Identification of shear buildings using an instrumental variable method and linear integral filters

Concha, Antonio; Garrido, R.; Álvarez-Icaza, Luis

doi:10.1016/j.jsv.2016.09.002

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Note that smaller value of the forgetting factor may produce large variations in the parameter estimation. 38 Applying this initial value, the forgetting factors for the structural states 2 to 9 correspond to 0.9901, 0.9902, 0.9903, 0.9904, 0.9905, 0.9906, 0.9907, and 0.9908, respectively. Since the recursive estimation in the online learning algorithm begins with the first state, one does not need to assign the forgetting factor to this normal condition.…”

Section: Ar Modelingmentioning

confidence: 99%

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Entezami

Shariatmadar

Karamodin

2018

Structural Health Monitoring

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Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.

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Section: Ar Modelingmentioning

confidence: 99%

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Entezami

Shariatmadar

Karamodin

2018

Structural Health Monitoring

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“…Second, use (2) to directly determine G 1 The same procedure should be followed to determine G 2 from D. For this example, we see that G 2 = G 1 . Second, use (2) to directly determine G 1 The same procedure should be followed to determine G 2 from D. For this example, we see that G 2 = G 1 .…”

Section: Example 2: Shear Buildingmentioning

confidence: 99%

“…[1][2][3] In this regard, two main categories can be recognized in the literature: online and offline methods. The idea behind these methods is to use the measured response of a structure (usually displacement, velocity, or acceleration) to estimate part of its unknown properties.…”

Section: Introductionmentioning

confidence: 99%

“…The idea behind these methods is to use the measured response of a structure (usually displacement, velocity, or acceleration) to estimate part of its unknown properties. [1][2][3] In this regard, two main categories can be recognized in the literature: online and offline methods. In the online methods, the estimation is done simultaneously with the measurements.…”

Section: Introductionmentioning

confidence: 99%

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A new method for online identification of civil structures: Virtual synchronization

Ghaderi

Amini

2018

Adaptive Control & Signal

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This paper presents a new approach, named Virtual Synchronization Method (VSM), for online identification of stiffness and viscous damping in a civil structure while it is subjected to a harmonic ground motion acceleration. It is assumed that displacement and velocity of the structure relative to the ground are measured in real time. They are used to define a virtual system similar to the actual structure. Proper controllers and estimation functions are designed to force the states of this virtual system to follow those of the actual structure.Theoretical results are derived to study on what conditions, after the two systems synchronized, the estimation functions converge to the true stiffness and damping of the actual structure. Provided numerical examples show that VSM is a promising method for online identification of civil structures and should be the subject of further studies. KEYWORDS adaptive system identification, online identification of structures, real-time estimation of damping and stiffness, smart structures, Virtual Synchronization Method 16

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“…12 Many researchers have made a lot of efforts in the identification of structural parameters with different methods such as Bayesian network, Kalman filter, Likelihood estimation. 13–18 Udwadia et al 19 put forward that the stiffness and damping of a building story can be uniquely identified with the conditions of the recorded absolute accelerations. Takewaki and Nakamura 20 taken advantage of the limited earthquake records and the knowledge of the floor masses to identify the stiffness and damping of building structures.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Xue

Wen

Huang

et al. 2018

International Journal of Distributed Sensor Networks

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Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

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Identification of shear buildings using an instrumental variable method and linear integral filters

Cited by 7 publications

References 34 publications

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

A new method for online identification of civil structures: Virtual synchronization

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

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