Enhanced optimization-based structural damage detection method using modal strain energy and modal frequencies

2017 ◽  
Vol 34 (3) ◽  
pp. 637-647 ◽  
Author(s):  
M. R. Ghasemi ◽  
M. Nobahari ◽  
N. Shabakhty
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Strain Energy ◽  
Detection Method ◽  
Structural Damage Detection ◽  
Modal Strain Energy

Structural Damage Detection Based on Ridge Estimation Method

2014 ◽  
Vol 578-579 ◽  
pp. 1028-1031
Author(s):  
Hui Yong Guo ◽  
Mao Sheng ◽  
Zheng Liang Li
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Strain Energy ◽  
Good Accuracy ◽  
Detection Method ◽  
Estimation Method ◽  
Structural Damage Detection ◽  
Modal Strain Energy ◽  
Ridge Estimation ◽  
Damage Equation

In order to identify structural damage locations and extent, a method based on ridge estimation and modal strain energy is presented in this paper. First, structural modal strain energy is given and a modal strain energy sensitivity damage equation is obtained. Then, considering the TikhonovTT regularization theoryTT, a ridge estimation method is proposed to solve the damage equation and ridge parameter of the method is optimized. Simulation results demonstrate that the proposed damage detection method based on ridge estimation and modal strain energy can identify structural damage locations and extent with good accuracy.

Study on Damage Location of Spatial Structures Based on Wavelet Analysis of Model Strain Energy

2013 ◽  
Vol 639-640 ◽  
pp. 1033-1037
Author(s):  
Yong Mei Li ◽  
Bing Zhou ◽  
Guo Fu Sun ◽  
Bo Yan Yang
Keyword(s):  
Wavelet Analysis ◽  
Damage Detection ◽  
Structural Damage ◽  
Strain Energy ◽  
Damage Identification ◽  
Damage Index ◽  
Structural Damage Detection ◽  
Modal Strain Energy ◽  
Wavelet Method ◽  
Long Span

The research to identify and locate the damage to the engineering structure mainly aimed at some simple structure forms before, such as beam and framework. Damage shows changes of local characteristics of the signal, while wavelet analysis can reflect local damage traits of the signal in time domain and frequency domain. For confirming the validity and applicability of structural damage identification methods, wavelet analysis is used to spatial structural damage detection. The wavelet analysis technique provides new ideas and methods of spatial steel structural damage detection. Based on the theory of wavelet singularity detection，with the injury signal of modal strain energy as structural damage index，the mixing of the modal strain energy and wavelet method to identify and locate the damage to the spatial structure is considered. The multiplicity of the bars and nodes can be taken into account, and take the destructive and nondestructive modal strain energy of Kiewitt-type reticulated shell with 40m span as an example of numerical simulation，the original damage signal and the damage signal after wavelet transformation is compared. The location of the declining stiffness identified by the maximum of wavelet coefficients，analyzed as signal by db1 wavelet，and calculate the graph relation between coefficients of the wavelets and the damage to the structure by discrete or continuous wavelet transform, and also check the accuracy degree of this method with every damage case. Finally，the conclusion is drawn that the modal strain energy and wavelet method to identify and locate the damage to the long span reticulated shell is practical, effective and accurate, that the present method as a reliable and practical way can be adopted to detect the single and several locations of damage in structures.

A Damage Detection Method: CMSE Combined with Niche GA

2007 ◽  
Vol 334-335 ◽  
pp. 929-932 ◽  
Author(s):  
Xu Ge ◽  
Yun Ju Yan ◽  
Huan Guo Chen
Keyword(s):  
Composite Material ◽  
Damage Detection ◽  
Composite Structures ◽  
Structural Damage ◽  
Strain Energy ◽  
Detection Method ◽  
Detection Methods ◽  
Damage Localization ◽  
Complex Structures ◽  
Modal Strain Energy

The paper presents an effective damage detection method of complex composite structures. It can be carried out through the experimental modal analysis of the damaged structure. The method using the improved Cross Modal Strain Energy (CMSE) technique and Niche GA has many advantages compared with other damage detection methods. The CMSE method can use any modes of the structure and the modes don’t need to be normalized or consistent in scale. The Niche GA improves the efficiency of the calculation and enhances the capacity of identifying structural damage localization. The model is the composite material airfoil case. The numerical results show that the method proposed in this paper is successful for damage detection of complex structures.

scholarly journals Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks

2019 ◽  
Vol 9 (16) ◽  
pp. 3376 ◽  
Author(s):  
Shuai Teng ◽  
Gongfa Chen ◽  
Gen Liu ◽  
Jianbin Lv ◽  
Fangsen Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Structural Damage ◽  
Strain Energy ◽  
Frame Structure ◽  
Modal Parameters ◽  
Structural Damage Detection ◽  
Simulation Data ◽  
Modal Strain Energy

In this paper, a convolutional neural network (CNN) was used to extract the damage features of a steel frame structure. As structural damage could induce changes of the modal parameters of the structure, the convolution operation was used to extract the features of modal parameters, and a classification algorithm was used to judge the damage state of the structure. The finite element method was applied to analyze the free vibration of the steel frame and obtain the first-order modal strain energy for various damage scenarios, which was used as the CNN training sample. Then vibration experiments were carried out, and modal parameters were obtained from the modal analysis of the vibration signals. The experimental data were inputted into the CNN to verify its damage detection capability. The result showed that the CNN was effective in detecting the intact structure, single damage, and multi damages with an accuracy of 100%. For comparison, the same samples were also applied to the traditional back propagation (BP) neural network, which failed to detect the intact structure and multiple-damage cases. It was found that: (1) The proposed CNN could be trained from finite element simulation data and used in real frame structure damage detection, and it performed better in structural damage detection than BP neural networks; (2) the measured data of a real structure could be supplemented by numerical simulation data, and satisfactory results have been demonstrated.

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