Damping study of composites using wavelet analysis

Bhattacharjee, Antara; Nanda, Bijoy Kumar

doi:10.1177/1077546317745256

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…In this research, similar to some works (Abuhamdia and Taheri, 2015; Bhattacharjee, 2017; Hazaveh et al, 2014; Simonovski and Bolte, 2003; Uhl and Klepka, 2005), the wavelet identification procedure is used to identify and evaluate the predominant frequencies of the structure during the environmental excitations, but in a different way, that is, in an intermittent (cutoff) manner. Then, the updated frequencies are used for poles adaptation and improvement of the pole assignment.…”

Section: Introductionmentioning

confidence: 96%

Seismic control of damaged structure by pole assignment and intermittent wavelet-based identification

Zadeh

Afshar

2021

Journal of Vibration and Control

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Calculation of the control forces by control algorithms, such as the pole assignment, proportional-integral-derivative, and linear quadratic regulator, is usually based on initial dynamic characteristics of the intact and undamaged structure, which is considered to be in the ideal conditions. However, because of the effect of natural loads and damage due to aging, these features can change during the structure’s life span, eventually leading to incorrect control forces. In this research, to overcome this problem and to get closer to the actual dynamic characteristics and on the other hand, in order to elude the adverse effects of real-time identification, such as elapsed time of detection, induced to the controller, the intermitted wavelet-based identification technique besides the pole assignment control is introduced. Performance of the proposed controller on three- and five-story with different cases of stiffness and two failure scenarios, under far and near-field earthquakes, are examined and compared by non-updated wavelet-based pole assignment, proportional-integral-derivative and linear quadratic regulator controllers. Results show that damaged structure response controlled by the suggested adapted pole assignment method is significantly reduced compared to ones controlled by other control methods.

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Section: Introductionmentioning

confidence: 96%

Seismic control of damaged structure by pole assignment and intermittent wavelet-based identification

Zadeh

Afshar

2021

Journal of Vibration and Control

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“…Kankar et al performed fault diagnosis for rolling element bearings using CWT and two classification techniques: an artificial neural network and a support vector machine [30]. Bhattacharjee et al conducted a study aiming at the estimation of the damping parameters of composite beams formed with glass-fiber-reinforced epoxy, glass-fiber-reinforced polyester, and carbon black filled epoxy [31]. CWT was used to investigate the system response analytically.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

2021

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This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate twodimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections. INDEX TERMSConcrete crack characterization, continuous wavelet transformation, convolutional neural network, SqueezeNet. MD ARAFAT HABIB was born in Rangpur, Bangladesh. He received his B.Sc. degree from BRAC University, Bangladesh. After that he joined Chosun University in South Korea to pursue his master's degree in computer engineering. His program was funded in part by Chosun University and National Research Foundation of Korea. During his master's program he has received Chosun University excellent student award based on academic performance and Brain Korea Scholarship. He is currently working at the University of Ulsan, South Korea. His current interests include artificial intelligence, signal processing, fault diagnosis, and condition monitoring of industrial machinery. MD JUNAYED HASAN received a B.Sc. degree in Computer Engineering from

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“…Veeramuthuvel et al (2017) showed that an artificial neural network could be successfully used to identify particle damping parameters. Bhattacharjee and Nanda (2018) proposed the identification technique of damping parameter using the Morlet wavelet transform. Su et al (2019) studied the exponential damping in a reinforced concrete cantilever beam.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization–based characterization technique of nonproportional viscous damping parameter of a cantilever beam

Das

Mondal

Guchhait

2021

Journal of Vibration and Control

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A complex eigenvector is a result of nonproportional damping present in a structural system. However, it is difficult to identify the accurate damping matrix considering the modal sparsity and coordinate sparsity. A nonproportional viscous damping parameter identification is formulated as an unconstrained optimization problem in the present study. The damping coefficient of each element is considered as the design variable for the optimization problem. The objective function is defined using the incomplete complex eigenvectors, which are generated because of the presence of external damping devices in the structure. This objective function is then minimized using standard particle swarm optimization to identify the damping coefficient of the damping matrix. The accuracy and efficiency of the particle swarm optimization are investigated by solving a few numerical problems with simulated measured data. The proposed method works well with the incomplete measured modal data. The current methodology performs satisfactorily with and without noisy data. A comparison study is performed with the existing gradient-based method, and the results show that the proposed method performs better than the existing gradient-based method for the present problem with and without noisy measurement data.

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Damping study of composites using wavelet analysis

Cited by 5 publications

References 21 publications

Seismic control of damaged structure by pole assignment and intermittent wavelet-based identification

Seismic control of damaged structure by pole assignment and intermittent wavelet-based identification

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

Particle swarm optimization–based characterization technique of nonproportional viscous damping parameter of a cantilever beam

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