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Examining Feasibility of Developing a Rock Mass Classification for Hard Rock TBM Application Using Non-linear Regression, Regression Tree and Generic Programming

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Abstract

Geotechnical and geological parameters have the greatest impact on the performance of hard rock tunnel boring machines (TBMs). This includes the rock and rock mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use rock mass classification systems for improving the accuracy of the machine performance prediction. Rock mass classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), Classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new rock mass classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a rock mass classification system that can be used for TBM applications.

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Acknowledgements

The authors wish to extend their sincere thanks to Dr. Prasnna Jain and Prof. T. N. Singh for sharing their database of TBM field performance for this study. Also, the authors express their appreciation to Mr. Ehsan Sharafian for his assistance and comments on this study.

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Authors and Affiliations

  1. Institute of Geotechnical Engineering, University of Stuttgart, Pfaffenwaldring 35, 70569, Stuttgart, Germany

    A. Salimi & C. Moormann

  2. Earth Mechanics Institute, Department of Mining Engineering, Colorado School of Mines, 1600 Illinois St., Golden, CO, 80401, USA

    J. Rostami

  3. School of Geology, College of Science, University of Tehran, Tehran, Iran

    J. Hassanpour

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  1. A. Salimi
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  2. J. Rostami
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  3. C. Moormann
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  4. J. Hassanpour
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Correspondence to A. Salimi.

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Salimi, A., Rostami, J., Moormann, C. et al. Examining Feasibility of Developing a Rock Mass Classification for Hard Rock TBM Application Using Non-linear Regression, Regression Tree and Generic Programming. Geotech Geol Eng 36, 1145–1159 (2018). https://doi.org/10.1007/s10706-017-0380-z

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