An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

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,α 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 ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC 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.

In situ experimental study on TBM excavation with high-pressure water-jet-assisted rock breaking

China’s first high-pressure hydraulically coupled rock-breaking tunnel boring machine(TBM) was designed to overcome the rock breaking problems of TBM in super-hard rock geology, where high-pressure water jet system is configured, including high-flow pump sets, high-pressure rotary joint and high-pressure water jet injection device. In order to investigate the rock breaking performance of high-pressure water-jet-assisted TBM, in situ excavation tests were carried out at the Wan’anxi Water Diversion Project in Longyan, Fujian Province, China, under different water jet pressure and rotational speed. The rock-breaking performance of TBM was analyzed including penetration, cutterhead load, advance rate and field penetration index. The test results show that the adoption of high-pressure water-jet-assisted rock breaking technology can improve the boreability of rock mass, where the TBM penetration increases by 64% under the water jet pressure of 270 MPa. In addition, with the increase of the water jet pressure, the TBM penetration increases and the field penetration index decreases. The auxiliary rock-breaking effect of high-pressure water jet decreases with the increase of cutterhead rotational speed. In the case of the in situ tunneling test parameters of this study, the advance rate is the maximum when the pressure of the high-pressure water jet is 270 MPa and the cutterhead rotational speed is 6 r/min. The technical superiority of high-pressure water-jet-assisted rock breaking technology is highlighted and it provides guidance for the excavation parameter selection of high-pressure hydraulically coupled rock-breaking TBM.