Research on Structural Damage Localization by Improved Curvature Method

2011 ◽  
Vol 255-260 ◽  
pp. 439-443
Author(s):  
Jun Chang ◽  
Yu Meng Wu
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Health Condition ◽  
Steel Beam ◽  
Damage Localization ◽  
Local Damage ◽  
Improved Method ◽  
Damage Location ◽  
Simple Support ◽  
Ideal Method

Damage identification is one of the main contents of structural health condition assessment. Curvature mode is an ideal method to identify structural damage location, with advantages of easy to be operated and sensitive to local damage, while the frequency is easy to test with high precision. An improved structural damage identification method is presented, which combines curvature and frequency. Finally, the improved method presented herein is verified by a simple support steel beam tested in laboratory. The results show that the improved method can effectively identify structural damage location.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

Application of BP Neural Network in Structural Damage Diagnosis of Bridge behind Abutment

2016 ◽  
Vol 847 ◽  
pp. 440-444 ◽  
Author(s):  
Yu Hui Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Current Knowledge ◽  
Horizontal Displacement ◽  
Structural Deformation ◽  
Bp Network ◽  
Damage Location

BP neural network is introduced and applied to identify and diagnose both location and extent of bridge structural damage; static load tests and dynamic calculations are also made on bridge structural damage behind abutment. The key step of this method is to design a reasonably perfect BP network model. According to the current knowledge, three BP neural networks are designed with horizontal displacement rate and inherent frequency rate as damage identification indexes. The neural networks are used to identify the measurement of structure behind abutment and the calculation of damage location and extent, at the same time, they can also be used to compare and analyze the results. The test results show that: taking the two factors (static structural deformation rate and the change rate of natural frequency in dynamic response) as input vector, the BP neural network can accurately identify the damage location and extent, implying a promising perspective for future applications.

Structural Damage Localization Based on AR Model and BP Neural Network

2011 ◽  
Vol 94-96 ◽  
pp. 1211-1215
Author(s):  
Yan Song Diao ◽  
Fei Yu ◽  
Dong Mei Meng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Ar Model ◽  
Model Parameters ◽  
Damage Localization ◽  
Modal Parameter ◽  
Damage Location ◽  
Simulation And Experiment ◽  
Damage Characteristic

When the AR model is used to identify the structural damage, one problem is often met, that is the method can only make a decision whether the structure is damaged, however, the damage location can not be identified exactly. A structural damage localization method based on AR model in combination with BP neural network is proposed in this paper. The AR time series models are used to describe the acceleration responses. The changes of the first 3-order AR model parameters are extracted and composed as damage characteristic vectors which are put into BP neural network to identify the damage location. The effectiveness of the method is validated by the results of numerical simulation and experiment for a four-layer offshore platform. Only the acceleration responses can be used adequately to localize the structural damage, without the usage of modal parameter and excitation force. Thus the dependence on the modal parameter and excitation can be avoided in this method.

The Feasibility Study of Vibration Transmissibility Applied to Structure Damage Identification

2013 ◽  
Vol 347-350 ◽  
pp. 107-110
Author(s):  
Sen Wu ◽  
Bin Wang ◽  
Hai Hua Zhang
Keyword(s):  
Numerical Simulation ◽  
Cantilever Beam ◽  
Feasibility Study ◽  
Structural Damage ◽  
Simulation Experiment ◽  
Damage Identification ◽  
Structure Damage ◽  
Damage Location ◽  
Identification Method ◽  
Vibration Transmissibility

In view of the defects of the traditional damage identification method based on vibration,the damage identification method based on vibration transmissibility is put forward. The feasibility of the vibration transmissibility applied to structural damage identification is analyzed by the numerical simulation experiment of a cantilever beam, the analysis results show that, vibration transmissibility contains the structure damage severity, damage location and other useful information, and all the information is favor of the damage identification.

Space Rigid Frame Damage Identification Method Based on Modal Analysis

2013 ◽  
Vol 351-352 ◽  
pp. 1244-1248
Author(s):  
Hong Yu Jia ◽  
Peng Fei Yue ◽  
Xiao Fei Wang
Keyword(s):  
Modal Analysis ◽  
Structural Damage ◽  
Vibration Mode ◽  
Damage Identification ◽  
Three Dimensional ◽  
Frame Structure ◽  
Space Frame ◽  
First Order ◽  
Damage Location ◽  
Rigid Frame

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.

Damage Identification Research of Cantilever Truss Based on the Principle of Strain Mode

2012 ◽  
Vol 226-228 ◽  
pp. 1432-1435
Author(s):  
Jun Hai Zhang ◽  
Nai Juan Du ◽  
Yue Guo Shen
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Damage Location ◽  
Strain Change ◽  
Before And After ◽  
Force Element ◽  
Damage Extent ◽  
Simulation Results ◽  
Modal Change ◽  
Identification Research

This paper presents a method converting the modal distance of the node into elemental strain based on the special characteristic of two-force element .The strain change before and after damage is applied to the damage identification. The change rule of the relative strain for the same location of the truss occur the various damage extent and the various location of the truss occur the same damage extent, respectively, is obtained according to the strain modal simulation using APDL language. The simulation results show that the strain modal change ratio is sensitive to the cantilever truss damage detection. The damage location and damage extent will be identified. It is an effective nodestructive test way to identify the cantilever truss structural damage.

Vibration-Based Structural Damage Identification Under Interval Uncertainty

2016 ◽  
Author(s):  
David Yoo ◽  
Jiong Tang
Keyword(s):  
Structural Damage ◽  
Prior Information ◽  
Random Distribution ◽  
Damage Identification ◽  
Interval Uncertainty ◽  
Damage Location ◽  
Physical Access ◽  
Non Destructive ◽  
Destructive Methods ◽  
Fuzzy Interval

Identifying damages in mechanical structures in advance is essential part of preventing catastrophic losses. Among several non-destructive methods, the vibration-based method, which utilizes global characteristics of the structures, has several advantages such as not requiring prior information on possible damage location and physical access to it. In the meantime, the mechanical structures are inevitably subject to uncertainties, whose distribution is often unknown in practical situations due to such as limited amount of available data. Uncertainties are treated as interval uncertainty in such cases. In this regard, this study presents vibration-based damage identification under interval uncertainty. To obtain reliable result, this research does not assume any random distribution, e.g., uniform distribution, inside interval. Since detected damage is not assumed to be monotonic function with respect to interval uncertainty either, traditional fuzzy interval arithmetic is not applicable. Instead, we first carry out exhaustive search to see the effect of the interval uncertainty on the identified damage; i.e., discretizing interval uncertainty into sub-intervals and executing damage identification under all possible combinations to see the effect of the interval uncertainty on the identified damage. We then develop the unique algorithm based on M-H algorithm to facility computational efficiency.

scholarly journals Research on Damage Identification of Nonuniform Microcrack in Beam Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jia Guo ◽  
Deqing Guan ◽  
Yanran Pan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Calculation Method ◽  
Structural Damage ◽  
Damage Identification ◽  
Wavelet Coefficient ◽  
Particle Swarm ◽  
Crack Identification ◽  
Swarm Optimization ◽  
Damage Location

Nonuniform microcrack identification is of great significance in mechanical, aerospace, and civil engineering. In this study, the nonuniform crack is simplified as a semielliptical crack, and simplified calculation methods are proposed for damage severity and damage identification of semielliptical cracks. The proposed methods are based on the calculation method for uniform cracks. The wavelet transform and the intelligent algorithm (IA) are used to identify the damage location and the damage severity of the structure, respectively. The singularity of the wavelet coefficient can be used to identify the signal singularity quickly and accurately, and IA efficiently and accurately calculates the structural damage severity. The particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), widely used, are applied to identify the damage severity of the beam. Numerical simulations and experimental analyses of beams with transfixion and semielliptical cracks are carried out to evaluate the accuracy of the semielliptical crack calculation method and the method of wavelet analysis combined with PSO and GA for nonuniform crack identification. The results show that the wavelet-particle swarm optimization (WPSO) and the wavelet-genetic algorithm (WGA) can accurately and efficiently identify the structural semielliptical damage location and severity and that these methods are not easily influenced by noise. The damage severity calculation method for semielliptical cracks can accurately calculate the semielliptical size and can be used to identify damage in beams with semielliptical cracks.

scholarly journals Damage Identification of Ancient Timber Structure Based on Autocorrelation Function

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanfang Hou ◽  
Weibing Hu ◽  
Xin Wang ◽  
Tingting Hou ◽  
Congli Sun
Keyword(s):  
Autocorrelation Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Good Condition ◽  
Vibration Response ◽  
Value Change ◽  
Change Rate ◽  
Damage Location ◽  
Degree Of Damage ◽  
Peak Value

A damage location method for the autocorrelation peak value change rate based on the vibration response of a random vibration structure is established. To calculate the autocorrelation function of the vibration response of each measurement point, we transformed the maximum values into an autocorrelation peak vector. Under a good condition, the autocorrelation peak vector has a fixed shape; hence, it can be used as a basis for structural damage identification. The two adjacent measurement points with the largest change corresponding to the two nodes of the damage unit and the damage location are determined to calculate the change rate of the autocorrelation peak values between damaged and intact structures. When the degree of damage is 5%, the autocorrelation peak value change rate of the acceleration response on the two nodes of the damage unit is significantly greater than that of the other points, which can accurately determine the damage location, indicating that the damage location index constructed has good damage sensitivity. The damage location index can determine a single damage, as well as a double damage. The antinoise capability of the damage location index gradually improves with an increase in the degree of damage. At 45% degree of damage and signal-to-noise ratio (SNR) of 0 dB, the damage location index can still accurately determine the damage location, which has good antinoise interference capability. The Xi’an Bell Tower is used as a case study, and the feasibility of this method is verified, which provides a new method for the study of damage location of ancient timber structures.

Damage Severity Assessment Using Modified BP Neural Network

2011 ◽  
Vol 179-180 ◽  
pp. 1016-1020
Author(s):  
Xiao Ma Dong ◽  
Zhong Hui Wang
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Simulation Experiment ◽  
Damage Identification ◽  
Severity Assessment ◽  
Improved Method ◽  
Modal Frequency ◽  
Structural Damage Identification

Damage severity identification is an important content among structural damage identification. In order to avoid the disadvantages of conventional BPNN, a modified BP neural network was proposed to identify structural damage severity in this paper. The modified BPNN was trained by using structural modal frequency qua BPNN input, and then used to forecast structural damage severity. Finally, the results of simulation experiment of composite material cantilever girder show that the improved method is very effective for damage severity identification and possess great applied foreground.

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