Application of Several Fuzzy-Based Techniques for Estimating Tunnel Boring Machine Performance in Metamorphic Rocks

Tunnel boring machine (TBM) performance prediction in mechanized tunneling is an essential factor for selecting an appropriate excavation machine, tunnel design, and safe construction. To implement safe mechanized excavation, it is important to accurately assess and predict the range of machine driving parameters, especially the machine rate of penetration (ROP); this can reduce the cost of TBM repairs due to the abrasion of disc cutters and cutterhead and also has a positive effect on the post-construction period. This study focuses on predicting the ROP of TBMs passing through metamorphic rocks during deep excavation and under a complex geotechnical situation. For this purpose, three fuzzy-based models of the Mamdani fuzzy inference system (MFIS), adaptive neuro-fuzzy inference system (ANFIS), Takagi Sugeno fuzzy model (TSF), as well as linear and non-linear regression models were developed. Historical tunnels were used to compile 189 data points (151 for training and 37 for testing). In the dataset, three parameters, including uniaxial compressive strength (UCS), cutterhead rotational speed per minute (RPM), and thrust force (TF), were considered effective parameters on the TBM’s ROP. According to the findings, the suggested models provided satisfactory and consistent accuracy. Moreover, the results demonstrated that the forecasted values correlate rather well with the measured ones. The proposed algorithms can be considered for use in similar ground and tunneling conditions (metamorphic rocks with low-average strength). It is worth noting that this study has the potential to drastically cut down on tunneling uncertainties and makes fuzzy inference systems a robust algorithm for planning mechanized tunneling. Highlights Prediction of TBM performance in complex geological conditions. Forecasting TBM performance in deep tunnels passing through metamorphic rocks. Presenting an empirical model for calculating the TBM performance based on statistical analysis. Detailed analysis of fuzzy-based techniques potential for TBM performance prediction. Examining the models’ accuracy with several loss functions and statistical indices.