Predicting tunnel boring machine penetration rate utilizing geomechanical properties

Predicting the penetration rate plays a key role in tunnel projects using a tunnel-boring machine (TBM). Developing accurate prediction models can improve project management, save budget, and time in tunnel projects. In this research, the Gelas water project data was used to obtain new statistical m...

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Bibliographic Details
Published in:Quarterly journal of engineering geology and hydrogeology 2022-11, Vol.55 (4), p.1
Main Authors: Karrari, Seyed Sajjad, Heidari, Mojtaba, Hamidi, Jafar Khademi, Khaleghi-Esfahani, Mohammad, Teshnizi, Ebrahim Sharifi
Format: Article
Language:English
Subjects:
Asia
Azerbaijan region
Boring machines
Brazilian tests
Classification systems
Compressive strength
crystalline rocks
dams
elastic constants
Engineering geology
fractures
Gelas Tunnel
Geomechanics
Geotechnics
Iran
Landslides
Lavin River
Mathematical models
Mechanical properties
Metamorphic rocks
Middle East
Modelling
Modulus of elasticity
Naghadeh Plain
Orumich Iran
Parameters
Penetration
prediction
Prediction models
Project management
Rock mass rating
rock masses
Rock mechanics
Statistical analysis
Statistical methods
Statistical models
Tensile strength
Toughness
tunnel boring machines
Tunnel construction
Tunnels
uniaxial tests
Urmia Lake
Weathering
Young's modulus
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Summary:Predicting the penetration rate plays a key role in tunnel projects using a tunnel-boring machine (TBM). Developing accurate prediction models can improve project management, save budget, and time in tunnel projects. In this research, the Gelas water project data was used to obtain new statistical models for predicting TBM penetration rate per revolution (PRev) utilizing the toughness index (Ti), modulus ratio (E/UCS), and joint parameters (JP). The relationship between various geomechanical properties and rock classification systems, including uniaxial compressive strength, Brazilian tensile strength, Young's modulus, joint parameter, toughness index, rock quality designation, rock mass rating, geological strength index, rock mass quality, and rock mass index analyzed and considered on TBM performance in sedimentary, igneous, and metamorphic rocks. The statistical analysis clearly shows that Ti revealed a significant correlation with actual PRev (R2=0.75). Additionally, the PRev was computed using Ti, and JP showed good agreement with the coefficient of determination (R2), i.e., 0.79. Results indicated that the Ti decreased by increasing the modulus ratio, so the PRev increased. This model can be used easily since it provides a straightforward predictive model using the multi-parameter model.
ISSN:1470-9236
2041-4803
DOI:10.1144/qjegh2021-126