Study on an auto-correlation-function-based damage index: Sensitivity analysis and structural damage detection

2015 ◽  
Vol 359 ◽  
pp. 195-214 ◽  
Author(s):  
Muyu Zhang ◽  
Rüdiger Schmidt
2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
W. R. Li ◽  
Y. F. Du ◽  
S. Y. Tang ◽  
L. J. Zhao

On the basis of the thought that the minimum system realization plays the role as a coagulator of structural information and contains abundant information on the structure, this paper proposes a new method, which combines minimum system realization and sensitivity analysis, for structural damage detection. The structural damage detection procedure consists of three steps: (1) identifying the minimum system realization matrixes A, B, and R using the structural response data; (2) defining the mode vector, which is based on minimum system realization matrix, by introducing the concept of the measurement; (3) identifying the location and severity of the damage step by step by continuously rotating the mode vector. The proposed method was verified through a five-floor frame model. As demonstrated by numerical simulation, the proposed method based on the combination of the minimum realization system and sensitivity analysis is effective for the damage detection of frame structure. This method not only can detect the damage and quantify the damage severity, but also is not sensitive to the noise.


2013 ◽  
Vol 639-640 ◽  
pp. 1033-1037
Author(s):  
Yong Mei Li ◽  
Bing Zhou ◽  
Guo Fu Sun ◽  
Bo Yan Yang

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.


2018 ◽  
Vol 39 (3) ◽  
pp. 631-649
Author(s):  
Miao Li ◽  
Wei-Xin Ren ◽  
Tian-Li Huang ◽  
Ning-Bo Wang

This article focuses on the experimental investigations on the cross-correlation function amplitude vector of the dynamic strain (CorV_S) under varying environmental temperature for structural damage detection. It is verified that under white noise excitation, CorV_S is only related to the natural frequencies, mode shapes, and damping ratios of structures. The normalized CorV_S of the undamaged structure maintains a uniform shape. A laboratory experimental investigation based on an end-fixed steel beam shows that CorV_S can be used for structural damage detection. However, CorV_S constructed by the dynamic strain of in-situ test varies with time, and the CorV_S curves do not have the same shape. When the environmental temperature fluctuates significantly, high correlation exists between the dynamic strain and environmental temperature. By analyzing the power spectral density of the signals measured from active and inactive strain gauges, it is found that the signals induced by temperature stress, which do not reflect the dynamic performance of the bridge, exist in the very low-frequency band. To avoid the interference to CorV_S, the temperature effect component is separated from the dynamic strain by analytical mode decomposition method. Then, each CorV_S curve maintains a uniform shape. The results demonstrate that it is prone to get a misjudgment for the condition of a structure if temperature effect on CorV_S is ignored. It is necessary to eliminate the environmental temperature effect on CorV_S for the damage detection of a structure in service.


2020 ◽  
pp. 147592172092125
Author(s):  
Xiaoyou Wang ◽  
Rongrong Hou ◽  
Yong Xia ◽  
Xiaoqing Zhou

Existing studies on sparse Bayesian learning for structural damage detection usually assume that the posterior probability density functions follow standard distributions which facilitate to circumvent the intractable integration problem of the evidence by means of numerical sampling or analytical derivation. Moreover, the uncertainties of each mode are usually quantified as a common parameter to simplify the calculation. These assumptions may not be realistic in practice. This study proposes a sparse Bayesian method for structural damage detection suitable for standard and nonstandard probability distributions. The uncertainty corresponding to each mode is assumed as different. Variational Bayesian inference is developed and the posterior probability density functions of each unknown are individually derived. The parameters are found to follow the gamma distribution, whereas the distribution of the damage index cannot be directly obtained because of the nonlinear relationship in its posterior probability density function. The delayed rejection adaptive Metropolis algorithm is then adopted to generate numerical samples of the damage index. The coupled damage index and parameters in the variational Bayesian inference are successively calculated via an iterative process. A laboratory-tested frame is utilised to verify the effectiveness of the proposed method. The results indicate that the sparse damage can be accurately detected. The proposed method has the advantage of high accuracy and broad applicability.


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