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Performance Prediction Models for Hard Rock Tunnel Boring Machines

Tunnel Boring Machine (TBM) is one of the most popular tunneling equipment in the industry. Modern hard rock TBMs are very versatile and have been used very favorably in various ground conditions while setting advance rate records of over 170 m a day. There has been a lot of research on development of models to allow accurate prediction of machine rate of penetration in given ground conditions. These models, although successful in prediction of machine performance in many cases, are short of accounting for some of the parameters affecting machine performance in a variety of grounds. Moreover, with more accurate predictive capabilities and better understanding of operational parameters, accurate planning and cost estimation is possible, which allows for wider area application for TBMs. This justifies the initial high investments for the machine and facilitates increased productivity by proper planning of the back up system, matching machine specifications to the jobs site conditions, and reduces the risks involved in using a machine for a particular project. Study and analysis of the rock cutting process by disc cutters is very important since discs are the most common cutting tools used on hard rock TBMs. The previous studies on this subject have been instrumental in developing models for estimation of rock cutting forces needed for design analysis and optimization as well as performance prediction of machines such as TBMs. This paper, offers a brief review of the previous research works in this field including an overview of rock indentation by disc cutter, failure mechanism, important parameters influencing on the performance. Also, models developed for performance prediction of hard rock TBMs based on the cutting forces will be introduced along with empirical models used for an overall estimation of the machine rate of penetration in a given ground condition. The paper will finally review the current efforts underway by the authors for improving the accuracy of the models.