Volume 14 Issue 1
Mar.  2023
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Xia Li, Yuewen Xiao, Jiaxuan Li, Haipeng Wang, Eryong Chuo, Haili Bai. Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse[J]. International Journal of Disaster Risk Science, 2023, 14(1): 113-126. doi: 10.1007/s13753-023-00470-5
Citation: Xia Li, Yuewen Xiao, Jiaxuan Li, Haipeng Wang, Eryong Chuo, Haili Bai. Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse[J]. International Journal of Disaster Risk Science, 2023, 14(1): 113-126. doi: 10.1007/s13753-023-00470-5

Special Emergency Resources Preallocation Concerning Demand Time for Tunnel Collapse

doi: 10.1007/s13753-023-00470-5
Funds:

The authors are grateful to the editor and anonymous reviewers for their critical comments and valuable suggestions, which helped to improve the quality of this manuscript substantially. This work was supported by the funding provided by the National Natural Science Foundation of China (Grant no. 51908187).

  • Accepted Date: 2023-01-21
  • Publish Date: 2023-02-15
  • Lacking timely access to rescue resources is one of the main causes of casualties in tunnel collapse. To provide timely rescue, this study proposed a multi-objective preallocation model of special emergency resources for tunnel collapse based on demand time. Efficiency, multiple coverage, and cost-effectiveness are taken as the key objectives of the model; the demand time service range is used as a coverage decision factor considering the unique nature of tunnel collapse. The weight of potential disaster-affected points and other general factors are also considered in this model in order to thoroughly combine the distribution of disaster points and service areas. Further, we take 15 main tunnel projects under construction in China as an example. When the relative proximity to the ideal point of the selected optimal scheme εa is smaller than 0.5, we will adjust the weight of three objectives and reselect the optimal scheme until εa > 0.5. Compared with the not preallocated case, the number of rescue rigs needed is reduced by 8.3%, the number of covered potential disaster-affected points is increased by 36.36%, the weighted coverage times are increased from 0.853 to 1.383, and the weighted distance is significantly reduced by 99% when the rescue rigs are preallocated, verifying the feasibility and superiority of the proposed model.
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