Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

International Journal of Mining Science and Technology

Volumn 22, Issue 2, 2012, Pages 229-236

  Manouchehrian, Amin ^a   Sharifzadeh, Mostafa ^b   Moghadam, Rasoul Hamidzadeh ^a  

^a SAHAND UNIVERSITY OF TECHNOLOGY   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural networks; Multivariate statistics; Predictive models; Textural characteristics; Uniaxial compressive strength

Indexed keywords

BIVARIATE CORRELATIONS; CEMENT TYPE; DATA SETS; DESCRIPTIVE STATISTICS; GRAIN SIZE; IN-SITU TEST; INTACT ROCKS; MECHANICAL PARAMETERS; MINERAL CONTENT; MULTIVARIATE REGRESSION; MULTIVARIATE STATISTICAL MODELS; MULTIVARIATE STATISTICS; PREDICTIVE MODELS; ROCK ENGINEERING; STATISTICAL MODELS; STATISTICAL TECHNIQUES; TEXTURAL CHARACTERISTIC; UNIAXIAL COMPRESSIVE STRENGTH; YOUNG MODULUS;

CEMENTS; COMPRESSIVE STRENGTH; CURVE FITTING; MICA; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; ROCKS; SANDSTONE; STATISTICS;

MULTIVARIANT ANALYSIS;

EID: 84860721056     PISSN: 20952686     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmst.2011.08.013     Document Type: Article

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