RT info:eu-repo/semantics/conferenceObject
T1 Monitor-While-Drilling - based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front
A1 Galende Hernández, Marta
A1 Fuente Aparicio, María Jesús de la
A1 Sáinz Palmero, Gregorio Ismael
A1 Menéndez, Manuel
K1 Tunneling
K1 RMR
K1 Sofcomputing
K1 Machine learning
K1 SDBR
AB 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
PB F. Herrera et. al (Eds.)
SN 978-84-09-05643-9
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/45603
UL http://uvadoc.uva.es/handle/10324/45603
LA eng
NO 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
NO Producción Científica
DS UVaDOC
RD 14-oct-2024