Document Type: Original Research Paper

Author

Department of Mining and Metallurgy Engineering, Amirkabir University of technology, Tehran, Iran

Abstract

In every tunnel boring machine (TBM) tunneling project, there is an initial low production phase so-called the Learning Phase Period (LPP), in which low utilization is experienced and the operational parameters are adjusted to match the working conditions. LPP can be crucial in scheduling and evaluating the final project time and cost, especially for short tunnels for which it may constitute a major percentage of the total project completion time. The contractors are required to have a better understanding of the initial phase of a project to provide better estimates in the bidding documents. While evaluating and shortening of this phase of low production is important for increasing the productivity and daily advance rate of the machine, there has been limited a direct study and assessment of this period. In this work, we discuss the parameters impacting LPP, and introduce a new methodology for its evaluation. In this regard, an algorithm is introduced for estimation of the approximate extent of LPP based on some TBM tunneling case histories. On the basis of many statistical analyses conducted on the actual data and application of two different shapes of linear and polynomial for the description of LPP, a linear function is proposed for estimation of the learning phase parameters. The major parameters of this function are the learning conditions’ rating and the proportion of LPP to tunnel diameter (X1/D). Analysis of the correlation between these two parameters show a very good coefficient of determination (R2 = 92%). This function can be used for the evaluation of TBM advance rates in LPP and for adjusting the TBM utilization factor in the initial stages of a TBM tunneling project. The learning phase can affect the overall utilization rate and completion time of the tunnels, especially when their lengths are around a couple of kilometers. A true understanding of the LPP characteristics can help the contractors to come up with a more accurate bidding time and cost evaluation, and may also benefit the clients to arrange a better schedule for the final project delivery to the public.

Keywords

[1]. Alvarez Grima, M. and P.A. (2000). Bruines and P.N.W. Verhoef, Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunnell. Undergr. Space Technol. 15 (3): 259–269.

[2]. Farrokh, E. (2013). Study of utilization factor and advance rate of hard rock TBMs. PhD dissertation, The Pennsylvania State University.

[3]. Farrokh, E. (2020). A study of various models used in the estimation of advance rates for hard rock TBMs, Tunneling and Underground Space Technology, Volume 97.

[4]. Gong, Q.M. (2005). Development of a rock mass characteristics model for TBM penetration rate prediction, PhD thesis, Nanyang Technology University.

[5]. Hassanpour J., Rostami J. and Zhao J. (2011). A new hard rock TBM performance prediction model for project planning, Tunneling and Underground Space Technology, 26 (5): 595-603.

[6]. Hassanpour, J. (2009). Investigation of the effect of engineering geological parameters on TBM performance and modifications to existing prediction models. Ph.D. Thesis, Tarbiat Modares University, Tehran, Iran.

[7]. Hassanpour, J., Rostami, J., Khamehchiyan, M., Bruland, A. and Tavakoli, H.R. (2010). TBM performance analysis in pyroclastic rocks: a case history of Karaj water conveyance tunnel, Rock Mech. Rock Eng, 43: 427–445.

[8]. Khademi Hamidi, J., Shahriar, K., Rezai, B. and Bejari, H. (2009). Application of Fuzzy Set Theory to Rock Engineering Classification Systems: An Illustration of the Rock Mass Excavability Index, Rock Mechanics and Rock Engineering, 43 (3): 335-350.

[9]. Ramezanzadeh, A., Rostami, J. and Kastner, R. (2005). Influence of Rock Mass Properties on Performance of Hard Rock TBMs. RETC, June 27-29, Seattle, Washington, USA.

[10]. Ribacchi, R, Lembo-Fazio, A. (2005). Influence of rock mass parameters on the performance of a TBM in a Gneissic formation (Varzo tunnel). Rock Mech Rock Eng., 38 (2):105–127

[11]. Rostami, J. (2016). Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground, Tunneling and Underground Space Technology, 57: 173-182.

[14]. Abd Al-Jalil, Y. (1998). Analysis of performance of tunnel boring machine-based systems, Ph.D. Thesis, The University of Texas at Austin.

[15]. Bieniawski, Z.T., Celada, B., Galera, J.M. and Tardáguila, I. (2008). New applications of the excavability index for selection of TBM types and predicting their performance, ITA World Tunneling Congress, Agra, India.

[16]. Khademi Hamidi, J., Shahriar, K., Rezai, B. and Bejari, H. (2009). Application of Fuzzy Set Theory to Rock Engineering Classification Systems: An Illustration of the Rock Mass Excavability Index, Rock Mechanics and Rock Engineering, 43 (3): 335-350.

[17]. Laughton, C. (1998). Evaluation and Prediction of Tunnel Boring Machine Performance in Variable Rock Masses. Ph.D. Thesis, the University of Texas at Austin.

[18]. Ahuja, H.N. and Nandakumar, V. (1985). Simulation model to forecast project completion time. Journal of Construction Engineering and Management, ASCE, 111 (4): 325- 342.

[19]. Farrokh, E., Rostami, J. and Laughton, Ch. (2011). Analysis of Unit Supporting Time and Support Installation Time for Open TBMs, Rock Mech Rock Eng 44:431–445.

[20]. Gratias, R., Allan, C. and Willis, D. (2014). The next level, why deeper is better for TBMs in mining, North American Tunneling, 118-125.

[21]. Maidl, B., Schmid, L., Ritz, W. and Herrenknecht, M. (2008). Hard rock tunnel boring machines.

[22]. Schmid, L. (2004). The development of the methodology of the shield tunneling in Switzerland. Geotechnique 27(2):193–200.

[23]. Wais, A., Wachter, R. (2009). Predicting the Learning Curve in TBM Tunneling. In Book "Tunnel Construction - Contributions from Research and Practice," Germany, Ch. 15, 143-154.

[24]. WBI-PRINT 6. (2007). Stability analysis and design for mechanized tunneling, Prof. Dr.-Ing. W. Wittke Consulting Engineers for Foundation Engineering and Construction in Rock Ltd.

[25]. Rostami, J., Farrokh, E., Laughton, Ch., Eslambolchi, S.S. (2014). Advance rate simulation for hard rock TBMs, KSCE Journal of Civil Engineering, 18(3): 837-852.

[26]. Brockway, J. (2016). EPB machines and interventions, Tunneling Short Course, Boulder CO, September 12 - 15, 2016.

[27]. Bevis, F.W., Finniear, C. and Towill, D.R. (1970). Prediction of operator performance during learning of repetitive tasks. International Journal of Production Research, 8, 293–305.

[28]. Gutjahr, W.J., Katzensteiner, S., Reiter, P., Stummer, C. and Denk, M. (2008). Competence-driven project portfolio selection, scheduling and staff assignment. Central European Journal of Operations Research, 16, 281–306.

[29]. Jin, H., Thomas, B. W. and Hewit, M. (2016). Integer programming techniques for makespan minimizing workforce assignment models that recognize human learning, Computers & Industrial Engineering.

[30]. Nembhard, D.A. and Norman, B.A. (2007). Cross Training in Production Systems with Human Learning and Forgetting. In D. A. Nembhard (Ed.), Workforce Cross Training chapter 4. (pp. 111–129). Boca Raton, FL, USA: CRC Press.

[31]. Sayin, S. and Karabati, S. (2007). Assigning cross-trained workers to depart ments: A two-stage optimization model to maximize utility and skill improvement. European Journal of Operational Research, 176, 1643–1658.

[32]. Wais, A. (2002). Possibilities and Limitations of Predicting Tunnel Boring Machine Advance Rates for the Assesment of Tunnel Project Costs, Master Thesis, University of Stuttgart, Institute of Hydraulic Engineering.

[33]. Farrokh, E., Rostami, J. and Laughton, Ch. (2012). Study of various models for estimation of penetration rate of hard rock TBMs, Tunnelling and Underground Space Technology, 30: 110-123.

[34]. Sharp, W. and Ozdemir, L. (1991). Computer Modeling for TBM Performance Prediction and Optimization. Proceedings of the International Symposium on Mine Mechanization and Automation, CSM/USBM, 1(4), 57–66.

[35]. US Army Corps of Engineers. (1997). Engineering and Design Tunnels and Shafts in Rock, Appendix C: Tunnel Boring Machine Performance-Concepts and Performance Prediction, Washington, D.C.

[36]. 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, Colorado, USA.

[37]. ITA/AITES WG. 14. (2000). Recommendations and Guidelines for Tunnel Boring Machine, Lausanne.

[38]. Bruland, A. (1998). Hard Rock Tunnel Boring. Ph.D. Thesis, Norwegian University of Science and Technology, Trondheim.

[39]. Barton, N. (2000). TBM tunneling in jointed and faulted rock. A.A. Balkema Publishers, Rotterdam.

[40]. Bieniawski, Z. T., Celada, B., Galera, J.M. and Tardáguila, I. (2009). Prediction of cutter wear using RME, ITA-AITES World Tunnel Congress, Budapest.

[41]. Bieniawski, Z.T., Celada B. and Galera, JM. (2007). TBM excavability: prediction and machine–rock interaction. In: Proceedings of the Rapid Excavation and Tunneling Conference (RETC), Toronto, Canada, 1118–1130.

[42]. Bieniawski, Z.T., Celada, B. and Galera Fernandez, J.M. (2007). Predicting TBM Excavability-Part I. Tunnels & Tunneling International, p. 25.

[43]. Bieniawski, Z.T., Celada, B., Galera, J.M. and Álvares, M. (2006). Rock Mass Excavability (RME) index: a New Way to Selecting the Optimum Tunnel Construction Method, Proc. ITA World Tunneling Congress, Seoul.

[44]. Bieniawski, Z.T. and Grandori R. (2007). Predicting TBM Excavability-Part II. Tunnels & Tunneling International, 15-18.

[45]. Fallahpour, M., Karami, M. and Sherizadeh, T. (2019). Double-shield TBM performance analysis in clay formation: A case study in Iran, WTC, Italy, 795-804.