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Blasting Vibration Prediction Based on Novel HGS-ANN Model
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Xinyu WANG1, Pengfei CAO1, Yiqing XIAO1, Guoquan XU2
Mining and Metallurgical Engineering | 2024, 44(4) : 159 - 163
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Mining and Metallurgical Engineering | 2024, 44(4): 159-163
• MINING •
Blasting Vibration Prediction Based on Novel HGS-ANN Model
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Xinyu WANG1, Pengfei CAO1, Yiqing XIAO1, Guoquan XU2
Affiliations
  • 1.Hebei Iron & Steel Group Mining Co., Ltd., Tangshan 063000, Hebei, China
  • 2.School of Earth Sciences, East China University of Technology, Nanchang 330000, Jiangxi, China
Published: 2024-08-01 doi: 10.3969/j.issn.0253-6099.2024.04.030
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Based on the combination of the hunger games search (HGS) algorithm and the artificial neural network (ANN), a new hybrid model of HGS-ANN was developed to predict blasting vibration. Four different prediction models were established based on group method of data handling (GMDH), support vector machines (SVM), ANN and Sadov's empirical formula, and compared with HGS-ANN model in evaluating the performance of models. For this purpose, 32 sets of blasting data of an open-pit mine were collected.7 independent variables, including detonation distance, maximum single-stage charge, total charge, burden, hole spacing, number of holes and hole depth were selected as inputs, while the particle vibration velocity was selected as the output. With the root-mean-square error (RMSE) and the decisive factor (R2) as the evaluating indicators, the established models was compared in terms of their performances. The results show that the HGS-ANN model, with the RMSE and R2 of 0.833 and 0.963, respectively, has performance better than the other four models. It is proposed that the HGS-ANN model can be used as an auxiliary tool to optimize the blasting design for reducing the blasting-induced seismic effect.

blasting vibration  /  hunger games search algorithm  /  artificial neural network  /  vibration prediction
Xinyu WANG, Pengfei CAO, Yiqing XIAO, Guoquan XU. Blasting Vibration Prediction Based on Novel HGS-ANN Model[J]. Mining and Metallurgical Engineering, 2024 , 44 (4) : 159 -163 . DOI: 10.3969/j.issn.0253-6099.2024.04.030
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Article Info
doi: 10.3969/j.issn.0253-6099.2024.04.030
  • Receive Date：2024-02-23
  • Online Date：2026-03-18
  • Published：2024-08-01
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  • Received：2024-02-23
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Affiliations
    1.Hebei Iron & Steel Group Mining Co., Ltd., Tangshan 063000, Hebei, China
    2.School of Earth Sciences, East China University of Technology, Nanchang 330000, Jiangxi, China
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Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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