Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Geomechanics and Engineering Volume 30, Number 1, July10 2022 , pages 75-91 DOI: https://doi.org/10.12989/gae.2022.30.1.075 |
|
|
|
Several models for tunnel boring machine performance prediction based on machine learning |
||
Arsalan Mahmoodzadeh, Hamid Reza Nejati, Hawkar Hashim Ibrahim, Hunar Farid Hama Ali,
Adil Hussein Mohammed, Shima Rashidi and Mohammed Kamal Majeed
|
||
| 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-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. | ||
| Key Words | ||
| feature selection; machine learning; penetration rate; tunnel boring machine; tunneling | ||
| Address | ||
| Arsalan Mahmoodzadeh and 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 | ||