- Title
- Six novel hybrid extreme learning machine–swarm intelligence optimization (ELM–SIO) models for predicting backbreak in open-pit blasting
- Creator
- Li, Chuanqi; Zhou, Jian; Khandelwal, Manoj; Zhang, Xiliang; Monjezi, Masoud; Qiu, Yingui
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186885
- Identifier
- vital:16970
- Identifier
-
https://doi.org/10.1007/s11053-022-10082-3
- Identifier
- ISBN:1520-7439 (ISSN)
- Abstract
- Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. © 2022, The Author(s).
- Publisher
- Springer
- Relation
- Natural Resources Research Vol. 31, no. 5 (2022), p. 3017-3039
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022, The Author(s)
- Rights
- Open Access
- Subject
- 3705 Geology; 4019 Resources engineering and extractive metallurgy; Backbreak; Blasting; Extreme learning machine; Swarm intelligence optimization
- Full Text
- Reviewed
- Funder
- This research is partially supported by the National Natural Science Foundation Project of China (42177164) and the Innovation-Driven Project of Central South University (2020CX040). The first author was funded by China Scholarship Council (Grant No. 202106370038). Open Access funding enabled and organized by CAUL and its Member Institutions.
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