Development of a new model for estimating TBM penetration rate in rock mass

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School of Mining and Geosciences
Development of a new model for estimating TBM
penetration rate in rock mass
Supervisor: Professor Saffet Yagiz
2nd year PhD student: Aitolkyn Yazitova

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OUTLINE
Research description
Methodology
Research plan
References
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Research description
The rock breakage method employs mechanized excavation, which uses mechanical drilling and cutting instruments
to entirely extract the rock from the excavation face.
There are various different advantages over the mechanical excavation method , including:
1)increased production rate,
2)increased safety,
3)consistent product size,
4)blast vibration destruction,
5)improved ventilation,
6)more automation,
7)less labor demanding work, and
8) greater control in underground construction.
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Research description
Since TBM is an important machine in tunneling excavation, it requires an accurate performance prediction
considering all influential parameters properly.
In TBM performance estimation it is necessary to consider the influential points that should be examined to predict
the rate of penetration of machine properly. Since the advancement of TBM depends on various parameters, a rock
mass property has a significant role in properties of intact rock substance and cracks in rock.
Another important parameter which influenced on TBM advancement are machine specifications composed of
torque, thrust and machine power
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Purpose of the Research
The main purpose of this research is to develop a new model to estimate TBM penetration rate.
In order to reach the aim of this research, the essential research activities are distributed through five main stages
as the following:
- Produce a database of TBM performance in different conditions of the rock;
- Determine the influence of intact rock properties on TBM penetration rate;
- Evaluate the influence of rock mass properties on TBM penetration rate;
- Estimate the impact of machine specifications on TBM penetration rate;
- Develop a new model for estimating TBM penetration rate using statistical analysis, different modellings and
machine learning methods based on obtained dataset.
All five stages provided before are determinative for the accomplishment of the research purpose. Consequently,
the influence of each parameters on TBM penetration rate will be precisely investigated whether it is intact rock
or rock mass properties and machine specifications.
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Research Significance
There are many papers have been published to estimate the TBM penetration rate; but most of those models
that are based on single project could not be accepted as general model to be used for worldwide.
The proposed model will be based on the data of tunnel projects excavating in different countries
As a result of the research a new model for estimating TBM penetration rate will be provided.
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Methodology
1. Produce a database of TBM performance in different conditions of the rock
There are different projects where the existing databases are required to be reviewed and expanded. Those
databases consist of information about rock mass properties, machine specifications, operational parameters and
geologic information of rock properties. The related database including rock and machine will be collected from
the following projects:
Queens Tunnel;
Manapouri Tunnel in New Zealand;
Karaj, Zagros Tunnels in Iran;
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MEMethodologyTHODOLOGY
2. Determine the influence of intact rock properties on TBM penetration rate
TBM performance prediction model needs reliable input parameters including intact and mass rock properties and machine
specifications.
Intact rock properties including strength, density, brittleness, porosity and rock abrasion have an effect on the performance of
the machines.
However, find out the main parameters having effect on the performance is not easy due to complexity of rock mass
environment.
In order to reach it, relevant literature review and data obtained from the literature will be examined. Later on, available tunnel
site and institute will be visited to collect data and to make some collaboration. Also available case studies and project reports
are going to be revised to obtain data to use for the purpose. After that, each parameter will be evaluated individually to find
out effect of own weight on machine performance
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Methodology
3. Evaluate the influence of rock mass properties on TBM penetration rate
Rock mass properties including faults, foliation, bedding, fracture spacing, weakness, rock mass quality (RQD) are another main
property group having effect on TBM performance. Most of the models in present do not cover every rock mass properties, since each
project is unique and it is not easy to collect all these data from tunnel site.
In this stage, most of the data will be collected from either literature or current tunneling project where TBM is used for excavation.
This step will also be considered individually to investigate the weight of each rock mass parameter on machine performance.
After evaluating the available parameters in the dataset using statistical methods, acceptable input parameters will be selected for
development of the model.
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Methodology
4. Estimate the impact of machine specifications on TBM penetration rate;
TBM specification including cutter hear thrust, individual cutter load, revolutions per minutes (RPM), power, torque, should
also be considered to estimate boreability of rocks.
Some empirical models were developed to estimate the TBM performance as a function of both rock properties and machine
specification (i.e., cutter load) (Gong and Zhao, 2009; Yagiz, 2017); however these models resulted from only one case with
limited data and cannot be used in worldwide.
Therefore, more than several cases will be examined to obtain the reliable database and develop the model.
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REFERENCES
5. Develop a new model for estimating TBM penetration rate
To develop database, several institutes will be visited and collaboration will be constituted.
Also, available tunnel sites and companies will be visited not only in Kazakhstan but also other countries having mechanized
tunneling projects.
After establishing the database including intact and mass rock properties and also machine specifications, the data set will be used for
development of a model to estimate TBM performance.
Firstly, influence of each available variable on an output of purposed model will be examined to select the proper inputs for the
model. After selecting possible and acceptable variables as inputs for the model, possible statistical analysis and model softwares
(such as SPSS and MAT Lab) will be used for development of the new model.
A new model will be developed based on empirical equations
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Research plan
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References
Armaghani, D.J., Koopialipoor, M., Marto, A. and Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock
Mechanics and Geotechnical Engineering, 11, 779-789
Barton, N. (2000). TBM tunnelling in jointed and faulted rock, Balkema, Brookfield, 173 pp
Bruland, A. (1998): Hard Rock Tunnel Boring; Drillability; Test Methods, Vol 8-10. PhD thesis, Norwegian University of Science and Technology (NTNU), Trondheim, 1998
Bruland, A. (2000). Hard Rock Tunnel Boring: Vol 1-10. PhD thesis, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, 2000
Gehring, K. (1995). Leistungs- und Verschleißprognose im maschinellen Tunnelbau, in Felsbau Magazin, 13, 439-448
Gong, Q.M., Zhao, J. (2009) Development of a rock mass characteristics model for TBM penetration rate prediction, International Journal of Rock Mechanics and Mining Sciences,
46(1):8–18
Hansen, J.N. (2018). Comparison of existing performance prediction models for hard rock tunnel boring based on data collected at the Line Project. PhD, Thesis, Department of
Geoscience and Petroleum, Norwegian University of Science and Technology, 188 pp
Hassanpour, J., Rostami, J. & Zhao, J. (2011). A new hard rock TBM performance prediction model for project planning, in Tunnelling and Underground Space Tech, 26, 595-603
Lislerud, A. (1988), Hard rock tunnel boring: prognosis and costs. Tunnel Underground Space Tech, 3, 9-17
Macias, F. J. (2016). Hard rock tunnel boring: performance predictions and cutter life assessment. PhD, Thesis, Norwegian University of Science and Technology, Faculty of Engineering
Science and Tech, Department of Civil and Transport Engineering, 322 pp
Ozdemir, L. (1977). Development of theoretical equations for predicting tunnel borability. PhD, Thesis, T-1969, Colorado School of Mines, Golden, Co, USA, 1977
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. PhD, Thesis Dissertation, Department of Mining Engineering, Colorado School of Mines, 384pp
Wilfing, L. (2016). The Influence of Geotechnical Parameters on Penetration Prediction in TBM Tunneling in Hard Rock. PhD, Thesis, Technische Univerität München, 191 pp
Yagiz S. (2009) Assessment of brittleness using rock strength and density with punch penetration test, Tunneling and Underground Space Technology, 24, 66-74
Yagiz, S. (2002). Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock
tunneling machines. PhD, Thesis, T-5605, Colorado School of Mines, USA, 289pp
Yagiz, S. (2017) New equations for predicting the field penetration index of tunnel boring machines in fractured rock mass. Arabian Journal of Geosciences, 10;33
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