Land subsidence risk zoning for high speed railway based on hesitant fuzzy linguistic model

被引:0
|
作者
Wang, Chuxin [2 ]
Wang, Yingchao [1 ,2 ]
Yang, Jiguang [3 ]
Fan, Xiamin [3 ]
Zhang, Zheng [3 ,4 ]
机构
[1] State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground, China University of Mining and Technology, Xuzhou,221116, China
[2] School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou,221116, China
[3] Construction Headquarters of Xuzhou Railway Hub Project, China Railway Shanghai Bureau Group Limited Company, Xuzhou,221000, China
[4] Hefei Railway Hub Project Construction Headquarters, China Railway Shanghai Bureau Group Limited Company, Hefei,230011, China
关键词
A new hesitant fuzzy 2-tuple-data envelopment analysis (DEA) model was established in order to assess the risk of land subsidence along high-speed railway. Assessment opinions were handled with the computation theory of hesitant fuzzy 2-tuple linguistic model. Experts’ weights were adjusted based on the principle of maximum group consensus; and the factors’ weights were determined by using the DEA model; which made up for the defects of the previous assessment methods; such as difficulty of passing consistency test; difficulty of assessment process; and low degree of consensus of the evaluators. The hesitant fuzzy 2-tuple-DEA analysis model was applied to a high-speed railway project under construction in Huaibei; Anhui Province. The risk assessment index system of hazard factors; vulnerability factors and sensitivity factors was established. The fuzzy assessment set of individual hesitation and matrix of group hesitation were established; and weight of each factor was obtained. The visual display of risk distribution in the disaster; vulnerability and sensitivity index assessment area was realized based on GIS system. Land subsidence risk distribution along the high-speed railway was obtained. Results show that DK66-DK67 is the highest risk section; and the subsidence monitoring in this section should be strengthened. © 2024 Zhejiang University. All rights reserved;
D O I
10.3785/j.issn.1008-973X.2024.08.016
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页码:1691 / 1703
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