Prediction Model of TBM Rock Breaking Efficiency Based on MachineLearning
In the operation of TBM,the rock breaking efficiency of the hob will be interfered by the complicated geological environment and its own equipment parameters,so it is particularly critical to analyze the changing principle of the rock breaking ef-ficiency of the hob.Compared with the general drilling and blasting method,TBM has the advantages of rapid tunneling and short rock breaking time.Therefore,one of the key problems in TBM construction is how to improve the rock breaking efficiency.There-fore,one of the key problems in TBM construction is how to improve the rock breaking efficiency.Based on the Phase Ⅱ project of Chongqing Rail Transit Line 10,this paper takes the granite collected at the site of the project as the research object to carry out la-boratory tests.In this paper,based on the two-dimensional hob fractured jointed rock mass test,machine learning method was intro-duced,the improved BP neural network was used to construct the prediction model of rock breaking efficiency of TBM hob,and the accuracy of the prediction results was tested.It is also found that the accuracy of the prediction results in this study is relatively good,which can be used in the prediction of the rock breaking efficiency of TBM.It also shows that the input parameters of the model can fully estimate the rock breaking efficiency of the TBM hob,which lays a theoretical condition for analyzing the rock break-ing efficiency of the project.
rock breakingmachine learningefficiency prediction
万方数据
维普
