Fuzzy logic modelling to predict the level of geotechnical risks in rock Tunnel Boring Machine (TBM) tunnelling (Rudarsko-geološko-naftni zbornik, 2020)
This study aims to analyze the level of geotechnical risks and predict the advance rate in rock Tunnel Boring Machine (TBM) tunnelling, using a multi-stage fuzzy logic modelling. Twelve parameters, affecting the geotechnical hazard scenario occurrence, which were clustered into five groups, were used as input parameters and the risk level was used as an output parameter. Also, based on the relation between the risk levels and advance rates, a predictive model for advance rate prediction was proposed. To validate the performance of modelling carried out, data from 58 geological zones in section two of the Zagros tunnel, Iran were used. The obtained results showed that by using the fuzzy logic- based model, in most zones, the risk levels estimated are in good agreement with field observations. Moreover, as expected, the high coefficient of determination (R2) of 0.91 between the risk level estimated and the average advance rate achieved in 58 analyzed zones, confirms the ability of the model proposed to predict the level of geotechnical risks. Furthermore, R2= 0.93, Root Mean Square Error (RMSE) of 0.62 and Variance Accounted For (VAF) of 97.51 between the measured and predicted advance rates show the good performance of the new predictive model developed for the advance rate estimation.