Geomechanics and Engineering

Techno-Press (테크노프레스)
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Several models for tunnel boring machine performance prediction based on machine learning

  • Mahmoodzadeh, Arsalan (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Nejati, Hamid Reza (Rock Mechanics Division, School of Engineering, Tarbiat Modares University) ;
  • Ibrahim, Hawkar Hashim (Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil) ;
  • Ali, Hunar Farid Hama (Department of Civil Engineering, University of Halabja) ;
  • Mohammed, Adil Hussein (Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil) ;
  • Rashidi, Shima (Department of Computer Science, College of Science and Technology, University of Human Development) ;
  • Majeed, Mohammed Kamal (Information Technology Department, Faculty of Science, Tishk International University (TIU))
  • Received : 2021.08.20
  • Accepted : 2022.05.04
  • Published : 2022.07.10
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Abstract

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

Keywords

References

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