Structural Damage Identification Method Based on IMF Model Energy Feature and BP Neural Network

2011 ◽  
Author(s):  
Zhaowei Sun ◽  
Gang Shi ◽  
Xiaoping Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Identification Method ◽  
Structural Damage Identification ◽  
Energy Feature

scholarly journals Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

2018 ◽  
Vol 8 (2) ◽  
pp. 168-175 ◽  
Author(s):  
Xiangyi Geng ◽  
Shizeng Lu ◽  
Mingshun Jiang ◽  
Qingmei Sui ◽  
Shanshan Lv ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Damage Identification

Damage Identification of Composite Materials Based on PNN

2010 ◽  
Vol 29-32 ◽  
pp. 642-645
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Performance Comparison ◽  
Measurement Noise ◽  
Dynamic Approach ◽  
Numerical Examples ◽  
Structural Damage Identification

In recent years, there were been increasing researches focusing on the application of artificial neural networks in structural damage identification. Most of them perform well with numerical examples under error-free conditions, but become worse when the experimental data are polluted with measurement noise. In this paper, a dynamic approach based on PNN for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to PNN for training the network and identifying the damages. A performance comparison between the PNN and BPNN for structural damage identification was carried out. The results show that the proposed method can more exactly identify the faults than the BP neural network.

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.

scholarly journals A Mahalanobis Distance Cumulant-Based Structural Damage Identification Method with IMFs and Fitting Residual of SHM Measurements

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Chuang Chen ◽  
Yinhui Wang ◽  
Tao Wang ◽  
Xiaoyan Yang
Keyword(s):  
Mahalanobis Distance ◽  
Structural Damage ◽  
Damage Identification ◽  
Feature Vector ◽  
Recursive Algorithm ◽  
Intrinsic Mode Functions ◽  
Data Set ◽  
Identification Method ◽  
Structural Damage Identification ◽  
Emd Method

Data-driven damage identification based on measurements of the structural health monitoring (SHM) system is a hot issue. In this study, based on the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) method and the trend term fitting residual of measured data, a structural damage identification method based on Mahalanobis distance cumulant (MDC) was proposed. The damage feature vector is composed of the squared MDC values and is calculated by the segmentation data set. It makes the changes of monitoring points caused by damage accumulate as “amplification effect,” so as to obtain more damage information. The calculation method of the damage feature vector and the damage identification procedure were given. A mass-spring system with four mass points and four springs was used to simulate the damage cases. The results showed that the damage feature vector MDC can effectively identify the occurrence and location of the damage. The dynamic measurements of a prestress concrete continuous box-girder bridge were used for decomposing into IMFs and the trend term by the EMD method and the recursive algorithm autoregressive-moving average with the exogenous inputs (RARMX) method, which were used for fitting the trend term and to obtain the fitting residual. By using the first n-order IMFs and the fitting residual as the clusters for damage identification, the effectiveness of the method is also shown.

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.

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