兰州大学机构库 >土木工程与力学学院
MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity
Zhang, Sen1,2; Wu, Wanyin3; Yang, Zhao4; Lin, Xu3; Ren, Zhihua1; Yan, ZX(言志信)2
2020
Source PublicationIEEE Access   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN21693536
Volume8Pages:50256-50267
page numbers12
AbstractTesting the health of tunnels, as a branch of highway operation, has an extremely important application in public property and even life safety. Among them, there are many factors that cause the tunnel to deform or collapse. The conventional methods use the finite element method (FEM) which are to simulate the bearing capacity loss rate of the lining by using the mechanical method. However, it takes a long time to calculate the stress-strain-situation of the lining model under each condition. This paper explores the machine learning to calculate the loss rate of the lining bearing capacity under more conditions based on FEM simulation data. Here, we establish a machine learning toolbox for modeling the loss rate of the lining bearing capacity named 'MLLBC', which contains three main components: 1) data loading; 2) machine learning model deployment; 3) performance evaluation. To ensure the fairness of model evaluation, ten machine learning models use a unified code library. We also conduct experiments on our new dataset which is the loss rate of the lining bearing capacity with different data amounts, as well as experiments on the goodness of model fitting under different ranges of various variables.
© 2013 IEEE.
KeywordMachine learning Data models Tools Load modeling Analytical models Finite element analysis Neural networks Toolbox the loss rate of the lining bearing capacity machine learning tunnel health
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI10.1109/ACCESS.2020.2979833
URL查看原文
Indexed BySCIE ; EI
Language英语
Funding Project[2016(A)01] ; National Natural Science Foundation of China[61501177]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000524898700006
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20201308360405
EI KeywordsBearing capacity ; Bearings (machine parts) ; Learning systems ; Linings ; Machine learning ; Safety testing
EI Classification NumberMachine Components:601.2 ; Accidents and Accident Prevention:914.1 ; Numerical Methods:921.6 ; Materials Science:951
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/417131
Collection土木工程与力学学院
Corresponding AuthorWu, Wanyin; Yang, Zhao
Affiliation
1.Yunnan Research Institute of Highway Science and Technology, Kunming; 650051, China
2.College of Civil Engineering and Mechanics, Lanzhou University, Lanzhou; 730000, China
3.Union Vision Innovation, Shenzhen, China
4.School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, China
First Author AffilicationSchool of Civil Engineering and Mechanics
Recommended Citation
GB/T 7714
Zhang, Sen,Wu, Wanyin,Yang, Zhao,et al. MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity[J]. IEEE Access,2020,8:50256-50267.
APA Zhang, Sen,Wu, Wanyin,Yang, Zhao,Lin, Xu,Ren, Zhihua,&Yan, Zhixin.(2020).MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity.IEEE Access,8,50256-50267.
MLA Zhang, Sen,et al."MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity".IEEE Access 8(2020):50256-50267.
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