ABSTRACT:

Explorative tunnels are one of the best ways to assess the rock quality in the preliminary stage of a long tunnel excavation project: this strategy allows predictions for evaluating, with good accuracy, methods, costs, and technical problems related to the excavation of the main tunnel. The paper presents the first results of a study carried out on a 6.3 m diameter exploratory tunnel excavated in hard rock by TBM. A study of the data taken during the TBM's advancement is ongoing. The evaluation of the rock cutting efficiency, through the excavation specific energy (SE) is performed. A method for the evaluation of the grain size distribution of the muck produced during the excavation is proposed. The development of Matlab code to automatically analyze the machine acquired data in relation with the rock mass characteristics is reported in the paper.

1 INTRODUCTION

The tendency to carry out the excavation of exploratory tunnels has developed to optimize the design of the large cross-border tunnels in Central Europe. These tunnels allow to better understand the encountered geological constrains decreasing the uncertainty linked to the strong lithological heterogeneity often affecting the long tunnel paths. Furthermore, the exploratory tunnels are excavated with the same technique chosen for the base tunnel, allowing to accurately assess the technical difficulties and consequently the methods, times and costs of realization of the work. Finally, the exploratory tunnels will serve as the service tunnel (ventilation, maintenance, rescue etc.) of the main tunnel. This option turns out to be even more appropriate in the case of excavation by means of TBMs, both for the choice/design of the machine to be used and for forecasting the performance during excavation. In this regard, several methods for predicting the performance of a TBM are available in technical literature, including empirical methods (Bruland 1998, Barton 2000), deterministic - theoretical methods (eg Roxborough & Phillips 1975, Rostami & Ozdemir 1993), probabilistic methods (eg Nelson et al. 1999, Copur et al. 2014), or methods based on laboratory (eg Roxborough & Rispin 1973, Copur et al. 2001) and on site testing with real machines; in order to obtain reliable results it is advisable to use more than one quoted method (Bilgin et al. 2014).

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