TY  - EJOU
AU  - Nguyen, Duyen Le Hien 
AU  - Do, Dieu Thi Thanh 
AU  - Lee, Jaehong 
AU  - Rabczuk, Timon 
AU  - Nguyen-Xuan, Hung 

TI  - Forecasting Damage Mechanics By Deep Learning
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 61
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Damage mechanics
KW  -  time series forecasting
KW  -  deep learning
KW  -  long short-term memory
KW  -  multi-layer neural networks
KW  -  hydraulic fracturing

DO  - 10.32604/cmc.2019.08001