dolphin echolocation algorithm

One of the indicators for evaluating the performance of a tunnel drilling machine is predicting the penetration rate of this machine. There are various methods and relationships for predicting the penetration rate, each of which has its own characteristics and are presented based on the parameters related to the rock mass and the characteristics of the machine. In this study, genetic, artificial immune system, dolphin echolocation and grey wolf algorithms were used to predict the penetration rate of TBM. In this regard, the database consists of 153 data (122 data for train and 31 data for test) including parameters of intact rock such as strength and brittleness and rock mass characteristics such as distance between planes of weakness and orientation of discontinuities along with TBM machine performance in Queens tunnel has been collected. Mean square error (MSE) and square correlation coefficient (R2) have been used to estimate the error rate between the developed methods. Considering the key parameters of rock mass and intact rock and TBM, relationships to predict the penetration rate are presented and based on statistical analysis, the best relationship is selected. The results are compared with the real data and the results of other models show that the values penetration rate predicted by the genetic algorithm with MSETrain=0.012, MSETest=0.02, R2Train=0.9319 and R2Test=0.8473,has acceptable accuracy compared to other methods.