Extending the Q system's prediction of support in tunnels employing fuzzy logic and extra parameters

Tzamos, S; Sofianos, AI

dc.contributor.author	Tzamos, S	en
dc.contributor.author	Sofianos, AI	en
dc.date.accessioned	2014-03-01T01:24:24Z
dc.date.available	2014-03-01T01:24:24Z
dc.date.issued	2006	en
dc.identifier.issn	1365-1609	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17243
dc.subject	Classification	en
dc.subject	Fuzzy logic	en
dc.subject	Q system	en
dc.subject	Rock mass	en
dc.subject	Tunnel support	en
dc.subject.classification	Engineering, Geological	en
dc.subject.classification	Mining & Mineral Processing	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Numerical analysis	en
dc.subject.other	Rocks	en
dc.subject.other	Statistical methods	en
dc.subject.other	Strength of materials	en
dc.subject.other	Fuzzy expert system	en
dc.subject.other	Rock mass classification	en
dc.subject.other	Tunnels	en
dc.subject.other	computer simulation	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	Q system	en
dc.subject.other	rock mass classification	en
dc.subject.other	support	en
dc.subject.other	tunnel	en
dc.subject.other	underground construction	en
dc.title	Extending the Q system's prediction of support in tunnels employing fuzzy logic and extra parameters	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.ijrmms.2006.02.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.ijrmms.2006.02.002	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Rock mass classifications predict support measures according to expert rules by rating rock mass and taking into account the span of the opening. A similar procedure is adopted, in this work, and computerized using statistics and fuzzy logic. Fuzzy expert systems are trained with data of previously constructed underground openings. Using subtractive clustering the systems have the intelligence to pick up the relations between input and output and define the rules that represent the system's behavior automatically. These systems are found to predict support to be used more successfully than the Q system. With the introduction of extra input variables, which are important in numerical analysis, such as depth and intact rock strength, an extended fuzzy system is developed. This system is suggested for preliminary use as it is able to predict support even better. (c) 2006 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	International Journal of Rock Mechanics and Mining Sciences	en
dc.identifier.doi	10.1016/j.ijrmms.2006.02.002	en
dc.identifier.isi	ISI:000238823800008	en
dc.identifier.volume	43	en
dc.identifier.issue	6	en
dc.identifier.spage	938	en
dc.identifier.epage	949	en