RT info:eu-repo/semantics/article
T1 Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front
A1 Galende Hernández, Marta
A1 Menéndez, Manuel
A1 Fuente Aparicio, María Jesús de la
A1 Sáinz Palmero, Gregorio Ismael
K1 Tunnels
K1 MWD
K1 FRBS
K1 Selection/extraction of features
K1 Clustering
K1 RMR
K1 Decision making
AB 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 ofknowledge 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.
PB Elsevier
SN 0926-5805
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/45597
UL http://uvadoc.uva.es/handle/10324/45597
LA eng
NO Automation in construction, Septiembre 2018, vol.93, p. 325-338
NO Producción Científica
DS UVaDOC
RD 11-jul-2024