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    Selection of regression models for predicting strength and deformability properties of rocks using GA

    Access Status
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    Authors
    Manouchehrian, A.
    Sharifzadeh, Mostafa
    Hamidzadeh, M.
    Nouri, T.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Manouchehrian, 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.
    Source Title
    International Journal of Mining Science and Technology
    DOI
    10.1016/j.ijmst.2013.07.006
    ISSN
    2095-2686
    URI
    http://hdl.handle.net/20.500.11937/18046
    Collection
    • Curtin Research Publications
    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.

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