di; Pirrotta, Antonina

doi:10.3390/buildings12070963

Cited by 8 publications

(6 citation statements)

References 49 publications

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“…A widely used technique for the structural monitoring of bridges is Operational Modal Analysis (OMA) [1] [2]. OMA-based methods make the identification of dynamic parameters such as frequencies, modal shapes, and damping ratios possible using data collected from sensors placed directly on the structure while it is under its operating conditions.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Bridge Interaction based approach for the monitoring of bridges through an electric mobile platform

Fiandaca,

Denoël,

Pirrotta

2024

J. Phys.: Conf. Ser.

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The main purpose of the Structural Health Monitoring (SHM) techniques applied in bridges is to monitor many structural parameters to prevent serious damage that may eventually lead to collapse of the entire structure. To date, Operational Modal Analysis (OMA) methods are widely seen as the most reliable SHM technique. These encompass a series of procedures for deriving the modal parameters of a structure using the data acquired under its operating conditions, without recording the external excitation. However, traditional OMA methods generally require expensive setup and time-consuming procedures. With the aim of overcoming these issues, in this paper, an innovative Vehicle-Bridge Interaction (VBI) based approach is investigated as a low-cost monitoring system for the identification of the structural modal parameters. These are obtained from the analysis in the frequency domain of the measured vehicle’s vibration response during several passages on the structure. Therefore, the proposed approach has been adopted in an experimental campaign on a pedestrian bridge in the city of Palermo (Italy). In particular, a very low-cost setup comprising a scaled-up electric vehicle as a mobile platform and few accelerometers has been used.

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Section: Introductionmentioning

confidence: 99%

A Vehicle Bridge Interaction based approach for the monitoring of bridges through an electric mobile platform

Fiandaca,

Denoël,

Pirrotta

2024

J. Phys.: Conf. Ser.

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“…Due to their stochastic framework, OMA methods in general result to be of very difficult usage for people that are not familiar with signal analysis and stochastic dynamics. For this reason, other methods based on the analytical signal and on the modal decomposition of the correlation functions' matrix have been recently proposed [23,24]. However, these methods can be applied only if the matrix containing the identified modal shapes is a square matrix, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Through the use of this method it is possible to estimate natural frequencies, modal shapes and damping ratios of a structural system. Since the dynamic identification performed by using the analytical signal has been firstly used in a deterministic framework [25][26][27] and then successfully extended to OMA [23,24,28], the proposed method is based on filtering techniques and on the decomposition of the matrix that contains the analytical signals of the output process' correlation functions. A practical application consisting in the dynamic identification of Chiaramonte palace in Palermo is presented and the results obtained are compared with those obtained by using EFDD and SSI.…”

Section: Introductionmentioning

confidence: 99%

An innovative only-output method to identify a structural system

Russotto,

Denoël,

Pirrotta

2024

J. Phys.: Conf. Ser.

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Structural Health Monitoring (SHM) is nowadays common in many branches of engineering since it allows to have a continuous or periodic report of the structural conditions and therefore to promptly intervene if there are incipient damages. The first step to perform a SHM is the identification of the dynamic parameters, i.e. natural frequencies, damping ratios and modal shapes, and it is a crucial step since a modification of the structural parameters can be a direct consequence of structural damages. Among the structural identification methods, Operational Modal Analysis (OMA) methods have received increasing attention from the researchers since they do not require the knowledge of the structural excitation that is due to ambient vibrations and that is usually modeled as a white noise. This aspect makes this kind of methods cheaper and simpler than the classical Experimental Modal Analysis (EMA) methods. In this paper an innovative OMA method is proposed. It is a semi - automated method that allows to identify natural frequencies, damping ratios and modal shapes of a structural system and that can be used also from users that have not knowledge in stochastic dynamics and signal analysis. First of all, the modal shapes are estimated through the use of signal filtering techniques applied on the stochastic properties of the output process and then natural frequencies and damping ratios can be estimated from the mono - component analytical signals obtained by performing a decomposition of the analytical signals matrix. The proposed method has been used to perform the dynamic identification of a real historic building situated in Palermo, i.e. Chiaramonte palace, and the results obtained have been compared with those obtained by using other OMA methods.

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“…As far as the input process generation are concerning it can be used for different purposes [3][4][5][6][7] and, if the input process is Gaussian, then it is fully characterized in probabilistic setting by its Power Spectral Density (PSD). The generation of samples of a Gaussian multi-variate input process can be performed in different ways; such an example Auto Regressive (AR) or Auto Regressive Moving Average (ARMA) techniques can be used for this purposes [1,8].…”

Section: Introductionmentioning

confidence: 99%

Digital simulation of multi-variate stochastic processes

Russotto

2023

Theoretical and Applied Mechanics - AIMETA 2022

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Abstract. Stochastic dynamic analysis of linear or nonlinear multi-degree-of-freedom systems excited by multi-variated processes is usually conducted by using digital Monte Carlo (MC) simulation. Since in structural systems few modal shapes contribute to the response in the nodal space, the computational burden of MC simulation is mainly related to the digital simulation of the input process. Usually, the generation of multi-variated samples of Gaussian input process is performed with the aid of the Shinozuka formula. However, since in this procedure the stochastic process is given as a summation of waves with random amplitude amplified by the square root of the power spectral density, the randomness is due to a random phase angle of each wave, therefore a very large number of waves is required to reach the Gaussianity, i.e. the process is only asymptotically stable. Moreover, the computational burden increases in case of multi-variated processes. The paper aims to drastically reduce the generation time of the input process through the use of a two-step procedure. In the first step, by using the Priestley formula, each wave is normally distributed. This first aspect allows to drastically reduce the computational effort for the mono-variate process since few waves are sufficient to reach the Gaussianity. In the second step, the multi-variate process is reduced as a summation of independent fully coherent vectors if the quadrature spectrum (q-spectrum) can be neglected. An application of digital simulation of the wind velocity field is discussed to prove the efficiency of the proposed approach.

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An Innovative Structural Dynamic Identification Procedure Combining Time Domain OMA Technique and GA

Cited by 8 publications

References 49 publications

A Vehicle Bridge Interaction based approach for the monitoring of bridges through an electric mobile platform

A Vehicle Bridge Interaction based approach for the monitoring of bridges through an electric mobile platform

An innovative only-output method to identify a structural system

Digital simulation of multi-variate stochastic processes

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