TBM performance estimation using rock mass classifications |
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| Authors: | M Sapigni M Berti E Bethaz A Busillo G Cardone |
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| Affiliation: | 1. Enelpower S.p.A., Via Torino 16, 30172 Venezia-Mestre, Italy;2. Dipartimento di Scienze della Terra e Geologico-Ambientali, Università di Bologna, Via Zambonii 67, 40126 Bologna, Italy;3. Enelpower S.p.A., Ciso Regina Margherita 267, 10143 Torino, Italy;4. SELI S.p.A., Viale America 93, 00144, Roma, Italy;5. SOGIN S.p.A., Via Torino 6, 00184, Italy;1. Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory for Rock Mechanics (LMR), 1015 Lausanne, Switzerland;2. Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China;1. CH2M, London, United Kingdom;2. Dept. of Geotechnical Engineering, School of Civil Engineering, National Technical University of Athens, Greece;1. Institute of Geotechnical Engineering, University of Stuttgart, Stuttgart, Germany;2. Earth Mechanic Institute, Department of Mining Engineering, Colorado School of Mines, USA |
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| Abstract: | Three tunnels for hydraulic purposes were excavated by tunnel-boring machines (TBM) in mostly hard metamorphic rocks in Northern Italy. A total of 14 km of tunnel was surveyed almost continually, yielding over 700 sets of data featuring rock mass characteristics and TBM performance. The empirical relations between rock mass rating and penetration rate clearly show that TBM performance reaches a maximum in the rock mass rating (RMR) range 40–70 while slower penetration is experienced in both too bad and too good rock masses. However, as different rocks gives different penetrations for the same RMR, the use of Bieniawski's classification for predictive purpose is only possible provided one uses a normalized RMR index with reference to the basic factors affecting TBM tunneling. Comparison of actual penetrations with those predicted by the Innaurato and Barton models shows poor agreement, thus highlighting the difficulties involved in TBM performance prediction. |
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