Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms

Existing rock mass classification systems, such as Rock Quality Index Q, Geological Strength Index (GSI), and Rock Mass Rating (RMR) are often used in many empirical design practices in rock engineering contrasting with their original application. For example, these models which were originally introduced for ground support design are being used in estimation of TBM performance in various ground conditions. Previous use of standard rock mass classification systems in TBM performance prediction has had limited success due to the nature of the weights associated with the input parameters as evidenced by low correlations between their output and Penetration Rate (PR) of TBM in various field applications. This limitation can be mitigated by revising the weights assigned to input parameters, to better represent influence of rock mass properties on TBM performance using multivariate regression analysis and artificial intelligence algorithms, including regression tree and genetic programming. This paper offers a brief review of the applications of common rock mass classification systems for performance prediction of TBMs and development of a new model which is based on the input parameters of RMR system for this purpose. The proposed model has been developed based on the analysis of a comprehensive database of TBM performance in various rock types and offers higher accuracy and sensitivity to rock mass parameters in predicting machine performance.