Seismic damage detection of tall airport traffic control towers using wavelet analysis

Vafaei, Mohammadreza; Adnan, Azlan

doi:10.1080/15732479.2012.704051

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…Kaloop and Hu (2016) proved that the energy of DWT has higher performance in damage detection than CWT due to lower correlation coefficient values. Also, sensitivity to stiffness change by DWT is more than CWT (Vafaei and Adnan, 2014).…”

Section: Introductionmentioning

confidence: 98%

A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform

Nejad

Aval

Maldar

et al. 2020

Advances in Structural Engineering

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With the help of Structural Health Monitoring (SHM) methods, it is possible to identify the occurrence of damage at its early stages and prevent fatality and financial damages. Great advances in signal processing methods in combination with Machine learning tools have led to better achieve this goal. In the present paper, the two major techniques, that is, Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are combined with Artificial Neural Network (ANN) through processing raw acceleration responses measured on a scaled jacket type offshore platform which was constructed and tested as a benchmark structure at K.N. Toosi University of Technology. In this way, ANN was trained by the signals obtained from EMD and DWT for three different conditions of the jacket platform to determine the relative damage severity. The envelope of the obtained signal’s energy (ENV) as an appropriate damage index was used to determine the damage location. The results of the application of this procedure on the case study indicated that DWT, compared to EMD, is a more reliable signal processing method in damage detection due to better noise reduction.

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Section: Introductionmentioning

confidence: 98%

A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform

Nejad

Aval

Maldar

et al. 2020

Advances in Structural Engineering

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“…They concluded that the method could identify the time of occurrence and roughly the spatial distribution and degree of the major damage in the imperial county services (ICS) building. Vafaei and Adnan [32] investigated the applicability of the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) to seismic damage detection of tall airport traffic control towers, numerically proving that CWT could successfully detect seismic damage even with noise-polluted signals. Aguirre et al [33] explored the feasibility of using output-only model-free wavelet-based techniques for damage detection in reinforced concrete structures subjected to seismic excitations.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Packet Singular Entropy-Based Method for Damage Identification in Curved Continuous Girder Bridges under Seismic Excitations

Cao

Deng

et al. 2019

Sensors

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Curved continuous girder bridges (CCGBs) have been widely adopted in the civil engineering field in recent decades for complex interchanges and city viaducts. Unfortunately, compared to straight bridges, this type of bridge with horizontal curvature is relatively vulnerable to earthquakes characterized by large energy and short duration. Seismic damage can degrade the performance of CCGBs, threatening their normal operation and even resulting in collapse. Detection of seismic damage in CCGBs is thus significantly important but is still not well resolved. To this end, a new method based on wavelet packet singular entropy (WPSE) is proposed to identify seismic damage by analyzing the dynamic responses of CCGBs to seismic excitation. This WPSE-based approach features characterizing damage using synergistic advantage of the wavelet packet transform, singular value decomposition, and information entropy. To testify the algorithm, a finite element model of a typical CCGB with two types of seismic damage is built, in which the seismic damage is individually modeled by stiffness reductions at the bottom of piers and at pier-girder connections. The displacement responses of the model to El Centro seismic excitation is used to identify the damage. The results show that damage indices in the WPSE-based approach can correctly locate the seismic damage in CCGBs. Furthermore, the WPSE-based method is competent to identify damage with higher accuracy in comparison with the wavelet packet energy based method, and has a strong immunity to noise revealed by robustness analysis. An array of responses used in this approach paves the way of developing practical technologies for detecting seismic damage using advanced distributed sensing techniques, typically the optical sensors.

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“…Thus, they can provide more detailed information than conventional Fourier analysis because Fourier transform projects the signal in the frequency domain and has no link with the time domain. In addition, the Fourier transform is limited to stationary signals [11]. However, wavelet analysis has the power to analyze the non-stationary structural dynamic responses.…”

Section: Introductionmentioning

confidence: 99%

Wavelet entropy based structural damage identification under seismic excitation

Diao

Zhang

Sun

et al. 2018

Smart Mater. Struct.

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In this work, an entropy based two-step structural damage identification method under seismic excitation is proposed. The measured signals are decomposed by means of wavelet packet transform, and the wavelet entropies are obtained on the basis of the information entropy theory. In the first step, the damage alarming indices, calculated with the wavelet entropies in undamaged and damaged conditions, are used as samples for Shewhart individuals control chart to alarm the structural damage. In the second step, the damage localization indices, constructed by calculating the differences of curvature of wavelet entropies in undamaged and damaged conditions, are fed to the back-propagation neural network to identify structure damage location. The numerical simulation and shaking table model test of an offshore platform under seismic excitation are implemented to verify the feasibility of the proposed two-step structural damage identification method. The results show that the proposed method needs only the non-stationary output data and has low sensitivity to signal noise.

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Seismic damage detection of tall airport traffic control towers using wavelet analysis

Cited by 16 publications

References 29 publications

A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform

A damage detection procedure using two major signal processing techniques with the artificial neural network on a scaled jacket offshore platform

Wavelet Packet Singular Entropy-Based Method for Damage Identification in Curved Continuous Girder Bridges under Seismic Excitations

Wavelet entropy based structural damage identification under seismic excitation

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