TBM performance prediction in basalt and pyroclastic rocks of Deccan traps, a case study of Maroshi-Ruparel water supply tunnel

Rock Mechanics and Rock Engineering: From the Past to the Future Ulusay et al. (Eds) 2016 Taylor & Francis Group, London, ISBN 978-1-138-03265-1 TBM performance prediction in basalt and pyroclastic rocks of Deccan traps, a case study of Maroshi-Ruparel water supply tunnel A. Salimi Institute of Geotechnical Engineering, University of Stuttgart, Stuttgart, Germany J. Rostami Department of Energy & Mineral Engineering, Pennsylvania State University, USA C. Moormann Institute of Geotechnical Engineering, University of Stuttgart, Stuttgart, Germany ABSTRACT: With widespread applications of mechanized tunneling in almost all ground conditions, prediction of anticipated performance and production rate of tunnel boring machine (TBM) is essential part of planning, cost estimation, and selection of proper machine specification to achieve efficient and safe operation. Penetration rate is a principal measure of TBM performance and is used to evaluate the feasibility of using the mechanized tunneling. This paper will review machine performance of a hard rock TBM in 12.24 km long tunnel between Maroshi and Ruparel College which is being constructed to improve the water supply system of Greater Mumbai, India. Analysis of field performance data has been used to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel between Maroshi and Vakola indicate a strong relationships between geomechanical parameters and TBM performance parameters in this particular project. A site specific empirical model is introduced to estimate TBM performance in this project, which could show the trends and anticipated variations in machine performance as a function of ground conditions and common rock mass properties in similar conditions. 1 INTRODUCTION Hard rock tunnel boring has become more or less the standard method of tunneling for tunnels of various sizes with lengths over 1.5 2 km. Estimating the performance of TBM is a vital part of tunnel design and selection of the most appropriate excavation machine. During the past three decades, numerous TBM performance prediction models for evaluation of TBM have been proposed. In brief, all the TBM performance prediction models can be divided into two distinguished approaches, namely theoretical and empirical ones (Rostami et al. 1996). Currently, two models including Colorado School of Mines or CSM (Rostami 1997) and Norwegian University of Science and Technology or NTNU (Blindheim 1979, Bruland 1998) models are the most recognized TBM performance prediction and prognosis models in use around the world. The CSM model allows calculation of the cutting forces that need to be applied to a disc in order to reach a certain penetration into the rock. This method offers the advantages of being able to consider the geometry of the disc and cutterhead (the diameter of disc and the distance between the grooves) in detail. However, the original CSM model does not consider the natural discontinuities of the rock mass, which have an important influence on the net advancement speed on the TBM. Yagiz (2002) has offered some modifications to the original CSM model by adding rock mass properties as input parameters into the model. Also, Ramezanzadeh (2005) has followed up on this work and developed a database of TBM field performance for over 60 km of tunnels. He offered adjustment factors for CSM model to account for joints and discontinuities. NTNU model was originally developed in 1970 s and is mainly based on field performance of TBM and accounts for joint conditions in the ground. Bruland (1998) updated and improved the NTNU model based on field data mainly collected from Norwegian tunnels. The NTNU method uses some rock drillability indices such as Drilling Rate Index (DRI) estimated from index of rock brittleness S 20 and Surface hardness index S J to represent the rock strength and hardness. The NTNU model requires special boreability indices that are not commonly performed in tunneling projects. It also uses joint conditions to develop the estimated rate of penetration of TBM. Field penetration index (FPI) has been presented by Nelson et al. (1983) and has been subsequently used as means for predicting the performance of TBMs. Hassanpour et al. (2011) has recently used FPI estimated as a function of RQD and UCS to develop a new chart for 975

Table 1. Main characteristics of tunneling project. Tunnel TBM Length Available diameter Projects (km) data (km) TBM type (m) Maroshi- 12.24 5.83 Hard rock 3.6 Ruparel water (Maroshi- Gripper supply tunnel Vakola) TBM (Wirth) Geological Max. zone Formation Lithology depth (m) Deccan Upper Fine compact 82 traps traps basalt, (Lava (Upper Porphyritic basalt, flows of Cretaceous Amygdaloidal Basaltic to Lower basalt, rocks) Eocene) Pyroclastic rocks (Tuff, Tuff breccia) and Intertrappeans (Shale) prediction of TBM performance. Furthermore, a new model has been proposed by Delisio & Zhao (2014) for prediction of TBM performance in blocky rock conditions (FPI blocky ) based on uniaxial compressive strength (UCS) and volumetric joint count (J v ). In this study, complied field data obtained from tunneling project namely, Maroshi-Ruparel water supply tunneling project in Mumbai, India (Jain et al. 2014, Jain 2014), was used to evaluate the relationship between geomechanical properties and TBM performance parameters to develop a new model for TBM performance estimation. 2 DESCRIPTION OF THE PROJECT USED FOR THIS STUDY As mentioned before, the tunneling project which is investigated for the relationship between geomechanical properties and TBM performance parameters is Maroshi-Ruparel water supply tunnel in Mumbai, India. The detail information related to geology of the area, TBM specifications, physio-mechanical properties of rocks can be found in Jain et al. 2014. The main characteristics of the TBM tunneling project are summarized in Table 1. 3 TBM FIELD PERFORMANCE DATABASE In this study, data on geological and ground conditions, TBM operational parameters and machine performance represented by rate of penetration were collected during pre-construction and construction phases. The data were organized in a special database including 72 tunnel sections where the ground conditions and machine performance were reliable and could be verified. The data sets included two main categories. The first category contained machine performance parameters such as, net boring time, length of mined section as well as the average of machine operational parameters like thrust, RPM, applied torque and power throughout the section. These parameters were gathered from the daily operating records and TBM data logger. Besides, the most important performance parameters containing average penetration rate (ROP), penetration per revolution (P) and field penetration index (FPI) have been estimated using the formula as listed below: where ROP is rate of penetration (m/h), L b is boring length (m), t b is boring time (h), P is cutter penetration in each cutterhead revolution (mm/rev), RPM is cutterhead rotational speed (rev/mm), FPI is Field Penetration Index (kn/cutter/mm/rev), F n is cutter load or normal force. The second part of database included some geological parameters such as intact rock properties (Compressive strength, porosity and ), discontinuity characteristics such as spacing, surface condition, weathering/alteration, ground water and also results of calculation of some rock mass parameters (like RQD, RMR, Q and GSI) in selected tunnel sections. Descriptive statistical distribution of variables in the database and input parameters for generated model is summarized in Table 2. Since the parameters including joint condition (J C ) and ground water condition (G W ) in RMR systems are qualitative (descriptive), the partial rating of these parameters are used in this analysis. Also, it is important to note that, where multiple joint sets are identified, different strategies could be adopted to incorporate their impact. One approach is to focus on the critical joint set which can have the highest impact (most assist or hinder) on TBM penetration rate. Another approach is to use a combination of the joints as prescribed by NTNU system and using a Ks-tot. The last approach is to compute an average α angle for all existing joint sets. This approach has some down sides since it assumes an arithmetic averaging of the joint orientation to represent the cumulative impact of the joints.the approach used in this study is the first one i.e. using the critical joint set, which was selected to be the set with highest frequency and minimum joint spacing. 4 ROCK MASS CLASSIFICATION SYSTEMS Over the years, many rock mass classification systems have been presented in mining and civil engineering. According to Bieniawski (1989), a rock mass classification scheme is intended to classify the rock masses, provide a basis for estimating deformation and strength properties, supply quantitative data for support estimation and present a platform for communication between exploration, design and 976

Table 2. Descriptive statistics of generated database for this study (Jain et al. 2014, Jain 2014). Variable Min Max Mean Std. Variance UCS (MPa) 16 143 72.77 29.05 843.90 J S (m) 0.05 1.25 0.41 0.241 0.059 RQD (%) 20 95 62.45 21.44 459.77 J C rating 8 26 15.54 4.86 23.66 in RMR G W rating 4 15 12.80 2.79 7.82 in RMR Alpha ( ) 1 79 42.36 20.04 401.84 RMR (basic) 25 90 53.63 15.80 249.89 Q 0.12 165.85 11.46 24.49 600.14 GSI 20 85 47.88 15.49 240.15 ROP (m/h) 1.38 4.11 2.40 0.69 0.477 FPI (kn/ 5.90 23.76 13.65 4.35 18.95 cutter/mm/rev) construction groups.also, these models are commonly employed in many empirical design practices and planning in rock engineering contrasting with their original intent and applications. A good example is usage of available rock mass classification systems in estimation of TBM performance in different tunneling projects because of the simplicity and worldwide acceptance of the classification systems in general engineering practices, and in particular in underground mining and construction. To address this conflict, extensive investigations have been carried out for determining thetbm performance based on rock mass classification systems. There are new models and rock mass classifications notably adapted for application of TBM projects. Barton (2000) proposed a new model based on Q rock mass classification system, namely Q TBM which includes many input parameters. Also, Alber (2000) presented RMR TBM which is modified of RMR system for use intbm tunneling projects. Sapigni et al. (2002) correlated TBM performance parameters containing penetration rate (PR) and FPI to RMR classification system. Innaurato et al. (1991) developed a new model for estimation of penetration rate based on intact rock (expressed by uniaxial compressive strength) and rock mass condition defined by Rock Structure Rating (RSR). The model takes into account the effect of intact and rock mass, but the latter is defined by infrequent geomechanical quality index which is rarely available in the geotechnical characterization of a tunnel. Moreover, the penetration rate is predicted without any note to the force FN acting on disc. In addition, the determination of Rock Mass Excavibility (RME) which has been proposed by Bieniawski et al. (2006) can be correlated directly with the performance oftbm based on the assessing of intact and rock mass characteristics. The fact is that, the RME index is quite similar to RMR and quite easy to estimate, however the force acting on single disc is not considered. Such a force, as shown by Rostami (1997) can have major effect on penetration rate. Among the most commonly used rock mass classification systems, the RMR classification is easiest to Figure 1. FPI. Correlation between basic RMR and measured apply and based on the results of Sapigni et al. (2002), the RMR shows a better correlation with TBM penetration rate, possibility due to the use of intact rock property as an input parameter. This matter also has been confirmed by Hassanpour et al. (2011) which found better correlation with RMR, compared to Q- system as well as GSI. Hence, the RMR classification has been selected for analyzing the TBM performance. 5 TBM PERFORMANCE ESTIMATION USING RMR CLASSIFICATION SYSTEM Various TBM performance indices have been introduced based on field penetration index (FPI), specific penetration (SP, inverse of FPI) and boreability index (BI, similar to FPI). FPI seems to have been more successful to show correlation between the RMR and TBM performance. Higher values of FPI are usually seen in strong and massive rock masses, in contrast low FPI indicates weak, jointed rocks (Hassanpour et al. 2011). Hence, the FPI has been selected to be a representative of rock mass boreability. According to the result of Hassanpour et al. (2011) basic RMR shows better correlation with machine performance than RMR89. Therefore, basic RMR ratings has been adopted for current study. The relationship between FPI and RMR is shown in Figure 1, which shows weak correlation between basic RMR and FPI (R 2 = 0.52). The study of correlations between the individual independent variables in the basic RMR and the actual measured FPI shows that UCS offers the highest R 2 value of 0.653, followed by the JS (0.614), RQD (0.523), J C partial rating in RMR (0.314) and G W partial rating in RMR (0.003). Khademi et al. (2010) considers α (Alpha) as an alternative adjustment factor for discontinuity orientation in RMR 89. The influence of the angle (α) on TBM penetration rate has been widely reported by many researchers (Bruland 1998, Gong & Zhao 2009). The relationship between FPI and the α angle illustrated in Figure 2, is almost consistent with that of the results of field studies by Bruland (1998) and numerical simulation by Gong et al. (2005). Some investigations maintain that the best TBM performance occurs at an Alpha angle between 45 and 90. The impact of joint 977

Figure 2. Correlation of joint orientation (Alpha) with measured FPI. Figure 4. Comparison between the measured and predicted FPI based on UCSrm. 6 DEVELOPING NEW EMPIRICAL EQUATIONS In the present investigation, the principle component analysis (PCA) was performed, followed by single and multiple regression analysis between TBM performance and geomechanical parameters in database. Figure 3. Correlation between RMRP and Measured FPI. angle is controlled by the nature of the joints and joint spacing. For single joints this angle was found to be between 35 to 45 in this study, and 60 in previous studies, i.e. NTNU model. The difference could also be derived from the index by which the TBM performance is analyzed. TBM performance analyzed based on FPI in this study, while the performance indicator used by other studies were penetration rate. Due to the marginal impact of ground water condition and low correlation with the FPI, it seems to be logical to adopt a modified RMR where a fixed value R W = 15 is used. By considering this simple procedure, correlation coefficient slightly better than those with basic RMR can be obtained (Fig. 3). Finally, it was deemed useful to analyze correlation of FPI with uniaxial compressive strength of rock mass UCSrm (Heok et al. 1995) which can be estimated as follow: The FPI values obtained from the Maroshi-Ruparel water supply tunnel was correlated with UCSrm (based on RMRP) using a power function with the correlation coefficient of R 2 = 0.68 as depicted in Figure 4. This indicates that the following equation explains 68% of the total variance of 72 datasets. 6.1 Principle Component Analysis PCA is frequently used in different types of analysis due to its simplicity, nonparametric method of extracting relevant information from confusing data sets. In this paper, PCA was performed on a set of input (independent variables) and output para-meters, and the ratio of variance of first component to total variance (variance ratio) were calculated. Accordingly, this ratio can be determined by the similarity among the output and a set of input factors. Several analyses with two, three and four features were performed to obtain the effective parameters on the TBM performance (Fig. 5). Although the factor containing four inputs (UCS, RQD, J C and Alpha) was shown to be more effective on FPI, the difference in comparison with three factors including (UCS, RQD, J C ) is minimal, hence FPI has been considered as a function of these three inputs i.e. (UCS, RQD, J C ); and therefore these parameters were selected as input parameters for developing a new empirical equation. 6.2 Non-linear multiple regression analysis The R program environment was used for multivariable non-linear regression analysis. As a result, a new performance prediction equation was empirically obtained as follows: where, FPI is the field penetration index (kn/mm/rev), UCS is the rock uniaxial compressive strength (MPa), RQD is Rock Quality Designation and J C is joint condition partial rating in basic RMR. Based on the results of statistical analysis, the coefficient of regression (R 2 ) 978

Figure 5. Principle components analysis for some features in this study. Figure 6. Comparison between the measured and predicted FPI. was found to be 0.762.A comparison between the measured and predicted results from Eq. 4 is shown in Figure 6. 7 CONCLUSION Use of original rock mass classification systems, may be a useful tool for tunnel support design, but different ratings should be applied if and when these systems are going to be used in TBM performance prediction. The application potential of RMR system for predicting TBM penetration rate was illustrated in this paper. Results of this study demonstrate that UCS has the highest effect on FPI and it decreases in order of J s, RQD, J c and G w. There is a good correlation between rock mass strength derived from modified RMR and field penetration index. It is well known that the orientation of discontinuities can affect the boreability and TBM performance most significantly but similar studies revealed that quantifying the impact of the joint orientation on TBM performance has not been very successful and formulas offered in many studies have limited application (Ramezanzadeh 2005). Also, Bruland (1998) noted that with the increase of joint spacing, the effect of joint orientation on TBM penetration rate decreases. This investigation shows that the effect of joint orientation on TBM penetration rate is a function of joint fracture spacing (i.e. RQD or J s ). Offering a clear relationship between FPI and joint angle is hindered by the lack of detailed information. A new site specific empirical equation has been presented for estimating TBM performance by employing non-linear regression analysis on field data collected from Maroshi-Ruparel water supply tunneling project Mumbai, India. However, the range of input data, rock types, machine type, etc. used for development of the proposed prediction model was very limited and as such, the results cannot be considered to be universal. Therefore, the proposed formula can be used in similar geology and machine type, although the use of FPI allows for using the model for various machine sizes. In brief, the developed equation in this study can use strength of rock material (UCS) and two inputs defining joint conditions (RQD and J C ) for estimation of ROP with R 2 of about 76%. Additional analysis is underway to include more field performance data to fine tune the proposed model. ACKNOWLEDGEMENT The authors wish to extend its sincere thanks to Dr. Prasnna Jain and Prof. T.N. Singh for sharing their data in building the TBM databases for this study. 979

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