A combination of wavelet packet energy curvature difference and Richardson extrapolation for structural damage detection

2020 ◽  
Vol 101 ◽  
pp. 102224
Author(s):  
V. Karami ◽  
M.R. Chenaghlou ◽  
A.R. Mostafa Gharabaghi
2005 ◽  
Vol 27 (9) ◽  
pp. 1339-1348 ◽  
Author(s):  
S.S. Law ◽  
X.Y. Li ◽  
X.Q. Zhu ◽  
S.L. Chan

2012 ◽  
Vol 594-597 ◽  
pp. 1105-1108 ◽  
Author(s):  
Dong Hai Xie ◽  
Hong Wei Tang

In recent years,most existing engineering structures which have approached their normal life span, such as concrete plate, concrete beam etc., Almost all of these architecture structures are subjected to damage due to external loads, initial design defect etc. Structural damage detection and assessment has been becoming a focus of increasing interest in civil engineering field. However,At present, the study on structural damage detection is still at initial stage and the adopted main approaches are theoretical analysis and numerical simulation, but physical models are scarce. This leads to the yielded theories and methods are not sufficiently applicable for practical engineering application. Aiming at this, this paper focuses on developing effective methods of using wavelet and neural networks to detect the damage of elastic thin plate due to their extensive applications in civil engineering.


2012 ◽  
Vol 249-250 ◽  
pp. 137-146
Author(s):  
Shu Mei Zhou ◽  
Yue Quan Bao

This paper proposes a structural damage detection method based on wavelet packet decomposition, non-negative matrix factorization (NMF) and a relevance vector machine (RVM). First, vibration data at multiple points are used to calculate the wavelet packet node energies and construct a non-negative damage feature matrix. Second, to increase the damage detection accuracy, the NMF technique is employed to obtain the reduced dimensional representation of the non-negative damage feature matrix and extract the underlying features. Last, the RVM, a powerful tool for classification and regression, that can obtain the probability estimation for classification, is used to determine the relationship between features extracted with NMF and the corresponding damage patterns by considering the measurement noise. The trained RVMs are then used to perform damage pattern identification and classification of an unknown state structure. Numerical study on the Binzhou Yellow River Highway Bridge is carried out to validate the ability of the proposed method in damage detection. The results show that the RVM can achieve a high accuracy in damage pattern identification accuracy using the features extracted by NMF.


2012 ◽  
Vol 490-495 ◽  
pp. 2588-2593
Author(s):  
Peng Yan ◽  
Qiao Li

For the purpose of effective damage localization, combined with natural excitation technique (NExT) and wavelet packet (WP) decomposition method, a new algorithm named NExT based wavelet packet energy (WPE) damage detection algorithm was proposed for continuous beam. Finite measuring points of bridge structure response are set firstly, and then classified as reference points and response points. Calculated with each corresponding two-point, the virtual impulse response signals are taken as input data of WP decomposition method. Finally, structural damage detection is carried out by using WPE as damage index. Through a three-span continuous beam finite element model, this algorithm was discussed with respect to the applicability and effectiveness of damage detection. The analysis results reveal that, with certain robustness to noise, the proposed algorithm has favorable effect on damage interval localization. Therefore, the algorithm put forward is practical to detect damage of continuous beam.


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