MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity
Testing 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 s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9032189/ |
| _version_ | 1819295782356910080 |
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| author | Sen Zhang Wanyin Wu Zhao Yang Xu Lin Zhihua Ren Zhixin Yan |
| author_facet | Sen Zhang Wanyin Wu Zhao Yang Xu Lin Zhihua Ren Zhixin Yan |
| author_sort | Sen Zhang |
| collection | DOAJ |
| description | Testing 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. |
| first_indexed | 2024-12-24T04:47:41Z |
| format | Article |
| id | doaj.art-36a9cb47868c440eb19f8e53b9830985 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:47:41Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-36a9cb47868c440eb19f8e53b98309852022-12-21T17:14:39ZengIEEEIEEE Access2169-35362020-01-018502565026710.1109/ACCESS.2020.29798339032189MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing CapacitySen Zhang0Wanyin Wu1https://orcid.org/0000-0002-8042-393XZhao Yang2Xu Lin3Zhihua Ren4Zhixin Yan5Yunnan Research Institute of Highway Science and Technology, Kunming, ChinaUnion Vision Innovation, Shenzhen, ChinaSchool of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, ChinaUnion Vision Innovation, Shenzhen, ChinaYunnan Research Institute of Highway Science and Technology, Kunming, ChinaCollege of Civil Engineering and Mechanics, Lanzhou University, Lanzhou, ChinaTesting 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.https://ieeexplore.ieee.org/document/9032189/Toolboxthe loss rate of the lining bearing capacitymachine learningtunnel health |
| spellingShingle | Sen Zhang Wanyin Wu Zhao Yang Xu Lin Zhihua Ren Zhixin Yan MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity IEEE Access Toolbox the loss rate of the lining bearing capacity machine learning tunnel health |
| title | MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity |
| title_full | MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity |
| title_fullStr | MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity |
| title_full_unstemmed | MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity |
| title_short | MLLBC: A Machine Learning Toolbox for Modeling the Loss Rate of the Lining Bearing Capacity |
| title_sort | mllbc a machine learning toolbox for modeling the loss rate of the lining bearing capacity |
| topic | Toolbox the loss rate of the lining bearing capacity machine learning tunnel health |
| url | https://ieeexplore.ieee.org/document/9032189/ |
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