Monitor-While-Drilling - based estimation of rock mass rating with computational intelligence: the case of tunnel excavation front
| Autor: | Galende Hernández, Marta, Fuente Aparicio, María Jesús de la, Sáinz Palmero, Gregorio Ismael, Menéndez, Manuel |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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