Abstract
Tunnel boring machine (TBM) is a popular rock cutting machine for rapid construction of tunnels. This paper mainly dwells on the excess cutter wear and low penetration rates encountered while tunnelling through hard and abrasive rock in a head race tunnel being driven for hydel power generation. Wear prediction in disc cutters with its mechanism was reviewed. Field performance data and laboratory characterization of rock were done for analyzing the causative factors. This was followed by data analysis using a multilayer shallow neural network (MSNN) for identifying the key parameters and their influence on the output parameters (cutter wear and rate of penetration). Five major process control parameters including two machine parameters, namely, thrust and torque, one design parameter, i.e., radial position of cutter and two rock parameters namely uniaxial compressive strength (UCS) and Cerchar abrasivity index (CAI) are considered in the study. Rock type is kept constant (quartzite) to analyze the influence of the machine operating parameters on the cutter penetration rate and the cutter wear. Two different scenarios were analyzed. The correlation coefficients obtained between output and target for two cases investigated were 0.927 and 0.965, respectively. Sensitivity analysis of the input parameters on the output parameter is also carried out. For validation of the result, response surface method (RSM) was used for the analysis of historical data. Both MSNN and RSM predict the influence of key variables affecting cutter wear (CW) and rate of penetration (RoP) with a good confidence. In a given rock setting, it is possible now to fix the optimal values of Thrust and Torque to control the cutter wear while maintaining an acceptable rate of TBM penetration.
Highlights
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TBM experiences high cutter wear and low penetration rate in hard and abrasive rock.
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Gauge cutters get worn out more than center and face cutters due to higher rolling length and confinement.
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Cutter wear depended more on CAI, UCS, thrust while RoP depended more on thrust and torque.
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RSM and desirability functions identified the optimal TBM operating range of thrust and torque for lower cutter wear and higher rate of penetration.
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Acknowledgements
Authors are grateful to CPRI for funding the research project (Project no. CPRI/2016-17/492/ME) and NHPC Ltd. for permitting site investigations and providing needful support. Support of Gaurav Kumar Srivastava, Biswaraj Dash, Ch. Srihari, Manish Kumar, research scholars and Shri R.K.Das Technical Superintendent, Rock Excavation Laboratory, IIT (ISM), Department of Mining Engineering, are thankfully acknowledged. The views expressed are of authors and have their domain of interpretation.
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Appendix
Appendix
See Table 10
Final Equation in Terms of Coded Factors.
CW0.88 = + 11.45 + 0.3186 A−1.2 B−0.1208 C – 0.7552 D−7.44 E[1] – 1.22 E[2] + 2.90 E[3] + 0.5214 AE[1] + 0.2518 AE[2] – 0.3511 AE[3] + 1.36 BD + 1.54 CE[1] – 0.1928 CE[2] −0.7820 CE[3] + 0.5448 B2 + 0.3572 D2.
For given levels of each element, the equation in terms of coded factors can be used to make predictions about the response. The high levels of the factors are coded as + 1 and the low levels of the factors are coded as -1 by default. By comparing the factor coefficients, the coded equation can be used to determine the relative impact of the components.
Final Equation in Terms of Actual Factors are as follows:
CW0.88 = CAI Level 1 of D + 24.58370 + 0.000143 * Thrust—0.012560 * Torque + 0.000925 * RPC—0.137452 * UCS + 0.000043 * Torque * UCS + 1.11186E-06 * Torque2 + 0.000176 * UCS2.
CW0.88 = CAI Level 2 of D + 33.28259 + 0.000097 * Thrust-0.012560 * Torque—0.000205 * RPC—0.137452 * UCS + 0.000043 * Torque * UCS + 1.11186E-06 * Torque2 + 0.000176 * UCS2.
CW0.88 = CAI Level 3 of D + 38.94808—5.55066E-06 * Thrust—0.012560 * Torque—0.000589 * RPC—0.137452 * UCS + 0.000043 * Torque * UCS + 1.11186E-06 * Torque2 + 0.000176 * UCS2.
CW0.88 = CAI Level 4 of D + 41.62735—0.000018 * Thrust—0.012560 * Torque -0.000447 * RPC—0.137452 * UCS + 0.000043 * Torque * UCS + 1.11186E-06 * Torque2 + 0.000176 * UCS2.
See Table 11
Final Equation in Terms of Coded Factors are as follows:
√RoP = + 1.70—0.1640 * A + 0.1412 * B + 0.0346 * C—0.5768 * D—0.1504 * E[1] + 0.0483 * E[2] + 0.0597 * E[3]—0.2256 * AD + 0.1967 * BD + 0.1655 * CE[1]—0.0117 * CE[2]—0.0560 * CE[3]—0.1213 * B2 + 0.2457 * D2.
Final Equation in Terms of Actual Factors.
√RoP = CAI Level 1 of D + 8.17090 + 0.000139 * Thrust—0.000422 * Torque + 0.000131 * RPC—0.060769 * UCS—8.54739E-07 * Thrust * UCS + 6.24570E-06 * Torque * UCS—2.47632E-07 * Torque2 + 0.000121 * UCS2.
√RoP = CAI Level 2 of D + 8.58540 + 0.000139 * Thrust—0.000422 * Torque + 0.000015 * RPC—0.060769 * UCS—8.54739E-07 * Thrust * UCS + 6.24570E-06 * Torque * UCS—2.47632E-07 * Torque2 + 0.000121 * UCS2.
√RoP = CAI Level 3 of D + 8.65081 + 0.000139 * Thrust—0.000422 * Torque -0.000014 * RPC—0.060769 * UCS—8.54739E-07 * Thrust * UCS + 6.24570E-06 * Torque * UCS—2.47632E-07 * Torque2 + 0.000121 * UCS2.
√RoP = CAI Level 4 of D + 8.68444 + 0.000139 * Thrust—0.000422 * Torque -0.000041 * RPC—0.060769 * UCS—8.54739E-07 * Thrust * UCS + 6.24570E-06 * Torque * UCS—2.47632E-07 * Torque2 + 0.000121 * UCS2.
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Agrawal, A.K., Murthy, V.M.S.R., Chattopadhyaya, S. et al. Prediction of TBM Disc Cutter Wear and Penetration Rate in Tunneling Through Hard and Abrasive Rock Using Multi-layer Shallow Neural Network and Response Surface Methods. Rock Mech Rock Eng 55, 3489–3506 (2022). https://doi.org/10.1007/s00603-022-02834-7
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DOI: https://doi.org/10.1007/s00603-022-02834-7