Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis

Modeling Earth Systems and Environment, Feb 2018

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.

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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)


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A. Jamshidi. Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis, Modeling Earth Systems and Environment, 2018, pp. 383-394, Volume 4, Issue 1, DOI: 10.1007/s40808-018-0432-2