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Risk Assessment Model of Highway Tunnel Collapse Based on Rough Set-grid Search-support Vector Classification
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Bo WU1, Jia-jia ZENG2, *, Qi CAI1, Ruo-nan ZHU2, Cong LIU1
Science Technology and Engineering | 2025, 25(3) : 1245 - 1252
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Science Technology and Engineering | 2025, 25(3): 1245-1252
Papers·Traffics and Transportations
Risk Assessment Model of Highway Tunnel Collapse Based on Rough Set-grid Search-support Vector Classification
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Bo WU1, Jia-jia ZENG2, *, Qi CAI1, Ruo-nan ZHU2, Cong LIU1
Affiliations
  • 1. School of Civil and Architecture Engineering, East China University of Technology, Nanchang 330000, China
  • 2. School of Water Resources and Environment Engineering, East China University of Technology, Nanchang 330000, China
Published: 2025-01-28 doi: 10.12404/j.issn.1671-1815.2309307
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In order to reasonably and efficiently carry out the risk assessment of road tunnel construction collapse, the risk assessment model of road tunnel construction collapse was studied by rough set (RS), grid search method(GS) and support vector classification (SVC). Firstly, the index system of highway tunnel construction collapse risk evaluation was constructed by integrating advanced geological prediction. At the same time, the information of 100 tunnel collapse cases was collected and the index data was discretized. Secondly, attribute reduction was conducted based on the condition information entropy of rough set to obtain the reduced core index set. Then, grid search method was used to find the optimal parameters of the support vector classification training set, the risk assessment model of highway tunnel construction collapse based on rough set-grid search-support vector classification (RS-GS-SVC) was established. Finally, the model was used to predict the test samples. The results show that under the condition of the same learning sample, compared with rough set-genetic algorithm-support vector classification (RS-GA-SVC) model and Rough set-particle swarm optimization-support vector classification (RS-PSO-SVC) model, RS-GS-SVC model has higher classification accuracy; Under the same proportion of training set and test set, the prediction accuracy of RS-GS-SVC model is higher than that of GS-SVC model, with the accuracy rates of 93.33% and 90% respectively, and the operation time of RS-GS-SVC model is shorter. It can clearly be seen that the model complexity is effectively reduced and the classification accuracy is improved through the reduction of rough set conditional information entropy attributes.

highway tunnel collapse  /  support vector classification  /  rough set  /  grid search method  /  risk assessment
Bo WU, Jia-jia ZENG, Qi CAI, Ruo-nan ZHU, Cong LIU. Risk Assessment Model of Highway Tunnel Collapse Based on Rough Set-grid Search-support Vector Classification[J]. Science Technology and Engineering, 2025 , 25 (3) : 1245 -1252 . DOI: 10.12404/j.issn.1671-1815.2309307
Year 2025 volume 25 Issue 3
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Article Info
doi: 10.12404/j.issn.1671-1815.2309307
  • Receive Date:2023-11-27
  • Online Date:2025-07-29
  • Published:2025-01-28
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History
  • Received:2023-11-27
  • Revised:2024-07-09
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    1. School of Civil and Architecture Engineering, East China University of Technology, Nanchang 330000, China
    2. School of Water Resources and Environment Engineering, East China University of Technology, Nanchang 330000, 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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