Concrete acoustic emission signal augmentation method based on generative adversarial networks

Fu, Wei; Zhou, Ruohua; Guo, Ziye

doi:10.1016/j.measurement.2024.114574

Measurement

2024

DOI: 10.1016/j.measurement.2024.114574

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Concrete acoustic emission signal augmentation method based on generative adversarial networks

Wei Fu,

Ruohua Zhou,

Ziye Guo

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Cited by 3 publications

References 36 publications

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Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Inderyas,

Alver,

Tayfur

et al. 2024

Arab J Sci Eng

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Acoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

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Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Inderyas,

Alver,

Tayfur

et al. 2024

Arab J Sci Eng

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Classification method for crack modes in concrete by acoustic emission signals with semi-parametric clustering and support vector machine

Yu,

Liang,

2025

Measurement

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Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network

Zeng,

Deng,

Xiong

et al. 2024

Structural Health Monitoring

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Influenced by factors such as temperature, aging, and overloading, rubber bearings may undergo rupture during prolonged usage, leading to severe consequences such as bearing failure and structural damage. To accurately assess the degree of rubber rupture damage, this study proposed a novel generative adversarial network (GAN)-enhanced Bayesian-optimized one-dimensional convolutional neural network (1DCNN) framework (GAN-BCNN). In the GAN-BCNN framework, GAN is used to enhance the proportion of damaged data in the database, while Bayesian optimization is utilized to optimize crucial hyperparameters in the 1DCNN network. The predictive results indicate that the proposed GAN-BCNN model accurately predicts the degree of rubber rupture damage, achieving high classification accuracies of 99.1, 95.5, 92.9, and 98.0% for the categories of no damage, slight damage, moderate damage, and severe damage, respectively. When compared to the linear discriminant analysis model and the BCNN model without GAN enhancement, the proposed GAN-BCNN model exhibits a significant improvement in predictive accuracy, with an increase in Accuracy values of 52.4 and 6.0%, respectively. These results indicate that the GAN-BCNN model holds promising practical applications, and the GAN algorithm contributes to improving the prediction accuracy of the model.

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Concrete acoustic emission signal augmentation method based on generative adversarial networks

Cited by 3 publications

References 36 publications

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

Classification method for crack modes in concrete by acoustic emission signals with semi-parametric clustering and support vector machine

Predicting the degree of rubber rupture damage using a GAN-enhanced Bayesian-optimized 1DCNN network

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