Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis
Modeling Earth Systems and Environment
Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis
A. Jamshidi 0
0 Department of Geology, Faculty of Basic Sciences, Lorestan University , Khorramabad , Islamic Republic of Iran
One of the most important aspects in the excavation of tunnels with a Tunnel Boring Machine (TBM) is the reliable prediction of its penetration rate. This affects the planning and other decision making on the organization of the construction site of the tunneling project, and, therefore, total costs. In this study, raw data obtained from the experimental works of different researchers were used to establish the new statistical models for prediction of rock TBM penetration rate from brittleness indexes, B1, B2, and B3. For this, correlation between the TBM penetration rate with brittleness indexes statistically was investigated using multiple regression analyses. In these analyses, the TBM penetration rate was considered to be the dependent variable, which is dependent on the independent variables of the brittleness indexes. The validity of the predictive models was validated by statistical tests. The results showed that statistical models are in good accuracy for prediction of TBM penetration rate, and thus making a rapid assessment of the TBM performance.
TBM; Penetration rate; Brittleness indexes; Statistical models
Introduction
Tunnel Boring Machine (TBM) performance can be
measured in terms of the penetration rate. Three main parameters,
machine design related parameters, geological conditions
and geotechnical properties of rocks along the tunnel
alignment affect the TBM penetration rate
(Yagiz and Karahan
2015)
. Although the parameters of drilling machine
equipment can be controlled, change to the geological conditions
and geotechnical properties of rocks cannot be.
Penetration rate (PR) which is also referred to as Rate of
penetration (ROP), and often expressed in m/h and refers to
the linear footage of excavation per unit time, when machine
engages the ground and is in production
(Rostami 2016)
.
Having some prior knowledge of the TBM penetration rate
in rock excavation projects is very helpful to plan
construction time and to control cost. Penetration rate prediction is
considered as a complex and difficult work because of the
interaction between rock mass and TBM, but a meaningful
task because it is significant for time planning, cost control
and choosing the excavation method (Yagiz 2002).
The development of various predictive models of the
TBM penetration rate has been main objective and is still
under progress for many years
(Graham 1976; Hughes 1986;
Rostami and Ozdemir 1993; Kahraman et al. 2003; Gong
and Zhao 2009; Yagiz and Karahan 2011, 2015; Farrokh
et al. 2012; Coffi Adoko et al. 2017)
. Existing prediction
approaches include theoretical and empirical models
(Barton
2000; Sapigni et al. 2002)
, simple and multiple regression
analyses
(Delisio and Zhao 2014; Farrokh et al. 2012;
Khademi Hamidi et al. 2010)
, artificial intelligence techniques
such as artificial neural networks
(Benardos and
Kaliampakos 2004; Salimi et al. 2016; Shao et al. 2013)
, fuzzy
inference systems
(Acaroglu et al. 2008; Alvarez Grima
et al. 2000; Yazdani-Chamzini et al. 2013)
, support vector
regression analysis
(Mahdevari et al. 2014)
, particle swarm
optimization
(Yagiz and Karahan 2011)
and other advanced
optimization algorithms
(Yagiz and Karahan 2015)
. In
general, these models are established on the basis of experience
gained and the data compiled from the past tunneling
projects in order to derive the complex and non- linear
relationship between the TBM penetration rate and the influencing
rock mass parameters. Table 1 is a summary of advantages
and disadvantages of these modelling concepts.
Advantages
Disadvantages
The penetration rate may depend on various rock
properties including strength, brittleness, distance between plane
of weakness, orientation of discontinuities and also TBM
specifications such as torque, thrust, RPM and disc diameter
etc. So, the problem is highly complicated to be solved with
simple regression approach
(Yagiz and Karahan 2015)
. On
the basis of the geological conditions and geotechnical
properties of rocks, there are two groups of statistical models for
prediction of penetration rate. The first group of models is
based on prediction of TBM performance by using a single
intact rock parameter. For many researchers, uniaxial
compressive and tensile strengths (UCS and BTS) are the most
widely used properties for rock drillability (Graham 1976;
Farmer and Glossop 1980; Hughes 1986; Karpuz et al. 1990;
Akcin et al. 1994; Bilgin et al. 1996; Huang and Wang 1997;
Kahraman 1999; Kahraman et al. 2003; Akun and Karpuz
2005; Tanaino 2005). Moreover, many different rock
parameters, such as point load index, P-wave velocity, porosity,
quartz content, Schmidt hammer number, Shore hardness,
cone inden (...truncated)