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dc.contributor.authorManouchehrian, A.
dc.contributor.authorSharifzadeh, Mostafa
dc.contributor.authorHamidzadeh, M.
dc.contributor.authorNouri, T.
dc.date.accessioned2017-01-30T12:05:43Z
dc.date.available2017-01-30T12:05:43Z
dc.date.created2014-01-13T20:01:10Z
dc.date.issued2013
dc.identifier.citationManouchehrian, Amin and Sharifzadeh, Mostafa and Hamidzadeh, Moghadam Rasoul and Nouri, Tohid. 2013. Selection of regression models for predicting strength and deformability properties of rocks using GA. International Journal of Mining Science and Technology. 23: pp. 495-501.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/18046
dc.identifier.doi10.1016/j.ijmst.2013.07.006
dc.description.abstract

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

dc.publisherElsevier
dc.titleSelection of regression models for predicting strength and deformability properties of rocks using GA
dc.typeJournal Article
dcterms.source.volume23
dcterms.source.startPage495
dcterms.source.endPage501
dcterms.source.issn2095-2686
dcterms.source.titleInternational Journal of Mining Science and Technology
curtin.department
curtin.accessStatusFulltext not available


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