Application of Improved BPNN to Damage Detection of Composite Materials

2011 ◽  
Vol 467-469 ◽  
pp. 1097-1101
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Dynamic Method ◽  
Damage Identification ◽  
Wavelet Series ◽  
Improved Algorithm

A dynamic method based on improved algorithm BP neural network for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to improved algorithm BP neural network for training the network and identifying the damages. Finally, the experiment results show that this proposed method can exactly identify the faults of composite materials.

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.

scholarly journals A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sergio V. Farias ◽  
Osamu Saotome ◽  
Haroldo F. Campos Velho ◽  
Elcio H. Shiguemori
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Lamb Wave ◽  
Learning Strategy ◽  
Damage Identification ◽  
Particle Collision ◽  
Learning Approaches ◽  
Propagation Methods ◽  
Artificial Neural Network Ann ◽  
Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

2020 ◽  
Author(s):  
Shweta Dabetwar ◽  
Stephen Ekwaro-Osire ◽  
João Paulo Dias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Materials ◽  
Data Fusion ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Mixed Data ◽  
Image Encoding ◽  
Using Data

Abstract Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.

Research on Homogenization of Composite Materials

2013 ◽  
Vol 663 ◽  
pp. 426-430
Author(s):  
Zhen Yu Zhou ◽  
Qi Wen Xue
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Pressure Vessels ◽  
Equivalent Material ◽  
Filament Wound ◽  
Filament Wound Composite ◽  
Output Information ◽  
Wound Composite ◽  
Composite Pressure Vessels

A numerical model is given to identify equivalent parameters of composite materials, using BP neural network algorithm. Taking Filament-wound composite pressure vessels as the research object, finite element models are first constructed .Getting node displacements as network training samples, the mechanical parameters as output information of network for effective training, the equivalent material parameters can be obtained. The satisfactory numerical validation is given and results show that the proposed method can identify the equivalent modulus and the equivalent Poisson’s ratio of the Filament-wound composite pressure vessels with precision. The computational efficiency is improved with 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.

Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Structural Damage ◽  
The State ◽  
Structural Damage Detection ◽  
Bp Neural Networks ◽  
Simply Supported ◽  
Modal Flexibility

Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

scholarly journals Beam Structure Damage Identification Based on BP Neural Network and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Yan ◽  
Yao Cui ◽  
Lin Zhang ◽  
Chao Zhang ◽  
Yongzhi Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Damage Identification ◽  
Support Vector ◽  
Single Crack ◽  
Beam Structure ◽  
Strain Mode ◽  
Structure Damage ◽  
Model Based

It is not easy to find marine cracks of structures by directly manual testing. When the cracks of important components are extended under extreme offshore environment, the whole structure would lose efficacy, endanger the staff’s safety, and course a significant economic loss and marine environment pollution. Thus, early discovery of structure cracks is very important. In this paper, a beam structure damage identification model based on intelligent algorithm is firstly proposed to identify partial cracks in supported beams on ocean platform. In order to obtain the replacement mode and strain mode of the beams, the paper takes simple supported beam with single crack and double cracks as an example. The results show that the difference curves of strain mode change drastically only on the injured part and different degrees of injury would result in different mutation degrees of difference curve more or less. While the model based on support vector machine (SVM) and BP neural network can identify cracks of supported beam intelligently, the methods can discern injured degrees of sound condition, single crack, and double cracks. Furthermore, the two methods are compared. The results show that the two methods presented in the paper have a preferable identification precision and adaptation. And damage identification based on support vector machine (SVM) has smaller error results.

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