Classification of cracking sources of different engineering media via machine learning

Complex civil structures require the cooperation of many building materials. However, it is difficult to accurately monitor and evaluate the inner damage states of various material systems. Based on a convolutional neural network (CNN) and the acoustic emission (AE) time‐frequency diagram, we used t...

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Veröffentlicht in:	Fatigue & fracture of engineering materials & structures 2021-09, Vol.44 (9), p.2475-2488
Hauptverfasser:	Huang, Jie, Hu, Qianting, Song, Zhenlong, Zhang, Gongheng, Qin, Chao‐Zhong, Wu, Mingyang, Wang, Xiaodong
Format:	Artikel
Sprache:	eng
Schlagworte:	Acoustic emission Artificial neural networks Building materials Construction materials convolutional neural network Cracking (fracturing) Machine learning Model accuracy Signal classification structure damage recognition transfer learning
Online-Zugang:	Volltext
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