Prediction of TBM performance in different rock types using input parameters of RMR by applying ML-based regression analysis
Despite the widespread use of tunnel boring machines (TBMs) in tunneling projects, accurate estimates of their performance, especially in complex geological conditions, can still be challenging. The aim of this study is to investigate the possibility of using RMR rock mass classification system parameters to predict the TBM performance in different rock types, using regression analysis based on machine learning algorithms. Therefore, real machine performance data, as well as geological and geomechanical data, were collected from 10 tunneling projects in a comprehensive database with 523 tunnel sections in different rock types and were used to develop new relationships to predict the field penetration index (FPI) based on input parameters of the RMR classification system. Since different rock types have different textures, structures, and mineralogical compositions and respond differently to machine shear forces, combining the effects of rock type in the models for prediction of TBM performance can improve the accuracy of estimates. These relationships can be especially useful in the design and planning of a tunneling project. This study aims to investigate the possibility of using the input parameters of the RMR rock mass classification system to develop TBM performance prediction relationships using regression analysis based on machine learning (ML) algorithms. New equations can lead to estimating the performance of the TBM under different geological conditions and taking into account the main and effective parameters.
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