With tunnelling projects being part of critical infrastructural development, it is of uppermost importance to schedule the construction for an on-time delivery. Specifically, a reliable drillability prediction is indispensable to forecast advancement rates adequately. With the drillability being a very complex entity, predicting advancement rates is challenging, potentially leading to extensive additional cost and operational delays. Based on a case study from an Austrian tunnelling project, various influencing factors were examined. Comparing predicted with actual drilling velocities, it was shown that the actual drilling rates remained behind the expectations by about 20 %. The analysis clearly shows that the geological conditions are not responsible for these shortcomings, but the working process and the machine parameters including the degree of automation being significant factors of influence.
The term excavatability is not strictly defined. In general, it describes the mechanic operation of breaking loose or excavating soil or rock. Specifically for tunnelling operations the term excavatability can be narrowed down to the drillability and the blastability for conventional tunnelling and the cuttability for mechanized tunnelling methods with roadheaders and TBMs. For all these different methods, the excavatability manifests itself in two different criteria: the performance criterion and the material criterion. These two criteria can be quantified on a construction site by measuring the drilling velocity and the usage of steel parts, which is a simple task. However, associating the measured values with the underlying influencing parameters is more complicated and challenging, as a multitude of influencing factors exist and interact with one another. These influencing factors on the drillability can be subdivided into three main parameters according to Thuro (1997) as shown in Figure 1. Over the past decades the influence of geological parameters has been analyzed in depth and their role for performance prediction models has been progressively refined. For the other two groups of influencing factors – the working process and machine parameters – it is more challenging putting the different aspects into numbers for forecasting and planning. In particular, the ongoing improvement of the drilling tools has been overlooked in the past, leading to shortcomings in performance predictions.
