PREDICTION OF TBM PENETRATION RATE OF WATER CONVEYANCE TUNNELS IN IRAN USING MODERN METHODS

Authors

  • Alireza Afradi Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
  • Arash Ebrahimabadi Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
  • Tahereh Hallajian Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

DOI:

https://doi.org/10.14311/CEJ.2020.04.0040

Keywords:

Linear regression, Non-linear regression, Gene expression programming, Support vector machine

Abstract

TBM has been used extensively in civil engineering activities and plays an important role in tunnelling projects. Hence, the penetration rate of these machines plays a crucial role in the success of their application. Therefore, in order to predict the TBM penetration rate in this study in several water conveyance tunnels including the tunnels of Karaj, Ghomrood, Golab, Nosoud and Sabzkooh, four intelligence techniques including multiple linear and nonlinear regression analysis, Gene Expression Programming (GEP) method, and Support Vector Machine (SVM) were applied. The obtained values of R2 and RMSE included 0.43 and 3.08 for linear regression, 0.68 and 2.3 for nonlinear regression, 0.74 and 2.09 for GEP method and 0.97 and 0.6 for SVM method, respectively which were utilized to predict TBM penetration rate. By investigating the tunnels database, the results indicated that the SVM method had the most accurate prediction of penetration rate (in terms of R2 and RMSE) and the maximum amount of R2 and the minimum amount of RMSE among all predictive modelings. Finally, respecting the amount of R2 and RMSE, the other methods like GEP method, nonlinear regression, and linear regression are listed to have the required accuracy in predicting penetration rate.

Downloads

Download data is not yet available.

References

Bruland, A., 2000. Hard rock tunnel boring: advance rate and cutter wear. The Norwegian Institute of Technology, Trondheim, Norway. https://doi.org/10.13140/2.1.3050.3204

Sapigni, M., Berti, M., Bethaz, E., Busillo, A., Cardone, G., 2002. TBM performance estimation using rock mass classifications. International Journal of Rock Mechanics and Mining Sciences ,39(6):771–788, https://doi.org/10.1016/S1365-1609(02)00069-2

Benardos, A.G., Kaliampakos, D.C., 2004. Modelling TBM performance with artificial networks. Tunnelling and Underground Space Technology, 19:597–605, https://doi.org/10.1016/j.tust.2004.02.128

Ribacchi, R., Lembo Fazio, A., 2005. Influence of rock mass parameters on the performance of a TBM in a Gneissic Formation (Varzo Tunnel). Rock Mechanics and Rock Engineering,38(2):105–127, https://doi.org/10.1007/s00603-004-0032-5

Zhao, Z., Gong, Q.M, Zhang, Y., Zhao, J., 2007. Prediction model of tunnel boring machine performance by ensemble neural networks.Geomechanics and Geoengineering,2:123–128, https://doi.org/10.1080/17486020701377140

Gong, Q.M., Zhao, J., 2009. Development of a rock mass characteristics model for TBM penetration rate prediction. International Journal of Rock Mechanics and Mining Sciences, 46(1):8–18, https://doi.org/10.1016/j.ijrmms.2008.03.003 [7] Hamidi, J.K., Shahriar, K., Rezai, B., Rostami, J., 2010. Performance prediction of hard rock TBM using Rock Mass Rating (RMR) System. Tunnelling and Underground Space Technology,25(4):333–345, https://doi.org/10.1016/j.tust.2010.01.008

Gholamnejad, J., Tayarani, N., 2010. Application of artificial networks to the prediction of tunnel boring machine penetration rate. Mining Science and Technology,20(5):727–733, https://doi.org/10.1016/S1674-5264(09)60271-4

Yagiz, S., Karahan, H., 2011. Prediction of hard rock TBM penetration rate using particle swarm optimization International Journal of Rock Mechanics and Mining Sciences,48(3):427–433, https://doi.org/10.1016/j.ijrmms.2011.02.013

Cheraghi seifabad, M., 2013. Engineering geology of ghomroud tunnel with emphasis on water inflow-a case study, Electronic Journal of Geotechnical Engineering, 18:45-54.

Sadeghi, M. Narimani, R., 2016. Using In situ penetration tests to improve the performance of TBM in long tunnels-Case study of Golab water transfer Tunnel, Conference Paper.

Hassanpour, J.,Rostami, J.,Khamehchiyan,M.,Bruland, A.,Tavakoli, H. R., 2010. TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel. Rock Mechanics and Rock Engineering, 43(4):427–445 https://doi.org/10.1007/s00603-009-0060-2.

Fahimdanesh, Sh., HafeziMoghaddas, N., 2014. Tunneling machine selection model based on geologic parameters using ahp method. Indian Journal of Scientific Research, 9 (1): 137-145. https://doi.org/10.5958/2250-0138.2014.00022.4

Talary, G.A., Rahimi, E., Abbasi, M., 2016. Seismotectonics and earthquake risk analysis in the area of Sabzkooh-Choqakhor water conveyance tunnel. Thirty-fourth gathering and second international congress of geosciences.

Wang, S., Zheng, L. and Dai, J., (2014). Empirical Likelihood Diagnosis of Modal Linear Regression Models. Journal of Applied Mathematics and Physics, 2(10): 948-952, https://doi.org/10.4236/jamp.2014.210107.

Brimacombe, M., 2016. Local Curvature and Centering Effects in Nonlinear Regression Models. Open Journal of Statistics, 6(1): 76-84. https://doi.org/10.4236/ojs.2016.61010.

Ferreira, C., 2002. Gene Expression Programming in Problem Solving. In: Roy R., Köppen M., Ovaska S., Furuhashi T., Hoffmann F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_54

Vapnik, V., 1995. The nature of statistical learning theory, Springer, New York, https://doi.org/10.1007/978-1-4757-3264-1.

Vapnik, V., (1998). Statistical learning theory, Wiley, New York.

Afradi, A., Ebrahimabadi, A., Hallajian, T., 2019. Prediction of the Penetration Rate and Number of Consumed Disc Cutters of Tunnel Boring Machines (TBMs) Using Artificial Neural Network (ANN) and Support Vector Machine (SVM)-Case Study: Beheshtabad Water Conveyance Tunnel in Iran. Asian Journal of Water, Environment and Pollution, 16 (1): 49-57. https://doi.org/10.3233/AJW190006.

Afradi, A., Ebrahimabadi, A., Hallajian, T., 2020. Prediction of TBM Penetration Rate Using Support Vector Machine. Geosaberes, 11:467 - 479. https://doi.org/10.26895/geosaberes.v11i0.1048

Afradi, A., Ebrahimabadi, A., Hallajian, T., 2020. Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel. Mining of Mineral Deposits,14(2):75-84. https://doi.org/10.33271/mining14.02.075

Downloads

Published

2020-12-31

How to Cite

Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2020). PREDICTION OF TBM PENETRATION RATE OF WATER CONVEYANCE TUNNELS IN IRAN USING MODERN METHODS. Stavební Obzor - Civil Engineering Journal, 29(4). https://doi.org/10.14311/CEJ.2020.04.0040

Issue

Vol. 29 No. 4 (2020)

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).