Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front

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.