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Engineering with Computers
Erschienen in: Engineering with Computers 3/2021

10.01.2020 | Original Article

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

verfasst von: Jian Zhou, Hongquan Guo, Mohammadreza Koopialipoor, Danial Jahed Armaghani, M. M. Tahir

Erschienen in: Engineering with Computers | Ausgabe 3/2021

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Abstract

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.
Nächster Artikel A hybrid sufficient performance measure approach to improve robustness and efficiency of reliability-based design optimization

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Metadaten
Titel
Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm
verfasst von
Jian Zhou
Hongquan Guo
Mohammadreza Koopialipoor
Danial Jahed Armaghani
M. M. Tahir
Publikationsdatum
10.01.2020
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 3/2021
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-019-00908-9

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