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 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.

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.

Detection Indicator of Structural Nondestructive Damage Based on Flexibility Curvature Difference Rate

2015 ◽  
Vol 744-746 ◽  
pp. 46-52 ◽  
Author(s):  
Chang Sheng Xiang ◽  
Yu Zhou ◽  
Sheng Kui Di ◽  
Li Xian Wang ◽  
Jian Shu Cheng
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Continuous Beam ◽  
Change Rate ◽  
Modal Frequency ◽  
Numerical Examples ◽  
Modal Flexibility ◽  
Structural Damage Identification ◽  
Before And After ◽  
Better Than

Applied to the structural damage identification, Modal Flexibility is better than the Modal Frequency and Modal Displacement, the indicators of Flexibility Curvature are effective and sensitive. This paper proposes a new detection indicator which is Flexibility Curvature Difference Rate (FCDR) that by using the change rate of diagonal elements of flexibility curvature difference when before and after damage. The numerical examples of a simple beam, a continuous beam and a frame with the damage conditions of the different positions and different degrees are used to verify FCDR. The result shows that FCDR can well identify the numerical examples damages, and sensitively diagnose the damage near the supports of beam and the nodes of framework.

Crack Damage Identification of Reinforced Concrete Simply Supported Beam Based on BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 738-741
Author(s):  
Xiao Ming Yang ◽  
Fu Li
Keyword(s):  
Neural Network ◽  
Reinforced Concrete ◽  
Bp Neural Network ◽  
Structural Damage ◽  
Damage Identification ◽  
Simply Supported Beam ◽  
Simply Supported ◽  
Before And After ◽  
Trained Neural Network ◽  
Application Prospects

Considering the good forecasting capability of BP neural network, a new crack damage identification method for reinforced concrete simply supported beam is proposed in this paper. After simulating the crack damage of a reinforced concrete simply supported beam, the natural frequency of the beam is chosen as the input parameters of the BP neural network. The data before and after damage of the simply supported beam are put into the trained neural network to judge the structural damage. The results demonstrate that the approach has a better application prospects in structural damage identification.

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