A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS
The prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most important concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnic...
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| Format: | Article |
| Language: | English |
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University of Zagreb
2020-01-01
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| Series: | Rudarsko-geološko-naftni Zbornik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/344636 |
| _version_ | 1827159743195512832 |
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| author | Mohammad Hossein Arbabsiar Mohammad Ali Ebrahimi Farsangi Hamid Mansouri |
| author_facet | Mohammad Hossein Arbabsiar Mohammad Ali Ebrahimi Farsangi Hamid Mansouri |
| author_sort | Mohammad Hossein Arbabsiar |
| collection | DOAJ |
| description | The prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most important concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnical risks (representing media characteristics) in rock TBM tunnelling. Fifteen main parameters that influence the geotechnical hazards were used in the modelling. In establishing an interaction matrix and also a parameter rating, the views of five experts were taken into account. The Vulnerability Index (VI) (geotechnical risk levels) for 2058 datasets out of 2168 sets of data from 53 geological zones in 11 km of the Zagros long tunnel was obtained. In the second step, based on the machine operating parameters such as torque, cutter head rotation per minute, cutter normal force and media characteristics (represented by VIs), which were used as input parameters and advance rate was used as an output parameter, while using 2058 datasets, linear and non-linear multiple regression analyses were carried out. 110 datasets (out of 2168 datasets), which were not used in the modelling, were applied to evaluate the performance of regression models and other models in literature and the results were compared. The obtained results showed that the new linear model proposed with R2=0.83 and RMSE=0.12 has a better performance than the other models. |
| first_indexed | 2024-04-24T09:20:28Z |
| format | Article |
| id | doaj.art-8cd26c2d68d14080ac55b291427caf87 |
| institution | Directory Open Access Journal |
| issn | 0353-4529 1849-0409 |
| language | English |
| last_indexed | 2025-03-21T00:01:59Z |
| publishDate | 2020-01-01 |
| publisher | University of Zagreb |
| record_format | Article |
| series | Rudarsko-geološko-naftni Zbornik |
| spelling | doaj.art-8cd26c2d68d14080ac55b291427caf872024-08-03T11:32:58ZengUniversity of ZagrebRudarsko-geološko-naftni Zbornik0353-45291849-04092020-01-01352577410.17794/rgn.2020.2.6A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONSMohammad Hossein Arbabsiar0Mohammad Ali Ebrahimi Farsangi1Hamid Mansouri2Mining Engineering Department, Shahid Bahonar University of Kerman, Kerman, IranMining Engineering Department, Shahid Bahonar University of Kerman, Kerman, IranMining Engineering Department, Shahid Bahonar University of Kerman, Kerman, IranThe prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most important concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnical risks (representing media characteristics) in rock TBM tunnelling. Fifteen main parameters that influence the geotechnical hazards were used in the modelling. In establishing an interaction matrix and also a parameter rating, the views of five experts were taken into account. The Vulnerability Index (VI) (geotechnical risk levels) for 2058 datasets out of 2168 sets of data from 53 geological zones in 11 km of the Zagros long tunnel was obtained. In the second step, based on the machine operating parameters such as torque, cutter head rotation per minute, cutter normal force and media characteristics (represented by VIs), which were used as input parameters and advance rate was used as an output parameter, while using 2058 datasets, linear and non-linear multiple regression analyses were carried out. 110 datasets (out of 2168 datasets), which were not used in the modelling, were applied to evaluate the performance of regression models and other models in literature and the results were compared. The obtained results showed that the new linear model proposed with R2=0.83 and RMSE=0.12 has a better performance than the other models.https://hrcak.srce.hr/file/344636advance rateregression modelsTBM geotechnical riskrock engineering systemshard rock TBMZagros long tunnel |
| spellingShingle | Mohammad Hossein Arbabsiar Mohammad Ali Ebrahimi Farsangi Hamid Mansouri A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS Rudarsko-geološko-naftni Zbornik advance rate regression models TBM geotechnical risk rock engineering systems hard rock TBM Zagros long tunnel |
| title | A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS |
| title_full | A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS |
| title_fullStr | A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS |
| title_full_unstemmed | A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS |
| title_short | A NEW MODEL FOR PREDICTING THE ADVANCE RATE OF A TUNNEL BORING MACHINE (TBM) IN HARD ROCK CONDITIONS |
| title_sort | new model for predicting the advance rate of a tunnel boring machine tbm in hard rock conditions |
| topic | advance rate regression models TBM geotechnical risk rock engineering systems hard rock TBM Zagros long tunnel |
| url | https://hrcak.srce.hr/file/344636 |
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