Geomechanics and Engineering

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Computing machinery techniques for performance prediction of TBM using rock geomechanical data in sedimentary and volcanic formations

  • Hanan Samadi (IRO, Civil Engineering Department, University of Halabja) ;
  • Arsalan Mahmoodzadeh (IRO, Civil Engineering Department, University of Halabja) ;
  • Shtwai Alsubai (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abdullah Alqahtani (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Abed Alanazi (Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University) ;
  • Ahmed Babeker Elhag (Department of Civil Engineering, College of Engineering, King Khalid University)
  • Received : 2023.05.19
  • Accepted : 2024.04.11
  • Published : 2024.05.10
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Abstract

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R2 = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

Keywords

Acknowledgement

This study is supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445). The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Groups RGP. 2/357/44.

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