2026-04-14T15:58:46Zhttps://uvadoc.uva.es/oai/request

oai: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 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 eng info:eu-repo/semantics/openAccess Monitor-While-Drilling - based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front info:eu-repo/semantics/conferenceObject