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-a), 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.
Arsalan Mahmoodzadeh, Hamid Reza Nejati — Rock Mechanics Division, School of Engineering, Tarbiat Modares University, Tehran, Iran
Hawkar Hashim Ibrahim — Department of Civil Engineering, College of Engineering, Salahaddin University-Erbil, 44002 Erbil, Kurdistan Region, Iraq
Hunar Farid Hama Ali — Department of Civil Engineering, University of Halabja, Halabja, Kurdistan Region, Iraq
Adil Hussein Mohammed — Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
Shima Rashidi — Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
Mohammed Kamal Majeed — Information Technology Department, Faculty of Science, Tishk International University (TIU), Erbil, Kurdistan Region, Iraq
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