Setting up simple estimating equations of TBM penetration rate using rock mass classification parameters

Establishing a suitable TBM performance prediction model is benefit to the machine type selection, project scheduling and budgeting. Some regression equations using rock mass classification parameters had already been set up based on TBM tunneling database, some of which were small. Besides, most of existing equations involved field penetration index (FPI) or the cutter force. Once apply these equations, how to determine the cutter load is a problem. The cutter thrust force is affected by various factors, the most important of which is the ground conditions. To make a straightforward estimation of penetration rate at planning phase, some simple regression equations merely using rock mass classification parameters were set up based on 115 tunnel sections sourced from three TBM job sites. TBM penetration rate (mm/rev) was employed as the prediction result. It is a comprehensive value including the rock mass condition and the experiences of TBM operators in the projects used for this study, since TBM operators would adjust the thrust force with the changing rock masses to get an acceptable penetration rate on site. The datasets in the database indicated the intrinsic relationship among the rock mass condition, the needed thrust force and the obtained penetration rate. Five rock mass classification systems including RMR, Q, GSI, HC and BQ were covered, the latter two are widely used in China. The input rock mass classification parameters were categorized into three types, which respectively are final ratings, estimated rock mass properties, and partial ratings or corresponding original values. Generally, the multivariate equations using partial ratings have the best correlation with TBM penetration rate. The strength of intact rock, integrity of rock mass and joint condition are proved to be the most critical factors in the estimation of TBM penetration rate. The established equations reflect the statistical relationship between the rock mass and penetration rate with the requisite of the normal TBM tunneling data in various rock masses, and can be easily applied in the planning phase of a TBM tunneling project.

Automated summary

Establishing a suitable TBM performance prediction model based on rock mass classification parameters is essential for machine type selection, project scheduling, and budgeting. Regression equations using field penetration index or cutter force have been developed, but determining cutter load remains a challenge. The thrust force of the cutter is influenced by various factors, particularly ground conditions, and can be estimated using simple regression equations during the planning phase of a TBM tunneling project.