Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm
When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the...
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| Format: | Article |
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Springer-Verlag London Ltd
2021
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| _version_ | 1796856816710189056 |
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| author | Zhou, Jian Guo, Hongquan Koopialipoor, Mohammadreza Armaghani, Danial Jahed M. Tahir, M. |
| author_facet | Zhou, Jian Guo, Hongquan Koopialipoor, Mohammadreza Armaghani, Danial Jahed M. Tahir, M. |
| author_sort | Zhou, Jian |
| collection | ePrints |
| description | When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated. |
| first_indexed | 2024-03-05T18:48:45Z |
| format | Article |
| id | utm.eprints-31075 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:48:45Z |
| publishDate | 2021 |
| publisher | Springer-Verlag London Ltd |
| record_format | dspace |
| spelling | utm.eprints-310752022-01-31T08:41:48Z http://eprints.utm.my/31075/ Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm Zhou, Jian Guo, Hongquan Koopialipoor, Mohammadreza Armaghani, Danial Jahed M. Tahir, M. TA Engineering (General). Civil engineering (General) When working on underground projects, especially where ground is burst prone, it is of a high significance to accurately predict the risk of rockburst. The present paper integrates the firefly algorithm (FA) and artificial neural network (ANN) aiming at modeling the complex relationship between the rockburst risk in deep mines and tunnels and factors effective on this phenomenon. The model was established and validated through the use of a data set extracted from previously conducted studies. The data set involves a total of 196 reliable rockburst cases. The use of smart systems was used to classify and determine patterns in this research using model development. The hybrid FA–ANN model provides a solution for determining different classes of hazard under different conditions. The capability of these developed systems was implemented to determine the four types of levels defined for this phenomenon. The results of these systems led to new solutions to classify this phenomenon by success rates. Each system, given its performance, yields a unique error. Finally, by combining the number of correctly classified classes and their error values, the success rates in the classification of rockburst phenomena in mines and underground tunnels were evaluated. Springer-Verlag London Ltd 2021-07 Article PeerReviewed Zhou, Jian and Guo, Hongquan and Koopialipoor, Mohammadreza and Armaghani, Danial Jahed and M. Tahir, M. (2021) Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm. Engineering with Computers, 37 (3). pp. 1679-1694. ISSN 0177-0667 http://dx.doi.org/10.1007/s00366-019-00908-9 DOI:10.1007/s00366-019-00908-9 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Zhou, Jian Guo, Hongquan Koopialipoor, Mohammadreza Armaghani, Danial Jahed M. Tahir, M. Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title | Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title_full | Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title_fullStr | Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title_full_unstemmed | Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title_short | Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| title_sort | investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm |
| topic | TA Engineering (General). Civil engineering (General) |
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