The multi-objective optimization of tunneling boring machine control based on geological conditions identification
Purpose – The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, t...
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2020-12-01
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| Series: | Journal of Intelligent Manufacturing and Special Equipment |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/JIMSE-07-2020-0005/full/pdf?title=the-multi-objective-optimization-of-tunneling-boring-machine-control-based-on-geological-conditions-identification |
| _version_ | 1811258172001746944 |
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| author | Hongyuan Wang Jingcheng Wang |
| author_facet | Hongyuan Wang Jingcheng Wang |
| author_sort | Hongyuan Wang |
| collection | DOAJ |
| description | Purpose – The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability. Design/methodology/approach – This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end. Findings – This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data. Originality/value – This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability. |
| first_indexed | 2024-04-12T18:10:04Z |
| format | Article |
| id | doaj.art-513df63b454340a79f61286ea02cb570 |
| institution | Directory Open Access Journal |
| issn | 2633-6596 2633-660X |
| language | English |
| last_indexed | 2024-04-12T18:10:04Z |
| publishDate | 2020-12-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Journal of Intelligent Manufacturing and Special Equipment |
| spelling | doaj.art-513df63b454340a79f61286ea02cb5702022-12-22T03:21:52ZengEmerald PublishingJournal of Intelligent Manufacturing and Special Equipment2633-65962633-660X2020-12-01118710510.1108/JIMSE-07-2020-0005653921The multi-objective optimization of tunneling boring machine control based on geological conditions identificationHongyuan Wang0Jingcheng Wang1Department of Automation, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai, ChinaPurpose – The purpose of this paper aims to design an optimization control for tunnel boring machine (TBM) based on geological identification. For unknown geological condition, the authors need to identify them before further optimization. For fully considering multiple crucial performance of TBM, the authors establish an optimization problem for TBM so that it can be adapted to varying geology. That is, TBM can operate optimally under corresponding geology, which is called geology-adaptability. Design/methodology/approach – This paper adopted k-nearest neighbor (KNN) algorithm with modification to identify geological conditions. The modification includes adjustment of weights in voting procedure and similarity distance measurement, which at suitable for engineering and enhance accuracy of prediction. The authors also design several key performances of TBM during operation, and built a multi-objective function. Further, the multi-objective function has been transformed into a single objective function by weighted-combination. The reformulated optimization was solved by genetic algorithm in the end. Findings – This paper provides a support for decision-making in TBM control. Through proposed optimization control, the advance speed of TBM has been enhanced dramatically in each geological condition, compared with the results before optimizing. Meanwhile, other performances are acceptable and the method is verified by in situ data. Originality/value – This paper fulfills an optimization control of TBM considering several key performances during excavating. The optimization is conducted under different geological conditions so that TBM has geological-adaptability.https://www.emerald.com/insight/content/doi/10.1108/JIMSE-07-2020-0005/full/pdf?title=the-multi-objective-optimization-of-tunneling-boring-machine-control-based-on-geological-conditions-identificationtunnel boring machineknngeological identificationmulti-objective optimizationweight adjustmentgeological adaptability |
| spellingShingle | Hongyuan Wang Jingcheng Wang The multi-objective optimization of tunneling boring machine control based on geological conditions identification Journal of Intelligent Manufacturing and Special Equipment tunnel boring machine knn geological identification multi-objective optimization weight adjustment geological adaptability |
| title | The multi-objective optimization of tunneling boring machine control based on geological conditions identification |
| title_full | The multi-objective optimization of tunneling boring machine control based on geological conditions identification |
| title_fullStr | The multi-objective optimization of tunneling boring machine control based on geological conditions identification |
| title_full_unstemmed | The multi-objective optimization of tunneling boring machine control based on geological conditions identification |
| title_short | The multi-objective optimization of tunneling boring machine control based on geological conditions identification |
| title_sort | multi objective optimization of tunneling boring machine control based on geological conditions identification |
| topic | tunnel boring machine knn geological identification multi-objective optimization weight adjustment geological adaptability |
| url | https://www.emerald.com/insight/content/doi/10.1108/JIMSE-07-2020-0005/full/pdf?title=the-multi-objective-optimization-of-tunneling-boring-machine-control-based-on-geological-conditions-identification |
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