2026-05-05T10:18:54Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/455972025-01-27T08:20:27Zcom_10324_1168com_10324_931com_10324_894col_10324_1302

Galende Hernández, Marta Menéndez, Manuel Fuente Aparicio, María Jesús de la Sáinz Palmero, Gregorio Ismael 2021-03-09T17:56:49Z 2021-03-09T17:56:49Z 2018 Automation in construction, Septiembre 2018, vol.93, p. 325-338 0926-5805 http://uvadoc.uva.es/handle/10324/45597 10.1016/j.autcon.2018.05.019 325 338 Automation in construction 93 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. eng info:eu-repo/semantics/restrictedAccess Elsevier Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front info:eu-repo/semantics/article