- Title
- Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms
- Creator
- Li, Enming; Yang, Fenghao; Ren, Meiheng; Zhang, Xiliang; Zhou, Jian; Khandelwal, Manoj
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/182600
- Identifier
- vital:16143
- Identifier
-
https://doi.org/10.1016/j.jrmge.2021.07.013
- Identifier
- ISBN:1674-7755 (ISSN)
- Abstract
- The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Publisher
- Chinese Academy of Sciences
- Relation
- Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
- 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-nc-nd/4.0/
- Rights
- Copyright @ 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Rights
- Open Access
- Subject
- 4005 Civil Engineering; 4019 Resources Engineering and Extractive Metallurgy; Blasting mean fragment size; E-support vector regression (e-SVR); Intelligent prediction; Meta-heuristic algorithms; V-support vector regression (v-SVR)
- Full Text
- Reviewed
- Funder
- This research was funded by the National Natural Science Foundation of China (Grant No. 42177164 ) and the Innovation-Driven Project of Central South University (Grant No. 2020CX040 ). The first author is supported by China Scholarship Council (Grant No. 202006370006 ).
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