Real-Time Prediction of Operating Parameter of TBM during Tunneling
Source abstract
Abstract
With the increasing use of the tunnel boring machine (TBM), attempts have been made to predict TBM operating parameters. Prediction of operating parameters is still an important step in the adaptability of the TBM for the future. In this study, we employ a walk forward (WF) prediction method based on ARIMAX, which can consider time-varying features and geological conditions. This method is applied to two different TBM projects to evaluate its performance, and is then compared with WF based on ordinary least squares (OLS). The simulation results show that the ARIMAX predictor outperforms the OLS predictor in both projects. For practical applications, an additional analysis is carried out according to the real-time prediction distance. The results show that time series-based ARIMAX provides meaningful results in 8 rings (11 m) or less of real-time prediction distance. The WF based on ARIMAX can provide reasonable TBM operating conditions with time-varying data and can be utilized in decision-making to improve excavation performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tunneling and Rock Mechanics · Drilling and Well Engineering · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Radioactive Waste Disposal Research Division, Korea Atomic Energy Research Institute, Daejeon 34057, Korea
Department of Civil Engineering, Inha University, Incheon 22212, Korea
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
GS Caltex Corporation, Seoul 06141, Korea
Funding
Minister of Science and ICT · Ministry of Land, Infrastructure, and Transport of the Korean government
References (23)
Show source-provided references
1. A new model for TBM performance prediction in blocky rock conditions10.1016/j.tust.2014.06.004 ↗
2. Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground10.1016/j.tust.2016.01.009 ↗
3. Maidl, B., Herrenknecht, M., Maidl, U., and Wehrmeyer, G. (2013). Mechanised Shield Tunnelling, John Wiley & Sons.10.1002/9783433601051 ↗
4. Modeling and analysis of thrust force for EPB shield tunneling machine10.1016/j.autcon.2012.02.004 ↗
5. Modeling and prediction for the thrust on EPB TBMs under different geological condi-tions by considering mechanical decoupling10.1007/s11431-016-6096-0 ↗
6. Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction10.1016/j.ijrmms.2006.07.007 ↗
7. Evaluation of cutting efficiency during TBM disc cutter excavation within a Korean granitic rock using linear-cutting-machine testing and photogrammetric measurement10.1016/j.tust.2012.08.006 ↗
8. New equations for predicting the field penetration index of tunnel boring machines in frac-tured rock mass10.1007/s12517-016-2811-1 ↗
9. Actual performance analysis of a double shield TBM through sedimentary and low to medium grade metamorphic rocks of Ghomrood water conveyance tunnel project (lots 3 and 4)10.1007/s10064-020-01947-z ↗
10. State-of-the-art predictive modeling of TBM performance in changing geolog-ical conditions through gene expression programming10.1016/j.measurement.2018.05.049 ↗
11. Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs10.1016/j.tust.2016.05.009 ↗
12. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data10.1016/j.autcon.2018.03.030 ↗
13. Recurrent neural networks for real-time prediction of TBM operating parameters10.1016/j.autcon.2018.11.013 ↗
14. Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel10.1016/j.tust.2012.06.015 ↗
15. Villeneuve, M. (2017). Using Tunnel Boring Machine Penetration Tests to Quantify Performance in Hard Rock, UC Research Repository.
16. Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Son.
17. Why do we sometimes get nonsense-correlations between Time-Series?—A study in sam-pling and the nature of time-series10.2307/2341482 ↗
18. Problems of Economic Conditions, 3, 1 (1927)
19. Bruland, A. (2000). Hard Rock Tunnel Boring, Fakultet for ingeniørvitenskap og teknologi.
20. Rostami, J. (1997). Development of a Force Estimation Model for Rock Fragmentation with Disc Cutters through Theoretical Modeling and Physical Measurement of Crushed Zone Pressure. [Ph.D. Thesis, Colorado School of Mines Golden].
21. Kirkpatrick, C.D., and Dahlquist, J.A. (2010). Technical Analysis: The Complete Resource for Financial Market Technicians, FT Press.
22. Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. Selected Papers of Hirotugu Akaike, Springer.10.1007/978-1-4612-1694-0_15 ↗
23. Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling10.3141/1776-25 ↗