Selection of regression models for predicting strength and deformability properties of rocks using GA
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/18046 |
| _version_ | 1848749632146898944 |
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| author | Manouchehrian, A. Sharifzadeh, Mostafa Hamidzadeh, M. Nouri, T. |
| author_facet | Manouchehrian, A. Sharifzadeh, Mostafa Hamidzadeh, M. Nouri, T. |
| author_sort | Manouchehrian, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:24:01Z |
| format | Journal Article |
| id | curtin-20.500.11937-18046 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:24:01Z |
| publishDate | 2013 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-180462017-09-13T15:44:54Z Selection of regression models for predicting strength and deformability properties of rocks using GA Manouchehrian, A. Sharifzadeh, Mostafa Hamidzadeh, M. Nouri, T. 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. 2013 Journal Article http://hdl.handle.net/20.500.11937/18046 10.1016/j.ijmst.2013.07.006 Elsevier restricted |
| spellingShingle | Manouchehrian, A. Sharifzadeh, Mostafa Hamidzadeh, M. Nouri, T. Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title | Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title_full | Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title_fullStr | Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title_full_unstemmed | Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title_short | Selection of regression models for predicting strength and deformability properties of rocks using GA |
| title_sort | selection of regression models for predicting strength and deformability properties of rocks using ga |
| url | http://hdl.handle.net/20.500.11937/18046 |