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Forecasting Damage Mechanics By Deep Learning

Duyen Le Hien Nguyen1, Dieu Thi Thanh Do2, Jaehong Lee2, Timon Rabczuk3, Hung Nguyen-Xuan1,4,*

1 CIRTech Institute, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam.
2 Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, Korea.
3 Institute of Structural Mechanics, Bauhaus-Universität Weimar, 99423, Weimar, Germany.
4 Department of Physical Therapy, Graduate Institute of Rehabilitation Science, China Medical University, Taichung, 40402, Taiwan.

* Corresponding Author: Hung Nguyen-Xuan. Email:

Computers, Materials & Continua 2019, 61(3), 951-977.


We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.


Cite This Article

D. Le Hien Nguyen, D. Thi Thanh Do, J. Lee, T. Rabczuk and H. Nguyen-Xuan, "Forecasting damage mechanics by deep learning," Computers, Materials & Continua, vol. 61, no.3, pp. 951–977, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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