Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms
Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions and greenfield projects. While high prediction accuracy of over 90% has bee...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
SAGE Publications
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/98021 |
| _version_ | 1848766349243842560 |
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| author | Chimunhu, Prosper Shirani Faradonbeh, Roohollah Topal, Erkan Asad, Mohammad Waqar Ali Ajak, A. D. |
| author_facet | Chimunhu, Prosper Shirani Faradonbeh, Roohollah Topal, Erkan Asad, Mohammad Waqar Ali Ajak, A. D. |
| author_sort | Chimunhu, Prosper |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions and greenfield projects. While high prediction accuracy of over 90% has been achieved using machine learning algorithms, particularly artificial neural networks (ANNs), the studies mostly predicted the overall dilution of stopes or included performance-subjective determinants, such as drill and blast factors. These factors compromise the models’ reproducibility for extensional application to cover new mining projects that do not have historical drill and blast input. To address this, the study explores gene expression programming (GEP) and ANN with backpropagation (BPNN) to predict dilution on a per-stope granularity based on geotechnical and design data. A 138-stope sample from a sublevel open stoping gold mine operation in Western Australia was used to generate predictive models. Model and infield results showed that the GEP model performed better, with a coefficient of determination, R2, of 0.740 with a root mean square error (RMSE) of 0.361 compared to BPNN's 0.681 and 0.409, respectively. Accordingly, the GEP model is recommended for dilution prediction for mine planning and production scheduling at the prescribed level of accuracy. |
| first_indexed | 2025-11-14T11:49:44Z |
| format | Journal Article |
| id | curtin-20.500.11937-98021 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:44Z |
| publishDate | 2025 |
| publisher | SAGE Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-980212025-07-16T03:22:57Z Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms Chimunhu, Prosper Shirani Faradonbeh, Roohollah Topal, Erkan Asad, Mohammad Waqar Ali Ajak, A. D. Unplanned dilution in underground mining is detrimental to the business, as imprecise dilution factors may impair production forecasts for existing operations or the economic evaluation and viability of brownfield expansions and greenfield projects. While high prediction accuracy of over 90% has been achieved using machine learning algorithms, particularly artificial neural networks (ANNs), the studies mostly predicted the overall dilution of stopes or included performance-subjective determinants, such as drill and blast factors. These factors compromise the models’ reproducibility for extensional application to cover new mining projects that do not have historical drill and blast input. To address this, the study explores gene expression programming (GEP) and ANN with backpropagation (BPNN) to predict dilution on a per-stope granularity based on geotechnical and design data. A 138-stope sample from a sublevel open stoping gold mine operation in Western Australia was used to generate predictive models. Model and infield results showed that the GEP model performed better, with a coefficient of determination, R2, of 0.740 with a root mean square error (RMSE) of 0.361 compared to BPNN's 0.681 and 0.409, respectively. Accordingly, the GEP model is recommended for dilution prediction for mine planning and production scheduling at the prescribed level of accuracy. 2025 Journal Article http://hdl.handle.net/20.500.11937/98021 https://doi.org/10.1177/25726668251348707 http://creativecommons.org/licenses/by/4.0/ SAGE Publications fulltext |
| spellingShingle | Chimunhu, Prosper Shirani Faradonbeh, Roohollah Topal, Erkan Asad, Mohammad Waqar Ali Ajak, A. D. Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title | Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title_full | Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title_fullStr | Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title_full_unstemmed | Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title_short | Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| title_sort | dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms |
| url | http://hdl.handle.net/20.500.11937/98021 |