Abstract:
Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring
machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous
incidents associated with rock mechanics and engineering. This study aims to predict TBM performance
(i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was
done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS
model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of
ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a
tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture
spacing, a angle between the plane of weakness and the TBM driven direction, and field single cutter
load were assigned as model inputs to approximate FPI values. According to the results obtained by
performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in
predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2
), the
values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC
model, respectively, which confirm its power and capability in solving TBM performance problem. The
proposed model can be used in the other areas of rock mechanics and underground space technologies
with similar conditions