State-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming

Abstract

Hard rock TBM performance prediction is of great interest to the tunneling community on account of its importance in time and cost risk management of underground projects. Continuous development of new empirical models in recent decades reveals the importance of accurate prediction of this factor in diverse ground and machine conditions. The great number of different parameters influencing TBM performance and the high variability linked to specific field conditions cause the problem to be very complex. Gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate hard rock TBM performance with routine ground properties for project design applications. The developed models are compared with those from statistical and soft computing-based models in the literature. Overall, GEP models show good performance and are proven to be much better than the previous models. The proposed models of this study can be remarked as an ultimate stage to one decade of researchers’ attempts to improve the accuracy of predictive equations developed through a well-known database of TBM performance in one of the most complex tunneling projects in the world.