A NUMERICAL STUDY OF STRUCTURAL DAMAGE DETECTION USING CHANGES IN THE ROTATION OF MODE SHAPES

2002 ◽  
Vol 251 (2) ◽  
pp. 227-239 ◽  
Author(s):  
M.A.-B. ABDO ◽  
M. HORI
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Numerical Study ◽  
Mode Shapes ◽  
Structural Damage Detection

scholarly journals Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

2015 ◽  
Author(s):  
W. Xu ◽  
W. D. Zhu ◽  
S. A. Smith
Keyword(s):  
Steady State ◽  
Damage Detection ◽  
Structural Damage ◽  
Longitudinal Vibration ◽  
Harmonic Excitation ◽  
Mode Shapes ◽  
Noise Robustness ◽  
Steady State Response ◽  
Structural Damage Detection ◽  
State Response

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, a slope vibration shape is transformed to a multiscale slope vibration shape in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numerical cases of damaged bars, and results show that multiscale slope vibration shapes can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.

scholarly journals Structural damage detection based on improved frequency change ratio

2019 ◽  
Vol 272 ◽  
pp. 01010
Author(s):  
Jian WANG ◽  
Huan JIN ◽  
Xiao MA ◽  
Bin ZHAO ◽  
Zhi YANG ◽  
...  
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection Method ◽  
Mode Shapes ◽  
Frequency Change ◽  
Damage Localization ◽  
Structural Damage Detection ◽  
Practical Application ◽  
Slight Damage

Frequency Change Ratio (FCR) based damage detection methodology for structural health monitoring (SHM) is analyzed in detail. The effectiveness of damage localization using FCR for some slight damage cases and worse ones are studied on an asymmetric planar truss numerically. Disadvantages of damage detection using FCR in practical application are found and the reasons for the cases are discussed. To conquer the disadvantages of FCR, an Improved Frequency Change Ratio (IFCR) based damage detection method which takes the changes of mode shapes into account is proposed. Verification is done in some damage cases and the results reveal that IFCR can identify the damage more efficiently. Noisy cases are considered to assess the robustness of IFCR and results indicate that the proposed method can work well when the noise is not severe.

Structural Damage Identification Based on Information Fusion Techniques

2010 ◽  
Author(s):  
Hui Li ◽  
Yuequan Bao
Keyword(s):  
Damage Detection ◽  
Information Fusion ◽  
Structural Damage ◽  
Damage Identification ◽  
Yellow River ◽  
Numerical Study ◽  
Detection Methods ◽  
Data Sets ◽  
Structural Damage Detection ◽  
Selective Fusion

With the aim to decrease the uncertainties of structural damage detection, two fusion models are presented in this paper. The first one is a weighted and selective fusion method for combing the multi-damage detection methods based on the integration of artificial neural network, Shannon entropy and Dempster-Shafer (D-S) theory. The second one is a D-S based approach for combing the damage detection results from multi-sensors data sets. Numerical study on the Binzhou Yellow River Highway Bridge and an experimental of a 20-bay rigid truss structure were carried out to validate the uncertainties decreasing ability of the proposed methods for structural damage detection. The results show that both of the methods proposed are useful to decrease the uncertainties of damage detection results.

Detection of Small Damage of Beam Structures Based on the Slope of Proper Orthogonal Mode

2010 ◽  
Vol 97-101 ◽  
pp. 4457-4460
Author(s):  
Dan Sheng Wang ◽  
Ying Bo Zhang ◽  
Hai Ping Yang ◽  
Hong Ping Zhu
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection Method ◽  
Numerical Study ◽  
Structural Damage Detection ◽  
Beam Structures ◽  
Study Results ◽  
The Difference ◽  
Proper Orthogonal

In recent two decades, the issues on structural damage detection and health monitoring have been paid considerable attention in mechanical and civil engineering communities. A lot of researchers have developed many methods to try to resolve the problems. To this day, detection of the small damage of structures, however, has still been a difficulty. The correlation theories of proper orthogonal decomposition (POD) and the basic principle of a new structural damage detection method based on the slope of POD are introduced in this paper. Numerical study on beam structures for small damage detection based on the proposed method is implemented. From the study results one can find that the method based on the slope of the difference of proper orthogonal modes (POMs) has the abilities to localize the small damage of beam structures.

scholarly journals Structural Damage Detection Using Slopes of Longitudinal Vibration Shapes

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
W. Xu ◽  
W. D. Zhu ◽  
S. A. Smith ◽  
M. S. Cao
Keyword(s):  
Steady State ◽  
Damage Detection ◽  
Structural Damage ◽  
Longitudinal Vibration ◽  
Harmonic Excitation ◽  
Mode Shapes ◽  
Noise Robustness ◽  
Steady State Response ◽  
Structural Damage Detection ◽  
State Response

While structural damage detection based on flexural vibration shapes, such as mode shapes and steady-state response shapes under harmonic excitation, has been well developed, little attention is paid to that based on longitudinal vibration shapes that also contain damage information. This study originally formulates a slope vibration shape (SVS) for damage detection in bars using longitudinal vibration shapes. To enhance noise robustness of the method, an SVS is transformed to a multiscale slope vibration shape (MSVS) in a multiscale domain using wavelet transform, which has explicit physical implication, high damage sensitivity, and noise robustness. These advantages are demonstrated in numerical cases of damaged bars, and results show that MSVSs can be used for identifying and locating damage in a noisy environment. A three-dimensional (3D) scanning laser vibrometer (SLV) is used to measure the longitudinal steady-state response shape of an aluminum bar with damage due to reduced cross-sectional dimensions under harmonic excitation, and results show that the method can successfully identify and locate the damage. Slopes of longitudinal vibration shapes are shown to be suitable for damage detection in bars and have potential for applications in noisy environments.

Structural Damage Detection Based on Curvature Mode Shapes and Neural Network Technique

2012 ◽  
Vol 204-208 ◽  
pp. 2907-2912
Author(s):  
Guang Qian Du ◽  
Chang Zhi Zhu ◽  
Li Juan Long ◽  
Meng Zhang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Natural Frequency ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Mode Shapes ◽  
Structural Damage Detection ◽  
Gray Theory ◽  
Input Structure ◽  
Frequency Changes

On the basis of the theory that natural frequency changes and curvature mode shapes can be employed to determine the locations and degrees of damage of structures, a BP neural network technique with an improved input structure was developed. The two networks were used for diagnosis of structural damage, and structural damages were predicted using gray theory. The results showed that the gray theory to predict the structural damage neural network was applicable to irregular objects such injury problem diagnosis.

Structural Damage Detection by Integrating Non-Negative Matrix Factorization and Relevance Vector Machines

2012 ◽  
Vol 249-250 ◽  
pp. 137-146
Author(s):  
Shu Mei Zhou ◽  
Yue Quan Bao
Keyword(s):  
Damage Detection ◽  
Matrix Factorization ◽  
Structural Damage ◽  
Numerical Study ◽  
Wavelet Packet ◽  
Pattern Identification ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Damage Pattern ◽  
Non Negative Matrix Factorization

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.

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