Abstract
Blast block prediction is a complex non-stationary, nonlinear problem, the contribution of factors affecting results varies for different external conditions. Studies in a single environment are not universally applicable, the establishment of a blasted block size prediction model with fusion of multiple algorithms and reliable prediction results is the most urgent problem to be solved. In this study, a method is proposed that applies to different regions and rock conditions. To achieve the grou** prediction of blasted block size, this study firstly used hierarchical clustering to cluster the data in different areas, then used the random forest to establish the data grou** discriminant model based on the grou** results, and used back propagation neural network with genetic algorithm (GA-BP) to establish the blasted block size prediction model for each group of the blasted block size data separately. The results of the study show that (1) the block size data with different properties can be grouped according to the elastic modulus; (2) the grou** discriminant model established can correctly group the data; (3) compared with the GA-BP neural network prediction model without grou**, this model has a higher coefficient of determination (\({R}^{2}=0.982\) ) and smaller root mean square error (\(\mathrm{RMSE}=0.2\)) and mean relative error (\(\mathrm{MRE}=4.857\) ), which verifies the correctness of grou** prediction according to the elastic modulus; (4) by comparing with multiple regression, least squares support vector machines (LSSVM), and BP neural networks, the model outperforms the other models in \({R}^{2}\)\(\mathrm{RMSE}\), and \(\mathrm{MRE}\), and the prediction results are more accurate.
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Acknowledgements
Appreciate is expressed to Shijiao Yang, Qinpeng Guo, Zhibin **ang. I would like to express my gratitude to all those who helped me during the writing of this thesis.
Funding
This study was financially supported by the National Natural Science Foundation of China (No.50974076), Postgraduate Scientific Research Innovation Project of Hunan Province (No. QL20210216), and Guangdong **yuan Blasting Technology Co., Ltd.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yuchen Wang, Shijiao Yang, Qinpeng Guo and Zhibin **ang. The first draft of the manuscript was written by Yuchen Wang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Wang, Y., Guo, Q., Yang, S. et al. A prediction model for blasted block size grou** based on HC and RF-GA-BP neural network. Arab J Geosci 15, 1391 (2022). https://doi.org/10.1007/s12517-022-10645-x
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DOI: https://doi.org/10.1007/s12517-022-10645-x