Structural damage localisation for a frame structure from changes in curvature of approximate entropy feature vectors

2013 ◽  
Vol 29 (1) ◽  
pp. 80-97 ◽  
Author(s):  
Y.H. An ◽  
J.P. Ou
2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.


2012 ◽  
Vol 568 ◽  
pp. 85-88
Author(s):  
Ming Gao

In 5·12 Wenchuan earthquake, most of the buildings were damaged at different degrees in Mianyang. To analysis seismic damage of RC frame structure building, and investigate its reinforcement situation,the results show that: For destruction of frame column or bottom frame structure column, enlarge section method is used mostly for reinforcement in civil engineering;To serious damage of affiliated structure such as filler wall and Parapet, most of them will be demolished and built again, and add constructional column; To the situation of concrete bottom plate with crack, paste carbon fiber sheet or bottom plant steel was used depending on the structural damage degree, and jet concrete for strengthening.


Author(s):  
K. He ◽  
W. D. Zhu

Two major challenges associated with a vibration-based damage detection method using changes in natural frequencies are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistic function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using the Levenberg-Marquardt method is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. It is applied to various engineering structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation, and the locations and extent of damage can be successfully detected in experimental damage detection.


2014 ◽  
Vol 578-579 ◽  
pp. 1020-1023
Author(s):  
Jing Zhou Lu ◽  
Jia Chen Wang ◽  
Xu Zhu

In this paper, we introduce a set of techniques for time series analysis based on principal component analysis (PCA). Firstly, the autoregressive (AR) model is established using acceleration response data, and the root mean squared error (RMSE) of AR model is calculated based on PCA. Then a new damage sensitive feature (DSF) based on the AR coefficients is presented. To test the efficacy of the damage detection and localization methodologies, the algorithm has been tested on the analytical and experimental results of a three-story frame structure model of the Los Alamos National Laboratory. The result of the damage detection indicates that the algorithm is able to identify and localize minor to severe damage as defined for the structure. It shows that the suggested method can lead to less amount of computing time, high suitability and identification accuracy.


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.


2010 ◽  
Vol 163-167 ◽  
pp. 2693-2698 ◽  
Author(s):  
Peng Sun ◽  
Ai Qun Li ◽  
You Liang Ding ◽  
Yang Deng

The damage alarming analysis based on wavelet packet energy spectrum is performed with regard to the experimental data of Benchmark steel frame structure and online monitoring data of Runyang Suspension Bridge, on the basis of which the damage alarming effects using various wavelet functions are investigated in detail. Results reveal that the Daubechies wavelet functions and Coiflets wavelet functions are applicable to structural damage alarming.


2014 ◽  
Vol 578-579 ◽  
pp. 1125-1128
Author(s):  
Jin Sheng Fan ◽  
Ying Yuan ◽  
Xiu Ling Cao

Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The trained neural network can identify the damage location and degree of the frame structure. It proved that this method is simple and valid.


2017 ◽  
Vol 28 (14) ◽  
pp. 1941-1956 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Bijan Samali ◽  
Jianchun Li ◽  
Ye Lu ◽  
Samir Mustapha

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.


2013 ◽  
Vol 351-352 ◽  
pp. 1244-1248
Author(s):  
Hong Yu Jia ◽  
Peng Fei Yue ◽  
Xiao Fei Wang

Space frame structure of no damage and injury finite element models were established with ANSYS, and analyze 3D curvature mode as well three-dimensional vibration mode variety rate of the space rigid frame based on modal analysis. Curvature mode and three-dimensional vibration mode variety rate as the labeled amount was selected and applied to structural damage. The calculated results showed that the first-order curvature mode not only identify against single or multiple damage location, but also determine the initial degree of injury, and the axial curvature mode is better than the horizontal curvature mode for damage identification; The calculated results also showed that the variety rate of the first-order vibration mode can identify against damage location. Methods were provided by identifying the space frame structural damage of the curvature mode or three-dimensional vibration mode variety rate.


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