A structural damage detection algorithm based on discrete wavelet transform and ensemble pattern recognition models

2022 ◽  
Author(s):  
Milad Fallahian ◽  
Ehsan Ahmadi ◽  
Faramarz Khoshnoudian
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Discrete Wavelet Transform ◽  
Structural Damage ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Structural Damage Detection

Structural damage detection and localization using a hybrid method and artificial intelligence techniques

2019 ◽  
Vol 19 (5) ◽  
pp. 1507-1523 ◽  
Author(s):  
Azadeh Noori Hoshyar ◽  
Bijan Samali ◽  
Ranjith Liyanapathirana ◽  
Afsaneh Nouri Houshyar ◽  
Yang Yu
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Hybrid Method ◽  
Structural Damage ◽  
Reinforced Concrete Beams ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Structural Damage Detection ◽  
Detection And Localization

In this article, an intelligent scheme for structural damage detection and localization is introduced by implementing a hybrid method using the Hilbert–Huang transform and the wavelet transform. First, the second derivatives of the Discrete Laplacian are computed on Hilbert spectrum parameters at each frequency coordinate, and then, in order to highlight the influence of damage on signals, the data are rescaled and weighted with respect to the variance to adjust the differences in amplitude at different scales. Afterwards, the anti-symmetric extension is applied to deal with the boundary distortion phenomenon. A two-dimensional map is created using the multi two-dimensional discrete wavelet transform. This generates the coefficient matrices of level 2 approximation and horizontal, vertical and diagonal details. Horizontal detail coefficients are used to localize damages due to its sensitiveness to any perturbation. Finally, the validity of the algorithm corresponding to various damage states, the single state damage and multiple state damage, is examined through experimental analysis. The results indicate that the proposed framework can effectively localize cracks on concrete and reinforced concrete beams and can provide reliable crack localization in the presence of noise up to 5% more than the expected noise. In addition, the detection problem is mapped to machine learning tasks (support vector machine, k-nearest neighbours and ensemble methods) to automate the damage detection process. The quality of the models is evaluated and validated using the features extracted from the horizontal detail coefficients. The numerical results show that the ensemble models outperform the other models with respect to accuracy, prediction speed and training time.

scholarly journals Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Chen ◽  
Zhi-wei Chen ◽  
Gan-jun Wang ◽  
Wei-ping Xie
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Discrete Wavelet Transform ◽  
Sudden Change ◽  
Damage Index ◽  
Time Instant ◽  
Discrete Wavelet ◽  
Building Structures ◽  
Stiffness Reduction ◽  
Shear Building

The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited.

ON-LINE HEALTH MONITORING AND DAMAGE DETECTION OF STRUCTURES BASED ON THE WAVELET TRANSFORM

2008 ◽  
Vol 08 (03) ◽  
pp. 367-387 ◽  
Author(s):  
B. ZHU ◽  
A. Y. T. LEUNG ◽  
C. K. WONG ◽  
W. Z. LU
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Discrete Wavelet Transform ◽  
Health Monitoring ◽  
Continuous Wavelet Transform ◽  
Abrupt Change ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Random Decrement Technique ◽  
On Line

Presented herein is an experiment that aims to investigate the applicability of the wavelet transform to damage detection of a beam–spring structure. By burning out the string that is connected to the cantilever beam, high-frequency oscillations are excited in the beam–spring system, and there results an abrupt change or impulse in the discrete-wavelet-transformed signal. In this way, the discrete wavelet transform can be used to recognize the damage at the moment it occurs. In the second stage of damage detection, the shift of frequencies and damping ratios is identified by the continuous wavelet transform so as to ensure that the abrupt change or impulse in the signal from the discrete wavelet transform is a result of the damage and not the noise. For the random forced vibration, the random decrement technique is used on the original signal to obtain the free decaying responses, and then the continuous wavelet transform is applied to identify the system parameters. Some developed p version elements are used for the parametric studies on the first stage of health monitoring and to find the damage location. The results show that the two-stage method is successful in damage detection. Since the method is simple and computationally efficient, it is a good candidate for on-line health monitoring and damage detection of structures.

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