2026-05-05T18:36:11Zhttps://uvadoc.uva.es/oai/requestoai: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
Producción Científica
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.
Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R.
application/pdf
eng
Elsevier
info:eu-repo/semantics/restrictedAccess
Elsevier
Tunnels
MWD
FRBS
Selection/extraction of features
Clustering
RMR
Decision making
Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front
info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
https://www.sciencedirect.com/science/article/abs/pii/S092658051630454X
SI