Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground |
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| Affiliation: | 1. Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China;2. CNNC Beijing Research Institute of Uranium Geology, Beijing 100029, China;3. Department of Civil Engineering, Monash University, Clayton, VIC 3800, Australia;1. Pamukkale University, Engineering Faculty, Geological Engineering Department, Denizli, Turkey;2. Pamukkale University, Engineering Faculty, Civil Engineering Department, Denizli, Turkey;1. Ecole Polytechnique Fédérale de Lausanne (EPFL), School of Architecture, Civil and Environmental Engineering, Laboratory of Rock Mechanics (LMR), Lausanne, Switzerland;2. Department of Civil Engineering, Monash University, Melbourne, Victoria 3800, Australia;1. University of Tehran, College of Science, Tehran, Iran;2. Pennsylvania State University, University Park, USA;3. Ferdowsi University of Mashhad (FUM), Mashhad, Iran;4. Monash University, Civil Engineering Department, Melbourne, Australia;5. Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland;1. Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan, Hubei 430072, China;2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China;3. Key Laboratory of Urban Safety and Disaster Engineering of Ministry of Education, Beijing University of Technology, Beijing 100124, China;4. Hebei Key Laboratory of Structural Health Monitoring and Control, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China |
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| Abstract: | Performance prediction of TBMs is an essential part of project scheduling and cost estimation. This process involves a good understanding of the complexities in the site geology, machine specification, and site management. Various approaches have been used over the years to estimate TBM performance in a given ground condition, many of them were successful and within an acceptable range, while some missing the actual machine performance by a notable margin. Experience shows that the best approach for TBM performance prediction is to use various models to examine the range of estimated machine penetration and advance rates and choose a rate that best represents the working conditions that is closest to the setting of the model used for the estimation. This allows the engineers to avoid surprises and to identify the parameters that could dominate machine performance in each case. This paper reviews the existing models for performance prediction of TBMs and some of the ongoing research on developing better models for improved accuracy of performance estimate and increasing TBM utilization. |
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| Keywords: | Performance prediction Rate of penetration (ROP) Advance rate (AR) Tunnel Boring Machine (TBM) Rock tunnelling |
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