Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction
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| Publicado no: | Geosciences vol. 15, no. 2 (2025), p. 47 |
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| Autor principal: | |
| Outros Autores: | , , , , , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/geosciences15020047 |2 doi | |
| 035 | |a 3170953598 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231468 |2 nlm | ||
| 100 | 1 | |a Yang, Bo |u School of Resources and Safety Engineering, Central South University, Changsha 410083, China; <email>225511001@csu.edu.cn</email> | |
| 245 | 1 | |a Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real time during tunnel boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions per minute (RPM), penetration rate (PR), advance rate (AR), penetration per revolution (PRev), and field penetration index (FPI), with rock classification based on the Rock Mass Rating (RMR) method. To address the class imbalance, the Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed the MFO-RF model’s superior performance, with training and testing accuracies of 0.992 and 0.927, respectively, and key predictors identified as PR, AR, and RPM. Additional validation using 91 data sets confirmed the reliability of the MFO-RF model on unseen data, achieving an accuracy of 0.879. A graphical user interface was also developed, enabling field engineers and technicians to make instant and reliable rock classification predictions, greatly supporting safe tunnel construction and operational efficiency. These models contribute valuable tools for real-time, data-driven decision-making in tunneling projects. | |
| 653 | |a Boring machines | ||
| 653 | |a Classification | ||
| 653 | |a Construction | ||
| 653 | |a Rock | ||
| 653 | |a Technicians | ||
| 653 | |a Rock mass rating | ||
| 653 | |a Thrust | ||
| 653 | |a Performance testing | ||
| 653 | |a Rocks | ||
| 653 | |a Graphical user interface | ||
| 653 | |a Sampling techniques | ||
| 653 | |a Tunnel construction | ||
| 653 | |a Data compression | ||
| 653 | |a Tunneling | ||
| 653 | |a Decision trees | ||
| 653 | |a Geology | ||
| 653 | |a Accuracy | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sampling methods | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Support vector machines | ||
| 653 | |a Algorithms | ||
| 653 | |a Engineering | ||
| 653 | |a Tunnels | ||
| 653 | |a Methods | ||
| 653 | |a Performance assessment | ||
| 653 | |a Probability theory | ||
| 653 | |a Real time | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Mathematical models | ||
| 653 | |a Drilling & boring machinery | ||
| 653 | |a Decision making | ||
| 700 | 1 | |a Danial Jahed Armaghani |u School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia; <email>danial.jahedarmaghani@uts.edu.au</email> | |
| 700 | 1 | |a Fattahi, Hadi |u Faculty of Earth Sciences Engineering, Arak University of Technology, Arak 3818146763, Iran; <email>h.fattahi@arakut.ac.ir</email> | |
| 700 | 1 | |a Afrazi, Mohammad |u Department of Mechanical Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA; <email>mohammad.afrazi@student.nmt.edu</email> | |
| 700 | 1 | |a Koopialipoor, Mohammadreza |u Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran; <email>mr.koopialipoor@aut.ac.ir</email> | |
| 700 | 1 | |a Asteris, Panagiotis G |u Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Marousi, 15122 Athens, Greece; <email>asteris@aspete.gr</email> | |
| 700 | 1 | |a Khandelwal, Manoj |u Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia | |
| 773 | 0 | |t Geosciences |g vol. 15, no. 2 (2025), p. 47 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3170953598/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3170953598/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3170953598/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |