Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Open access

Abstract

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Abu Zeid M.M., 1991. Lithostratigraphy and framework of sedimentation of the subsurface Paleogene succession in northern Qatar. Arabian Gulf. N. Jb. Geol. Paliaiont. Mh.: 191-204.

Avunduk E. Copur H., Bilgin N., Balci C., Tumac D., 2012. Effect of Some Geotechnical Properties on TBM Performance.

Eurock 2012 ISRM International Symposium, 28-30 May 2012, Stockholm, Sweden.

Barton N., 2000. TBM Tunneling In Jointed and Faulted Rock. Balkema, Rotterdam, p. 173.

Benato A., Oreste P., 2015. Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics. Int. J. Rock Mech. Min. Sci. 74, 119-127.

Bieniawski Z.T., Celada B., Galera J.M., 2007. TBM excavability: prediction and machine-rock interaction. In: Proceedings, Rapid Excavation and Tunneling Conference, pp. 1118-1130.

Breiman L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees. Chapman & Hall/CRC.

Bruland A., 1998. Prediction model for performance and costs. Norwegian TBM Tunnelling, Publication No. 11. Norwegian Tunnelling Society.

Bruland A., 2014. The NTNU prediction model for performance. Norwegian Tunnelling Technology, Publication No. 23. Norwegian Tunnelling Society.

Delisio A., Zhao J., 2014. A new model for TBM performance prediction in blocky rock conditions. Tunnel. Undergr. Space Technol. 43, 440-452.

Gong Q.M., Zhao J., 2009. Development of a rock mass characteristics model for TBM penetration rate prediction. Int. J. Rock Mech. Mining. Sci. 46 (1), 8-18.

Hamidi J.K., Shahriar K., Rezai B., Rostami J., 2010. Performance prediction of hard rock TBM using rock mass rating (RMR) system. Tunn.

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 (KWCT). J. Rock Mech. Rock Eng. 4, 427-445.

Hassanpour J., Rostami J., Zhao J., 2011. A new hard rock TBM performance prediction model for project planning. Tunnel. Undergr. Space Technol. 26, 595-603.

Jain P., Naithani A.K., Singh .TN., 2014. Performance characteristics of tunnel boring machine in basalt and pyroclastic rocks of Deccan traps - a case study. Int. J. Rock Mech. Geo. Eng. 6 (1), 36-47.

LeBlanc J., 2008. A Fossil Hunting Guide to the Tertiary Formations of Qatar. Middle East. First Edition, March 2008.

Maher J.A., 2017. Machine Learning Approach to Predicting and Maximizing Penetration Rates in EPB TBMs. https://src.acm.org, access 2017.

NTNU, 1998. Report 1B-98 Hard Rock Tunnel Boring - Advance Rate and Cutter Wear. Department of Civil and Transport Engineering, Trondheim, Norway.

Palmstrom A., 1995. RMi, a rock mass characterization system for rock engineering purposes. Chapter 7, Ph.D. thesis, Oslo University, Norway.

Pathak A.K., Stypulkowski J.B, Bernardeau F.G., 2015. Super-vision of Engineering Geological Activities during Construction of Abu Hamour Surface and Ground Water Drainage Tunnel Phase-1 Doha, Qatar. [In:] International

Conference on Engineering Geology in New Millennium at IIT, New Delhi 27-29 Oct 2015. Special Publication, J. of .EG. October 2015, pp. 467-486.

Ramezanzadeh A., 2005. Performance analysis and Development of new models for performance prediction of hard rock TBMs in rock mass. Ph.D. thesis, INSA, Lyon, France.

Rostami J., 1997. Development of a force estimation model for rock fragmentation with disccutters through theoretical modeling and physical measurement of crushed zone pressure, Ph.D. thesis, Colorado School of Mines, Golden, Colorado, USA, p. 249.

Salimi A., Rostami J., Moormann C., Delisio A., 2016. Application of nonlinear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunn Undergr Space.

Stypulkowski J.B., Siyam A.A.F.M., Bernardeau F.G., Al Kuwari N.G., 2013. Abu Hamour Drainage Tunnel. First TBM Mined Tunnel in Doha, Qatar. [In] The First Arabian Tunnelling Conference & Exhibition, Dubai, pp. 300-314.

Stypulkowski J.B., Bernardeau F.G., Jakubowski J., 2017. Abu Hamour Tunnel Phase I the First TBM Tunnel in Qatar: The art of tunneling in a new world. [In:] Engineering Challenges for Sustainable Underground Use. GeoMEast 2017. Springer, Cham.

Tajduś A., Cała M., Tajduś K., 2012. Geomechanics in underground construction : design and construction of tunnels (in Polish). AGH, Kraków.

Tibco Software Inc., 2017. Statistica (data analysis software system), version 13.

Yagiz S., 2008. Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn. Undergr. Space Technol. 23 (3), 326-339.

Archives of Mining Sciences

The Journal of Committee of Mining of Polish Academy of Sciences

Journal Information


IMPACT FACTOR 2016: 0.550
5-year IMPACT FACTOR: 0.610

CiteScore 2016: 0.72

SCImago Journal Rank (SJR) 2016: 0.320
Source Normalized Impact per Paper (SNIP) 2016: 0.950

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 200 200 13
PDF Downloads 104 104 4