Structural Damage Detection of the Simple Beam Based on Responses Phase Space

2012 ◽  
Vol 605-607 ◽  
pp. 989-995 ◽  
Author(s):  
Cheng Cheng ◽  
Zhen Hua Nie ◽  
Hong Wei Ma
Keyword(s):  
Phase Space ◽  
Dynamic Response ◽  
Damage Detection ◽  
Structural Damage ◽  
Phase Plane ◽  
Damage Index ◽  
Phase Space Reconstruction ◽  
Dynamic Responses ◽  
Reconstruction Method ◽  
Structural Damage Detection

In this paper, the technology of attractor phase space in chaotic theory is introduced and applied in the structural damage detection. Firstly the phase plane is constructed with the displacement and acceleration responses. Using the changes of phase plane topology of intact and damaged responses, a new damage index is extracted, and the structural damage existence and severity are identified successfully. Since some of the state variables can not be measured, a method of phase space reconstruction is proposed using single dynamic response. The dynamic responses are directly displayed into phase space, realizing transforming the signals from time domain to space domain. Then using the reconstructed phase space, the damage is diagnosed. The results indicate that the phase space reconstruction method has good robustness to noise, and higher sensitivity compared with traditional modal-based methods. The phase space reconstruction method can calculate the value of the damage index using single dynamic response, so that a single sensor can monitor structural damage existence and severity.

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.

Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure

2016 ◽  
Vol 16 (6) ◽  
pp. 711-731 ◽  
Author(s):  
Yun-Lai Zhou ◽  
Nuno M.M. Maia ◽  
Rui P.C. Sampaio ◽  
Magd Abdel Wahab
Keyword(s):  
Damage Detection ◽  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Analysis ◽  
Structural Damage ◽  
Real Life ◽  
Dynamic Responses ◽  
Hierarchical Clustering Analysis ◽  
Structural Damage Detection ◽  
Structural Dynamic

Maintenance and repairing in actual engineering for long-term used structures, such as pipelines and bridges, make structural damage detection indispensable, as an unanticipated damage may give rise to a disaster, leading to huge economic loss. A new approach for detecting structural damage using transmissibility together with hierarchical clustering and similarity analysis is proposed in this study. Transmissibility is derived from the structural dynamic responses characterizing the structural state. First, for damage detection analysis, hierarchical clustering analysis is adopted to discriminate the damaged scenarios from an unsupervised perspective, taking transmissibility as feature for discriminating damaged patterns from undamaged ones. This is unlike directly predicting the structural damage from the indicators manifestation, as sometimes this can be vague due to the small difference between damaged scenarios and the intact baseline. For comparison reasons, cosine similarity measure and distance measure are also adopted to draw out sensitive indicators, and correspondingly, these indicators will manifest in recognizing damaged patterns from the intact baseline. Finally, for verification purposes, simulated results on a 10-floor structure and experimental tests on a free-free beam are undertaken to check the suitability of the raised approach. The results of both studies are indicative of a good performance in detecting damage that might suggest potential application in actual engineering real life.

Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm

2020 ◽  
pp. 147592172092125
Author(s):  
Xiaoyou Wang ◽  
Rongrong Hou ◽  
Yong Xia ◽  
Xiaoqing Zhou
Keyword(s):  
Bayesian Inference ◽  
Damage Detection ◽  
Probability Density ◽  
Structural Damage ◽  
Posterior Probability ◽  
Damage Index ◽  
Structural Damage Detection ◽  
Variational Bayesian ◽  
Variational Bayesian Inference ◽  
Posterior Probability Density

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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