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Application of deep neural networks in predicting the penetration rate of tunnel boring machines

  • Mohammadreza KoopialipoorEmail author
  • Hossein Tootoonchi
  • Danial Jahed ArmaghaniEmail author
  • Edy Tonnizam Mohamad
  • Ahmadreza Hedayat
Original Paper
  • 9 Downloads

Abstract

Performance prediction in mechanized tunnel projects utilizing a tunnel boring machine (TBM) is a prerequisite to accurate and reliable cost estimation and project scheduling. A wide variety of artificial intelligence methods have been utilized in the prediction of the penetration rate of TBMs. This study focuses on developing a model based on deep neural networks (DNNs), which is an advanced version of artificial neural networks (ANNs), for prediction of the TBM penetration rate based on the data obtained from the Pahang–Selangor raw water transfer tunnel in Malaysia. To evaluate and document the success and reliability of the new DNN model, an ANN model based on five different data categories from the established database was developed and compared with the DNN model. Based on the results obtained of the coefficient of determination and root mean square error (RMSE), a significant increase in the performance prediction of the penetration rate is achieved by developing a DNN predictive model. The DNN model demonstrated better performance for penetration rate estimation compared with the ANN model and it can be introduced as a newly developed model in the field of TBM performance assessment.

Keywords

Deep neural network Artificial neural network Penetration rate Tunnel boring machine 

Notes

Acknowledgements

The authors wish to express their appreciation to Universiti Teknologi Malaysia for supporting the present research.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mohammadreza Koopialipoor
    • 1
    Email author
  • Hossein Tootoonchi
    • 1
  • Danial Jahed Armaghani
    • 2
    Email author
  • Edy Tonnizam Mohamad
    • 3
  • Ahmadreza Hedayat
    • 4
  1. 1.Faculty of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  3. 3.Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruUSA
  4. 4.Department of Civil and Environmental EngineeringColorado School of MinesGoldenUSA

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