Long-Term Prediction of Crack Growth Using Deep Recurrent Neural Networks and Nonlinear Regression: A Comparison Study

Long-Term Prediction of Crack Growth Using Deep Recurrent Neural Networks and Nonlinear Regression: A Comparison Study

Cracks in a building can potentially result in financial and life losses. Thus, it is essential to predict when the crack growth is reaching a certain threshold, to prevent possible disaster. However, long-term prediction of the crack growth in newly built facilities or existing facilities with rece...

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Bibliographic Details
Main Authors:	Salahuddin Muhammad Iqbal, Jun-Ryeol Park, Kyu-Il Jung, Jun-Seoung Lee, Dae-Ki Kang
Format:	Article
Language:	English
Published:	MDPI AG 2022-10-01
Series:	Applied Sciences
Subjects:	deep recurrent neural networks LSTM Seq2Seq LSTM segmented nonlinear regression long-term prediction crack growth
Online Access:	https://www.mdpi.com/2076-3417/12/20/10514

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