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Optimization of SVR functions for flyrock evaluation in mine blasting operations

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Abstract

This study introduces a new model to determine the critical flyrock event in mines. The flyrock was predicted and optimized using a field database including six parameters and 240 blasting events. The human learning optimization (HLO) algorithm was used in this research to optimize the support vector regression (SVR) function. Given different coefficients of kernels, optimization process minimized the likelihood of error in the models, allowing them to be detected and performed with the greatest precision. This procedure was repeated until the best model was discovered. Eventually, the radial basis function kernel was chosen for evaluating flyrock because it received the lowest computational error and the highest model accuracy. This model provided coefficient of determination (R2) = 0.9372 and R2 = 0.9294, respectively, as the accuracy for training and testing results. This function was considered as a relationship that the HLO algorithm could use to find the best options (i.e., optimal condition) under various conditions. The findings for 14 cases that are the essential examples in this study indicated that the optimal states are found with a great precision. The variation of the results obtained from optimization with real values is less than 5%. This demonstrates that a suitable model can be developed by employing the HLO algorithm in the development of the predictive related models to blasting and rock mechanics.

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Acknowledgements

This research was funded by the Fundamental Research Funds for the Central Universities (Grant No. 2021QN1006), National Natural Science Foundation of China (Grant No. 52108426), the Faculty Start-up Grant of China University of Mining and Technology (Grant No. 102520282) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20210513).

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Authors and Affiliations

  1. School of Civil Engineering, Guangzhou University, Guangzhou, 510006, China

    Jiandong Huang

  2. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Jiandong Huang & Junhua Xue

  3. College of Safety Science and Engineering, Xi’an University of Science and Technology, 710054, Xi’an, China

    Junhua Xue

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  1. Jiandong Huang
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Huang, J., Xue, J. Optimization of SVR functions for flyrock evaluation in mine blasting operations. Environ Earth Sci 81, 434 (2022). https://doi.org/10.1007/s12665-022-10523-5

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