Efficiency of a tunnel boring machine (TBM) depends on its cutting performance, and instances of breakdowns reduce the machine availability while escalating the time and cost associated with the project. Cutting performance is governed by the replacement cycle of cutter-picks, since they endure damage due to wear and tear. In order to optimize the consumption of cutter-picks, it is important to analyse the shear and impact forces on the cutter and the properties that govern rock-cutter interactions. One such governing property is abrasivity that can be determined by Laboratoire Central des Ponts et Chausées (LCPC) test and CERCHAR Abrasivity Index (CAI) test. Since CAI provides a timely and reliable estimation of rock abrasivity at the tunnelling site, the results of CAI can be used to estimate the lifecycle and cost of cutter replacement. Research suggests that the value of CAI is largely dependent on the mineralogical, physical and mechanical properties of rocks. Therefore, in this study, samples of igneous, metamorphic and sedimentary rocks were analysed for their physico-mechanical response and their respective CAI values were obtained. Additionally, an estimation of tool consumption has been proposed based on the experimental data. Further, statistical and soft-computing tools such as multivariate regression analysis (MVRA), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to develop novel predictive and correlation models. The physico-mechanical properties such as compressive and tensile strength, porosity and dry density served as input parameters for the prediction of CAI. A comparative analysis of various performance indices has been conducted in order to establish the efficiency of the developed models. The results suggest that the ANFIS model provides better correlation between the input and output parameters.
Techno-economic efficiency of excavating underground infrastructures depends on the efficiency of the cutting operation in machines such as tunnel boring machine (TBM) and road headers. Efficiency is governed by the condition of the cutting mechanism since cutter-discs and picks experience consistent wear and tear upon rock interaction, and frequent replacements of the worn-out tools increases the project cost and duration. Studies suggest that the rate of consumption of a tool depends on the geology and machine parameters. While the machine parameters are within our control, the type of geological formation and its characteristics govern the choice equipment to be used. One such characteristic is rock abrasivity which accounts for gradual loss of the cutting tool due to pick-rock interaction. The physico-mechanical properties of the rock surface and the stresses involved in the pick-rock interaction also play a major role in the total wear (Alber 2008; Singh et al. 2001). Abrasivity of rocks, can be analysed using the CERCHAR abrasivity index test (CAI) which involves creating a 10 mm scratch on a rock sample that moves at a velocity of 1 mm/s by means of a steel stylus carrying a static load of 70 N (Käsling and Thuro 2010; West 1989). Thereafter, the stylus is examined under a microscope to calculate the abrasivity index (CAI) (Alber et al. 2013). This index can be used to estimate the consumption and damage of cutter-discs, picks and drill-bits with the help of predefined charts and equations (Plinninger et al. 2004). The magnitude of wear depends entirely on the rock type, and therefore, in this study, rocks of various lithology have been analysed in order to study the dependence of abrasivity on the physico-mechanical properties.
