Excavation performance estimation of tunnel boring machine by fuzzy logic approach

With the increasing population in our country and the world, the number of tunnels bored for transportation purposes has been increased, especially in the regions where urbanization is intense. Full-face tunnel boring machines (TBM) are widely used in tunnel boring operations. It is a very complex process to determine the excavation performance of a TBM in advance. The reason for this is that many factors related to both the rock mass and the machine are effective in determining the excavation performance of TBMs. Therefore, theoretical, empirical and intelligent models used to determine the excavation performance of TBMs are frequently used methods. In this study, the excavation performance (rate of penetration, İH) of the earth pressure tunnel boring machine (EPB TBM) used in Halkalıİstanbul Havalimanı metro tunnel was monitored for 2 years. In addition, machine-related data such as the cutter head rotation speed (KDH), penetration (P), and thrust force (İ) were measured and recorded during excavation. In addition, field data related to rock mass such as rock mass rating (RMR), uniaxial compressive strength (TEBD) and weathering degree (AD) of the excavated formations were also gathered. Using these data, multiple regression analysis was performed, and a multivariate prediction equation was obtained. In addition, a fuzzy logic-based prediction model which uses Mamdani algorithm was created to estimate the penetration rate of the EPB-TBM and the obtained results were compared. Accordingly, the correlation coefficient of the prediction equation obtained from the multiple regression analysis was found to be r=0.61, and the correlation coefficient between the penetration rate obtained from the fuzzy prediction model and the actual field measurements was found to be r=0.63. This result reveals that intelligent models using fuzzy logic concept can be successfully used in the prediction of TBM excavation performance.