Volume 5, Issue 2 (4-2012)
2012, 5(2): 1217-1234 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Lashkaripour G, Akbari M. Predicting of TBM penetration rate using the artificial neural networks (case study- Tabriz subway). Journal of Engineering Geology 2012; 5 (2) :1217-1234
URL: http://jeg.khu.ac.ir/article-1-372-en.html
URL: http://jeg.khu.ac.ir/article-1-372-en.html
Abstract: (9174 Views)
Tunnel boring machines (TBM) are widely used in excavating urban tunnels. These kinds of machines have different types based on supporting faces and tunnel walls. One type of these machines, is the Earth Pressure Balance (EPB) type that was used in excavating the Line 1 Tunnel of Tabriz Metro. Different parameters such as geological conditions, rock mass properties, dip and machine specifications affect the efficiency of the machine. One method of predicting the efficiency of these machines is to estimate their penetration rates. In this study the value of TBM penetration rates are predicted by an artificial neural network. Predicting of this parameter is so effective for conducting in high risk regions by understanding the time of facing to these regions. The main result of this study is to forecast the penetration rate with an acceptable accuracy and to determine the effective parameters through sensitivity analysis measured by an artificial neural network.
Keywords: Predicting of TBM penetration rate, Tabriz Metro Tunnel, Artificial neural network, sensitivity analysis
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
