Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique
This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 da...
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| Format: | Article |
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Springer Verlag
2016
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| _version_ | 1796861905788207104 |
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| author | Faradonbeh, R. S. Jahed Armaghani, D. Monjezi, M. |
| author_facet | Faradonbeh, R. S. Jahed Armaghani, D. Monjezi, M. |
| author_sort | Faradonbeh, R. S. |
| collection | ePrints |
| description | This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2Â =Â 0.908, RMSEÂ =Â 17.638 and VAFÂ =Â 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively. |
| first_indexed | 2024-03-05T20:03:30Z |
| format | Article |
| id | utm.eprints-72282 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:03:30Z |
| publishDate | 2016 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | utm.eprints-722822017-11-23T04:17:44Z http://eprints.utm.my/72282/ Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique Faradonbeh, R. S. Jahed Armaghani, D. Monjezi, M. TA Engineering (General). Civil engineering (General) This research was aimed at developing a new model to predict flyrock distance based on a genetic programming (GP) technique. For this purpose, six granite quarry mines in the Johor area of Malaysia were investigated, for which various controllable blasting parameters were recorded. A total of 262 datasets consisting of six variables (i.e., powder factor, stemming length, burden-to-spacing ratio, blast-hole diameter, maximum charge per delay, and blast-hole depth) were collected applied to developing the flyrock predictive model. To identify the optimum model, several GP models were developed to predict flyrock. In the same way, using non-linear multiple regression (NLMR) analysis, various models were established to predict flyrock. Finally, to compare the performance of the developed models, regression coefficient (R2), root mean square error (RMSE), variance account for (VAF), and simple ranking methods were computed. According to the results obtained from the test dataset, the best flyrock predictive model was found to be the GP based model, with R2Â =Â 0.908, RMSEÂ =Â 17.638 and VAFÂ =Â 89.917, while the corresponding values for R2, RMSE and VAF for the NLMR model were 0.816, 26.194, and 81.041, respectively. Springer Verlag 2016 Article PeerReviewed Faradonbeh, R. S. and Jahed Armaghani, D. and Monjezi, M. (2016) Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bulletin of Engineering Geology and the Environment, 75 (3). pp. 993-1006. ISSN 1435-9529 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962175361&doi=10.1007%2fs10064-016-0872-8&partnerID=40&md5=b5067cebe2051f19367df3ea5de7ce3b |
| spellingShingle | TA Engineering (General). Civil engineering (General) Faradonbeh, R. S. Jahed Armaghani, D. Monjezi, M. Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title | Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title_full | Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title_fullStr | Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title_full_unstemmed | Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title_short | Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique |
| title_sort | development of a new model for predicting flyrock distance in quarry blasting a genetic programming technique |
| topic | TA Engineering (General). Civil engineering (General) |
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