@Article{cmc.2019.08001,
AUTHOR = {Duyen Le Hien Nguyen, Dieu Thi Thanh Do, Jaehong Lee, Timon Rabczuk, Hung Nguyen-Xuan},
TITLE = {Forecasting Damage Mechanics By Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {61},
YEAR = {2019},
NUMBER = {3},
PAGES = {951--977},
URL = {http://www.techscience.com/cmc/v61n3/35283},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2019.08001}
}