A review of deterministic approaches in the performance evaluation of Raise Boring Machines (RBMs)

The raise boring technology was initially developed to meet the requirements of the underground mining sector. However, it was also found extensive application in infrastructure projects, including tunnelling for ventilation and the excavation of deep shafts. The selection and precise performance prediction of Raise Boring Machines (RBMs) during the feasibility (planning) stage of a shaft excavation project are crucial for project budgeting and scheduling. The ability to accurately predict and optimize RBM performance leads to more realistic planning that reduces overall shaft excavation costs. Deterministic approaches are one of the most widely used methods to predict the performance of mechanical miners such as RBMs. Deterministic models offer a more detailed and precise approach to performance prediction by considering various parameters. However, they rely heavily on accurate and adequate input data and initial conditions to produce reliable outputs. This paper aims to review the deterministic models used for predicting the operational and performance parameters of RBMs. The applicability of the available models is discussed and examples are presented for field validation.