Automated output-only dynamic identification of civil engineering structures

Rainieri, Carlo; Fabbrocino, Giovanni

doi:10.1016/j.ymssp.2009.10.003

Cited by 152 publications

(63 citation statements)

References 12 publications

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“…On one side, frequencies, contrary to mode shapes or damping ratios, are easily identified with 2 Shock and Vibration a good accuracy even from ambient vibration tests (AVT). The recent improvement and diffusion of effective automated operational modal analysis (OMA) techniques [8][9][10][11][12][13] have further contributed to elect frequencies as privileged damage detection parameter. On the other hand, natural frequencies are often not especially sensitive to local damage, while they are strongly affected by environmental and operational conditions, such as temperature, humidity [5,14], and, particularly in the case of tall structures, wind intensity [15].…”

Section: Introductionmentioning

confidence: 99%

Effect of Environmental Conditions on the Modal Response of a 10-Story Reinforced Concrete Tower

Regni

Arezzo

Carbonari

et al. 2018

Shock and Vibration

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We analyse the effect of temperature and wind velocity on the natural frequencies and modal damping ratios of the Faculty of Engineering Tower at the Università Politecnica delle Marche, a 10-story reinforced concrete frame building, permanently monitored with low-noise accelerometers. The data recorded over the first 5 months of monitoring demonstrate that temperature variations and wind intensity have a clear effect on the first three natural frequencies and the corresponding damping ratios. Temperature is positively correlated to the first and second frequencies, corresponding to shear displacement modes and negatively correlated to the third frequency, corresponding to a torsional mode. All frequencies are positively correlated to wind velocity and changes in damping ratios are inversely correlated to any change in frequency. A mechanical explanation of these phenomena is offered, based on a critical review of literature case studies. These results suggest that using changes in modal parameters for damage detection always requires accurate knowledge of the correlation between modal parameters and environmental quantities (temperature, humidity, and wind velocity), an information which is only available through long-term continuous monitoring of the structural response.

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Section: Introductionmentioning

confidence: 99%

Effect of Environmental Conditions on the Modal Response of a 10-Story Reinforced Concrete Tower

Regni

Arezzo

Carbonari

et al. 2018

Shock and Vibration

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“…Often the input is not clearly known, so that output-only procedures are utilized, such as Stochastic Subspace Identification (SSI) in the time domain and Enhanced Frequency Domain Decomposition (EFDD) in the frequency domain. These methods have also been implemented in softwares such as ARTEMIS [9] and LEONIDA [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Identification Techniques to Numerically Detect the Structural Damage

Foti

2013

TOBCTJ

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Damage detection in civil engineering structures using changes in measured modal parameters is an area of research that has received notable attention in literature in recent years. In this paper two different experimental techniques for predicting damage location and severity have been considered: the Change in Mode Shapes Method and the Mode Shapes Curvature Method. The techniques have been applied to a simply supported finite element bridge model in which damage is simulated by reducing opportunely the flexural stiffness EI. The results show that a change in modal curvature is a significant damage indicator, while indexes like MAC and COMAC -extensively and correctly used for finite element model updating -lose their usefulness in order to damage detection.

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“…Studies on automated modal parameter identification have been presented. The frequency‐domain methods are generally based on the peak‐identification of the power spectral density (PSD) or the singular value decomposition of the output PSD matrix. As pointed out by Rainieri and Fabbrocino, the frequency‐domain automated modal identification methods have the following limitations: The threshold‐based peak‐pick property requires static settings of thresholds. A preliminary calibration phase at each new application is required. The thresholds for peak‐pick are sensitive to noise. …”

Section: Introductionmentioning

confidence: 99%

“…Studies on automated modal parameter identification have been presented. The frequency-domain methods 15,16 are generally based on the peak-identification of the power spectral density (PSD) or the singular value decomposition of the output PSD matrix. As pointed out by Rainieri and Fabbrocino, 17 the frequency-domain automated modal identification methods have the following limitations:…”

Section: Introductionmentioning

confidence: 99%

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

Mao

Wang

et al. 2019

Struct Control Health Monit

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Summary Obtaining timely information about the health of civil infrastructure is critical to ensuring safe and reliable operation. Structural health monitoring has been proposed as a means to provide such information; however, most structural health monitoring systems provide only raw data, rather than actionable information. It is necessary to develop automated modal analysis strategies that can provide near real‐time dynamic information regarding the in‐service state of a bridge, which is essential to vibration control, finite element model calibration, and damage detection for safety and serviceability condition assessment. This study presents an automated framework to extract structural modal parameters from the stabilization diagram using a parametric modal identification method such as stochastic subspace identification. The framework focuses on the automated modal analysis issues of an in‐service long‐span bridge with close‐frequency modes. The presented framework is validated using experimental tests of a 1.8‐m 18‐story laboratory model. Subsequently, data from Sutong Cable‐Stayed Bridge are employed to demonstrate its potential usage in the field. Finally, an application of the automated framework is presented to identify and track the modal parameters of the deck of Sutong Cable‐Stayed Bridge for 20 days. Results show that the presented framework can successfully extract the structural modal parameters with good accuracy and robustness, hence can provide a reliable technical support for in‐service monitoring of long‐span bridges.

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Automated output-only dynamic identification of civil engineering structures

Cited by 152 publications

References 12 publications

Effect of Environmental Conditions on the Modal Response of a 10-Story Reinforced Concrete Tower

Effect of Environmental Conditions on the Modal Response of a 10-Story Reinforced Concrete Tower

Dynamic Identification Techniques to Numerically Detect the Structural Damage

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

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