Abstract
Abstract The construction of tunnels has serious geomechanical uncertainties involving matters of both safety and budget. Nowadays, modern machinery gathers very useful information about the drilling process: the so-called Monitor While Drilling (MWD) data. So, one challenge is to provide support for the tunnel construction based on this on-site data. Here, an MWD based methodology to support tunnel construction is introduced: a Rock Mass Rating (RMR) estimation is provided by an MWD rocky based characterization of the excavation front and expert knowledge. Well-known machine learning (ML) and computational intelligence (CI) techniques are used. In addition, a collectible and “interpretable” base of knowledge is obtained, linking MWD characterized excavation fronts and RMR. The results from a real tunnel case show a good and serviceable performance: the accuracy of the RMR estimations is high, Errortest≅3%, using a generated knowledge base of 15 fuzzy rules, 3 linguistic variables and 3 linguistic terms. This proposal is, however, is open to new algorithms to reinforce its performance.
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