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Prediction model of blasting fragmentation based on GA-QLightGBM quantile regression
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Shuxian WANG1, Yi YANG**, 1, Yulian SHI2, Yaxi SHEN3
China Safety Science Journal | 2026, 36(2) : 163 - 171
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China Safety Science Journal | 2026, 36(2): 163-171
Safety Technology and Engineering
Prediction model of blasting fragmentation based on GA-QLightGBM quantile regression
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Shuxian WANG1, Yi YANG**, 1, Yulian SHI2, Yaxi SHEN3
Affiliations
  • 1Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming Yunnan 650032, China
  • 2School of Land and Resource Engineering, Kunming University of Science and Technology, Kunming Yunnan 650032, China
  • 3School of Architecture and Engineering, Tianjin University, Tianjin 300072, China
Published: 2026-02-28 doi: 10.16265/j.cnki.issn1003-3033.2026.02.0434
Outline
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To address the challenges of high uncertainty and complex influencing factors in predicting blast fragmentation in mining operations, this study proposed a LightGBM prediction model GA-QLightGBM that integrated GA optimisation with quantile regression. First, GA was employed to optimise the hyperparameters of LightGBM by simulating the natural selection process (selection, crossover, and mutation), thereby improving the model's predictive accuracy and stability. Then, different quantiles were set to construct prediction intervals for blast fragmentation, enabling the quantification of prediction uncertainty. Finally, the proposed model was applied to mine field datasets to verify its predictive performance and generalisation ability, providing an effective approach for blast fragmentation prediction and uncertainty analysis. The results show that the model achieves a coefficient of determination (R2) of 0.880 and a mean squared error (MSE) of 0.004 in point prediction, outperforming traditional point prediction models. In interval prediction, the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and corrected prediction interval accuracy (CPIA) are 0.947, 0.228, and 0.762, respectively, confirming the accuracy and reliability of GA-QLightGBM model. These findings offer a practical framework for quantifying the uncertainty of blast fragmentation, supporting refined blast design and risk control in mining engineering.

genetic algorithm (GA)  /  light gradient boosting machine (LightGBM)  /  blasting fragmentation  /  uncertainty  /  quantile regression  /  prediction model
Shuxian WANG, Yi YANG, Yulian SHI, Yaxi SHEN. Prediction model of blasting fragmentation based on GA-QLightGBM quantile regression[J]. China Safety Science Journal, 2026 , 36 (2) : 163 -171 . DOI: 10.16265/j.cnki.issn1003-3033.2026.02.0434
Year 2026 volume 36 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.02.0434
  • Receive Date:2025-09-10
  • Online Date:2026-07-08
  • Published:2026-02-28
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  • Received:2025-09-10
  • Revised:2025-12-10
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Affiliations
    1Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming Yunnan 650032, China
    2School of Land and Resource Engineering, Kunming University of Science and Technology, Kunming Yunnan 650032, China
    3School of Architecture and Engineering, Tianjin University, Tianjin 300072, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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