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A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction.

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Title: A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction.
Authors: Lu, Zhipeng1 (AUTHOR), Shi, Kebin1 (AUTHOR) xndsg@sina.com
Source: Earth Science Informatics. Sep2023, Vol. 16 Issue 3, p2925-2938. 14p.
Subject Terms: *Tunnel design & construction, *Penetration mechanics, *Engineering models, *Machine learning, *Optimization algorithms, *Goodness-of-fit tests, *Statistical correlation
Abstract: Scientific and reasonable prediction of tunneling parameters is the premise to ensure the safety of the project. To further improve the accuracy and reliability of the prediction of tunneling parameters. To address the shortcomings of the kernel limit learning machine parameter selection affecting the prediction ability and the characteristics of volatility and non-stationarity of load data, a prediction model based on variational modal decomposition with a hunter-prey algorithm optimized for the kernel limit learning machine improved by the population chaos strategy and the Levy flight strategy is proposed. Relying on the Xinjiang YEGS tunnel project, 6,900 tunneling data sets were selected after data processing, and the data sets were divided according to the construction sequence. Grey correlation analysis was used to select four dimensions of cutter torque, cutter speed, penetration degree, and total thrust to predict the tunneling speed of full-section tunnel boring machines. The results show that under the same conditions, the VMD-LHPO-KELM model shows a 385% and 28% improvement in RMSE value simulation and 50% and 47.7% improvement in MAPE simulation, respectively, compared to the whale algorithm and the unimproved hunter-prey algorithm seeking algorithm, showing a better iteration rate. Compared with the most commonly used neural network VMD-LSTM model in engineering simulation, the goodness of fit coefficient is increased by 9.2%. The established VMD-LHPO-KELM model can better restore the real environmental impact of TBM construction. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 170397316
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lu%2C+Zhipeng%22">Lu, Zhipeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Kebin%22">Shi, Kebin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xndsg@sina.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Sep2023, Vol. 16 Issue 3, p2925-2938. 14p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Tunnel+design+%26+construction%22">Tunnel design & construction</searchLink><br />*<searchLink fieldCode="DE" term="%22Penetration+mechanics%22">Penetration mechanics</searchLink><br />*<searchLink fieldCode="DE" term="%22Engineering+models%22">Engineering models</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Goodness-of-fit+tests%22">Goodness-of-fit tests</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Scientific and reasonable prediction of tunneling parameters is the premise to ensure the safety of the project. To further improve the accuracy and reliability of the prediction of tunneling parameters. To address the shortcomings of the kernel limit learning machine parameter selection affecting the prediction ability and the characteristics of volatility and non-stationarity of load data, a prediction model based on variational modal decomposition with a hunter-prey algorithm optimized for the kernel limit learning machine improved by the population chaos strategy and the Levy flight strategy is proposed. Relying on the Xinjiang YEGS tunnel project, 6,900 tunneling data sets were selected after data processing, and the data sets were divided according to the construction sequence. Grey correlation analysis was used to select four dimensions of cutter torque, cutter speed, penetration degree, and total thrust to predict the tunneling speed of full-section tunnel boring machines. The results show that under the same conditions, the VMD-LHPO-KELM model shows a 385% and 28% improvement in RMSE value simulation and 50% and 47.7% improvement in MAPE simulation, respectively, compared to the whale algorithm and the unimproved hunter-prey algorithm seeking algorithm, showing a better iteration rate. Compared with the most commonly used neural network VMD-LSTM model in engineering simulation, the goodness of fit coefficient is increased by 9.2%. The established VMD-LHPO-KELM model can better restore the real environmental impact of TBM construction. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12145-023-01043-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 2925
    Subjects:
      – SubjectFull: Tunnel design & construction
        Type: general
      – SubjectFull: Penetration mechanics
        Type: general
      – SubjectFull: Engineering models
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Goodness-of-fit tests
        Type: general
      – SubjectFull: Statistical correlation
        Type: general
    Titles:
      – TitleFull: A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction.
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          Name:
            NameFull: Lu, Zhipeng
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          Name:
            NameFull: Shi, Kebin
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          Dates:
            – D: 01
              M: 09
              Text: Sep2023
              Type: published
              Y: 2023
          Identifiers:
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              Value: 18650473
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              Value: 16
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Earth Science Informatics
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