Research on the evaluation of the resilience of subway station projects to waterlogging disasters based on the projection pursuit model

Lanjun Liu; Han Wu; Junwu Wang; Tingyou Yang; Lanjun Liu; Han Wu; Junwu Wang; Tingyou Yang

doi:10.3934/mbe.2020374

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 6: 7302-7331. doi: 10.3934/mbe.2020374

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Research on the evaluation of the resilience of subway station projects to waterlogging disasters based on the projection pursuit model

1.
School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430070, China
2.
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
3.
CCTEB Infrastructure Construction Investment Co., Ltd, Wuhan 430070, China

Received: 19 August 2020 Accepted: 21 October 2020 Published: 27 October 2020

To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a projection pursuit model (PPM) improved by quantum particle swarm optimization (QPSO) for the evaluation of the resilience of subway station projects to waterlogging disasters. In view of the lack of research results related to the evaluation of the resilience of subway station projects to waterlogging disasters, 16 secondary indicators that affected the ability of subway station projects to recover from waterlogging disasters were identified from defense, recovery, and adaptability, for the first time. A PPM improved by QPSO was then proposed to effectively deal with the high-dimensional data about the resilience of subway station projects to waterlogging disasters. The QPSO was used to solve the best projection vector of the PPM, and interpolation algorithm was used to construct the mathematical model of evaluation. Finally, four station projects of Chengdu Metro Line 11 in China were selected for a case study analysis. The case study revealed that, among the secondary indicators, the emergency plan of construction order, the exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2, 1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the actual disaster situation in the flood season of 2019. In addition, the case study results showed that QPSO had the advantages of fewer parameter settings and a faster convergence speed as compared with PSO and the genetic algorithm.
- subway station project,
- waterlogging disasters,
- resilience capability,
- projection pursuit model,
- quantum particle swarm optimization
Citation: Lanjun Liu, Han Wu, Junwu Wang, Tingyou Yang. Research on the evaluation of the resilience of subway station projects to waterlogging disasters based on the projection pursuit model[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7302-7331. doi: 10.3934/mbe.2020374

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Abstract

To improve sustainable development, increasingly more attention has been paid to the evaluation of the resilience to waterlogging disasters. This paper proposed a projection pursuit model (PPM) improved by quantum particle swarm optimization (QPSO) for the evaluation of the resilience of subway station projects to waterlogging disasters. In view of the lack of research results related to the evaluation of the resilience of subway station projects to waterlogging disasters, 16 secondary indicators that affected the ability of subway station projects to recover from waterlogging disasters were identified from defense, recovery, and adaptability, for the first time. A PPM improved by QPSO was then proposed to effectively deal with the high-dimensional data about the resilience of subway station projects to waterlogging disasters. The QPSO was used to solve the best projection vector of the PPM, and interpolation algorithm was used to construct the mathematical model of evaluation. Finally, four station projects of Chengdu Metro Line 11 in China were selected for a case study analysis. The case study revealed that, among the secondary indicators, the emergency plan of construction order, the exercise frequency of emergency plans, and relief supplies had the greatest weights. The recovery was found to be the most important in the primary indicators. The values of the resilience of Lushan Avenue Station, Miaoeryan Station, Shenyang Road Station, and Tianfu CBD North Station to waterlogging disasters were found to be 2, 1.6571, 2.8318, and 3 respectively. This resilience ranking was consistent with the actual disaster situation in the flood season of 2019. In addition, the case study results showed that QPSO had the advantages of fewer parameter settings and a faster convergence speed as compared with PSO and the genetic algorithm.

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