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Multi-objective prediction optimization for large-diameter slurry shield tunneling construction based on CatBoost-MOEAD
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Xianguo WU1, Jun LIU1, Feiming SU1, **, Hongyu CHEN2, Zongbao FENG1
China Safety Science Journal | 2024, 34(6) : 57 - 64
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China Safety Science Journal | 2024, 34(6): 57-64
Safety engineering technology
Multi-objective prediction optimization for large-diameter slurry shield tunneling construction based on CatBoost-MOEAD
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Xianguo WU1, Jun LIU1, Feiming SU1, **, Hongyu CHEN2, Zongbao FENG1
Affiliations
  • 1 School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan Hubei 430074,China
  • 2 Department of Building and Real Estate,The Hong Kong Polytechnic University,Hong Kong 999077,China
Published: 2024-06-28 doi: 10.16265/j.cnki.issn1003-3033.2024.06.1734
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To effectively optimize the shield construction parameters and achieve the goals of safety,efficiency,and energy-saving in the large-diameter slurry shield tunneling process,a hybrid intelligent algorithm combining categorical boosting (CatBoost) and decomposition was proposed based on a multi-objective evolutionary algorithm (MOEAD). The main shield construction parameters were set as the major research objects considering shield construction parameters and geological conditions,and the surface settlement,penetration rate,and tunneling-specific energy were determined as the prediction and control objectives. Moreover,the selected shield construction parameters were optimized,and a line of Wuhan rail transit was used to validate the hybrid algorithm performance. The results showed that the proposed CatBoost algorithm had great prediction performance for large-diameter slurry shields with the fitting accuracy (R2) of the three control objectives more than 0.9. The model's importance rank indicated that the total propulsion force and propulsion speed of the large-diameter slurry shield had significant influences on surface settlement,penetration,and tunneling-specific energy. The proposed CatBoost-MOEAD hybrid intelligent algorithm had an obvious optimization effect on the three control objectives,and the optimization ranges of surface settlement,penetration rate,and tunneling-specific energy reached 12.35%,7.47%,and 10.70%,respectively. Moreover,the control ranges of corresponding shield construction parameters were presented.

large-diameter slurry shield  /  categorical boosting (CatBoost)  /  multi-objective evolutionary algorithm based on decomposition (MOEAD)  /  multi-objective optimization  /  surface settlement
Xianguo WU, Jun LIU, Feiming SU, Hongyu CHEN, Zongbao FENG. Multi-objective prediction optimization for large-diameter slurry shield tunneling construction based on CatBoost-MOEAD[J]. China Safety Science Journal, 2024 , 34 (6) : 57 -64 . DOI: 10.16265/j.cnki.issn1003-3033.2024.06.1734
Year 2024 volume 34 Issue 6
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doi: 10.16265/j.cnki.issn1003-3033.2024.06.1734
  • Receive Date:2023-12-08
  • Online Date:2025-07-09
  • Published:2024-06-28
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  • Received:2023-12-08
  • Revised:2024-03-21
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Affiliations
    1 School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan Hubei 430074,China
    2 Department of Building and Real Estate,The Hong Kong Polytechnic University,Hong Kong 999077,China
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表12种不同金属材料的力学参数

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Number of
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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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