Forecasting Damage Mechanics By Deep Learning

作     者：Duyen Le Hien Nguyen Dieu Thi Thanh Do Jaehong Lee Timon Rabczuk Hung Nguyen-Xuan 

作者机构：CIRTech InstituteHo Chi Minh City University of Technology(HUTECH)Ho Chi Minh CityVietnam Department of Architectural EngineeringSejong University209 Neungdong-roGwangjin-guSeoulKorea Institute of Structural MechanicsBauhaus-Universität Weimar99423WeimarGermany Department of Physical TherapyGraduate Institute of Rehabilitation ScienceChina Medical UniversityTaichung40402Taiwan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第61卷第9期

页      面：951-977页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：The author would like to thank European Commission H2020-MSCA-RISE BESTOFRAC project for research funding 

主　　题：Damage mechanics time series forecasting deep learning long short-term memory multi-layer neural networks hydraulic fracturing 

摘      要：We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics *** methodologies that are able to work accurately for less computational and resolving attempts are a significant demand *** 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 *** 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 *** predicted results by deep learning algorithms are well-agreed with experimental data.