Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests

0450214 - ÚGN 2016 RIV GB eng J - Journal Article
Mishra, A. Deepak - Srigyan, M. - Basu, A. - Rokade, P. J.
Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests.
International Journal of Rock Mechanics and Mining Sciences. Roč. 80, December 2015 (2015), s. 418-424. ISSN 1365-1609. E-ISSN 1873-4545
Institutional support: RVO:68145535
Keywords : uniaxial compressive strength * rock indices * fuzzy inference system * artificial neural network * adaptive neuro-fuzzy inference system
Subject RIV: DH - Mining, incl. Coal Mining
Impact factor: 2.010, year: 2015
https://www.sciencedirect.com/science/article/pii/S1365160915300708?via%3Dihub

Uniaxial compressive strength ( UCS ) is one of the most widely used rock mechanical parameters in rock engineering. Determi- nation of this parameter in the laboratory, however, requires quality rock specimens. The use of various index tests that require little or no specimen preparation and are easier to perform as well as less expensive than the uniaxial compression test has always been attractive in order to predict UCS of rock materials empirically 1 – 16 . Amongst different predictive models such as re- gression analyses, fuzzy inference system and neural network approaches ; regression analyses are commonly employed to es- tablish a predictive model to estimate UCS 17 . In the last decade or so, however, the use of soft computing methods (e.g. fuzzy in- ference system (FIS), arti fi cial neural network (ANN) and adaptive neuro – fuzzy inference system (ANFIS)) in order to establish pre- dictive models has also gained signi fi cant attention in the areas of rock mechanics and engineering geology. Several research works have dealt with the estimation of UCS and/or other intact rock properties from index test results using these soft computing methods 17 – 36 .
Permanent Link: http://hdl.handle.net/11104/0251587