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Tunnel Blasting Parameters Based on Deep Learning and Multi-objective Optimization
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Ling-zhi XI1, Shuang-shuang GE2, Chen LI1, Wei GAO2, *, Qiang ZHANG1, Shao-bin HU2, Huai YANG1, Xin CHEN2, Zhi-hao ZHAO2
Science Technology and Engineering | 2025, 25(21) : 8841 - 8850
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Science Technology and Engineering | 2025, 25(21): 8841-8850
• Papers·Mining and Metallurgical Engineering •
Tunnel Blasting Parameters Based on Deep Learning and Multi-objective Optimization
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Ling-zhi XI1, Shuang-shuang GE2, Chen LI1, Wei GAO2, *, Qiang ZHANG1, Shao-bin HU2, Huai YANG1, Xin CHEN2, Zhi-hao ZHAO2
Affiliations
  • 1 PowerChina Huadong Engineering Corporation Limited, Hangzhou 310022, China
  • 2 School of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Published: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2407878
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Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.

tunnel blasting excavation  /  deep learning model  /  crown subsidence  /  overbreak and underbreak  /  multi-objective optimization
Ling-zhi XI, Shuang-shuang GE, Chen LI, Wei GAO, Qiang ZHANG, Shao-bin HU, Huai YANG, Xin CHEN, Zhi-hao ZHAO. Tunnel Blasting Parameters Based on Deep Learning and Multi-objective Optimization[J]. Science Technology and Engineering, 2025 , 25 (21) : 8841 -8850 . DOI: 10.12404/j.issn.1671-1815.2407878
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Year 2025 volume 25 Issue 21
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Article Info
doi: 10.12404/j.issn.1671-1815.2407878
  • Receive Date：2024-10-23
  • Online Date：2026-01-13
  • Published：2025-07-28
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History
  • Received：2024-10-23
  • Revised：2025-04-15
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Affiliations
    1 PowerChina Huadong Engineering Corporation Limited, Hangzhou 310022, China
    2 School of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
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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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