2026-04-14T18:51:11Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/456032025-01-27T08:19:50Zcom_10324_1168com_10324_931com_10324_894col_10324_1304
Galende Hernández, Marta
Fuente Aparicio, María Jesús de la
Sáinz Palmero, Gregorio Ismael
Menéndez, Manuel
2021-03-09T19:39:15Z
2021-03-09T19:39:15Z
2018
XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), XIX Congreso Español sobre Tecnologías y Lógica Fuzzy, 23-26 Octubre, 2018, Granada, España. p. 231-232
978-84-09-05643-9
http://uvadoc.uva.es/handle/10324/45603
Producción Científica
The construction of tunnels has serious geomechan-ical uncertainties involving matters of both safety and budget.Nowadays, modern machinery gathers very useful informationabout the drilling process: the so-called Monitor While Drilling(MWD) data. So, one challenge is to provide support for thetunnel construction based on thison-sitedata .Here, an MWD based methodology to support tunnel con-struction is introduced: a Rock Mass Rating (RMR) estimationis provided by an MWD rocky based characterization of theexcavation front and expert knowledge [1].Well-known machine learning (ML) and computational intel-ligence (CI) techniques are used. In addition, a collectible and“interpretable”base of knowledge is obtained, linking MWDcharacterized excavation fronts and RMR.The results from a real tunnel case show a good and serviceableperformance: the accuracy of the RMR estimations is high,Errortest∼=3%, using a generated knowledge base of 15 fuzzyrules, 3 linguistic variables and 3 linguistic terms.This proposal is, however, is open to new algorithms toreinforce its performance
Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R.
2p
application/pdf
eng
F. Herrera et. al (Eds.)
info:eu-repo/semantics/openAccess
Tunneling
RMR
Sofcomputing
Machine learning
SDBR
Monitor-While-Drilling - based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front
XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), XIX Congreso Español sobre Tecnologías y Lógica Fuzzy
info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
https://sci2s.ugr.es/caepia18/proceedings/proceedings.php#ESTYLF