A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures

Tran-Ngoc, H.; Khatir, Samir; Le-Xuan, T.; Roeck, Guido De; Bui-Tien, Thanh; Wahab, Magd Abdel

doi:10.1016/j.ijengsci.2020.103376

Cited by 74 publications

(12 citation statements)

References 34 publications

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“…The possible solution is to use an Artificial Neural Networks (ANN). They have been proven to be successful in various applications, i.e., in the use to Structural Health Monitoring (SHM) [ 39 ] or to damage detection [ 40 ]; however, they do not allow to fully avoid the described problematics. Recently, due to the innumerous extension of capabilities of computing machines, the deep learning methodology, that were created on the basis of ANN, brings new perspective and shows great potential to identification procedures’ automatization.…”

Section: Introductionmentioning

confidence: 99%

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

2021

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This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time–frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials.

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Section: Introductionmentioning

confidence: 99%

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

2021

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“…[16][17][18][19][20][21][22][23] Given that, Shi et al [24][25][26] have reviewed the application of ML method in materials engineering as well as the functional field. In addition, we identify several common themes associated with the application of ML approaches, such as predictions of phase diagrams, [27] crystal structures, [28] damage identification, [29][30][31][32][33] and materials properties, [34][35][36][37] which significantly accelerates the discovery of new materials via a data-driven materials research approach. The ML approaches commonly exhibit excellent performance in dealing with the complex multivariate nonlinear relationship between input and output variables in view of the existing data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel

Qiao

Zhu

Yuan

2021

steel research int.

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In this article, different machine learning (ML) algorithms are provided to predict the transformation temperature of pearlite using relevant material descriptors, austenitizing temperature, and cooling rate. To search for an appropriate model, the predictive performance of ML model including artificial neural network (ANN), generalized regression neural network (GRNN), radial basis function neural network (RBFNN), and extreme learning machine (ELM) is evaluated and compared on testing dataset. To quickly find the appropriate parameters, artificial fish swarm algorithm (AFSA) is applied to further improve the prediction accuracy of ANN model. In view of the distinguished prediction performance, the regression analysis shows that the GRNN model performs favorably against the other learning models. To verify the superiority of proposed model, a type of pearlitic steel with hypereutectoid composition is prepared. The phase transition temperature and transformation products are determined using diametral dilatometric and microscopy technique through continuous cooling transformation with cooling rates of 1 and . As a result, the experimental results agree well with the predictive results. The work proposes a reliable model to predict the phase transition temperature which can facilitate the optimization of process parameters to achieve the best possible microstructure.

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“…In recent years, some research in the interpretation of SHM data has been reported (Tran-Ngoc et al , 2018, 2020; Gillich et al , 2019; Khatir and Abdel Wahab, 2019; Khatir et al , 2019, 2020; Nie et al , 2020). Based on the presence of behaviour models in the analysis, the data interpretation of SHM can be categorised into model-based and data-based interpretation methods.…”

Section: Introductionmentioning

confidence: 99%

Estimation of structural response using convolutional neural network: application to the Suramadu bridge

Pamuncak

Salami

Adha

et al. 2021

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PurposeStructural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.Design/methodology/approachThe CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.FindingsThe CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.Originality/valueThis work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

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A novel machine-learning based on the global search techniques using vectorized data for damage detection in structures

Cited by 74 publications

References 34 publications

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel

Estimation of structural response using convolutional neural network: application to the Suramadu bridge

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