EVALUATING BOREABILITY OF METAMORPHIC ROCKS: INTEGRATING TEXTURE COEFFICIENT, ROCK ABRASIVITY, AND OPERATIONAL PARAMETERS FOR ENHANCED TBM PERFORMANCE PREDICTION

Abstract

The efficiency and cost-effectiveness of mechanized tunneling in hard rock conditions are closely tied to the geological characteristics of the rock mass, particularly its texture and mechanical properties. This study investigates the potential of assessing rock boreability using the Texture Coefficient (TC), which quantifies rock texture based on grain shape, orientation, and interlocking. By examining the relationship between TC, other physical rock properties, and Tunnel Boring Machine (TBM) performance, this research aims to improve the prediction of Rate of Penetration (ROP) and support effective tunneling strategies.

The methodology is based on the analysis of rock samples collected from the Queens Water Tunnel excavation project in New York City, 1996-1999. Thin sections of the rock samples were examined using image analysis software (ImageJ), following the TC identification procedure outlined by Howarth and Rowlands (1987). Additionally, mechanical and physical properties, including Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Cerchar Abrasivity Index (CAI), and others, were obtained from previously performed laboratory tests. TBM performance parameters such as penetration rate, thrust, torque, and power were gathered from field data recorded during excavation.

The linear and nonlinear regression models were developed to estimate the TBM penetration rate using TC, UCS, CAI, Cutterhead Power (CP), and Alpha Angle as input variables. The analysis indicated that CP and CAI strongly influenced ROP prediction, while the contribution of other variables was less significant. The most optimal models achieved a considerable degree of fit with R² values between 0.82 and 0.85, indicating a strong correlation between the input parameters and actual TBM performance.

The results show that the application of TC for TBM performance prediction models is limited in its current form. Despite the overall high predictive power of the presented models, the TC plays an insignificant role if included in the model, occasionally increasing the prediction error. Future work may explore the potential modifications of TC depending on the rock type and the automation of the TC calculation process, improving its practicality for research and engineering applications.