Risk assessment of complex footbridge based on Dempster-Shafer evidence theory using Fuzzy matter-element method

被引:6
|
作者
Lu, Pengzhen [1 ]
Zhou, Yutao [1 ,2 ]
Wu, Ying [3 ]
Li, Dengguo [1 ]
机构
[1] Zhejiang Univ Technol, Hangzhou 310014, Zhejiang, Peoples R China
[2] Univ Manchester, Dept Mech Aerosp & Civil Engn, Dynam Lab, Manchester M13 9PL, England
[3] Jiaxing Nanhu Univ, Jiaxing 314001, Zhejiang, Peoples R China
关键词
Footbridge; Risk assessment; Multi-source information fusion; Fuzzy matter-element method; Improved Dempster evidence theory; MANAGEMENT; SYSTEM; BIM; SAFETY; PROJECTS;
D O I
10.1016/j.asoc.2022.109782
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Owing to the landscapes of cities and the requirements of subchannels, the construction of large -span footbridges is complex and extremely risky. These risks may lead to the collapse of a footbridge during construction, resulting in casualties and other catastrophic consequences, delays in projects and significant property losses, and potential safety hazards during follow-up maintenance. At present, China's urban footbridge construction has not been included in the overall urban construction category, and its risk assessment is static and temporary, mainly relying on the subjective judgment of experts and construction personnel, and the different cases are difficult to learn from each other. To address this issue, a new risk assessment method and a novel system quality management framework are proposed herein. In this system, real-time engineering quality data under 4M1E (Man, Method, Material, Machine, Environment) framework is used to replace traditional risk factors. An improved Dempster-Shafer theory is adopted to effectively combine heterogeneous data from the 4M1E framework and calculate the dynamic total risk index. Finally, the proposed method is verified. The research results show that the new system can predict the risk level of each stage of bridge construction objectively and effectively by using real-time monitoring data, and has high robustness in different project testing.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] The analysis of uncertainty of network security risk assessment using Dempster-Shafer theory
    Gao, Huisheng
    Zhu, Jing
    Li, Congcong
    [J]. PROCEEDINGS OF THE 2008 12TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN, VOLS I AND II, 2008, : 754 - +
  • [32] A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory
    Wang, Haiying
    Shi, Yuke
    Chen, Long
    Zhang, Xiaofeng
    [J]. Sensors, 2024, 24 (19)
  • [33] A New Dempster-Shafer Theory-based Method with Fuzzy Targets for Fuzzy Sets Ranking
    Chai, Kok Chin
    Tay, Kai Meng
    Lim, Chee Peng
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013,
  • [34] Development of a hybrid AHP and Dempster-Shafer theory of evidence for project risk assessment problem
    Albogami, Saad M.
    Ariffin, Mohd Khairol Anuar Bin Mohd
    Bin Supeni, Eris Elianddy
    Ahmad, Kamarul Arifin
    [J]. JOURNAL OF PROJECT MANAGEMENT, 2021, : 77 - 94
  • [35] Image edge detection using Dempster-Shafer evidence theory
    Zhao, Chunjiang
    Deng, Yong
    [J]. Journal of Computational Information Systems, 2010, 6 (13): : 4345 - 4352
  • [36] Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies
    Nikolov, Andriy
    Uren, Victoria
    Motta, Enrico
    de Roeck, Anne
    [J]. UNCERTAINTY REASONING FOR THE SEMANTIC WEB I, 2008, 5327 : 143 - 160
  • [37] Dempster-Shafer theory for combining in silico evidence and estimating uncertainty in chemical risk assessment
    Rathman J.F.
    Yang C.
    Zhou H.
    [J]. Computational Toxicology, 2018, 6 : 16 - 31
  • [38] A Novel Risk Assessment Model in Tunnel Leakage by Using Cloud Model and Dempster-Shafer Evidence Theory
    Guo, Kai
    Zhang, Limao
    [J]. CONSTRUCTION RESEARCH CONGRESS 2020: PROJECT MANAGEMENT AND CONTROLS, MATERIALS, AND CONTRACTS, 2020, : 889 - 897
  • [39] Multimodal recommendation algorithm based on Dempster-Shafer evidence theory
    Xiaole Wang
    Jiwei Qin
    [J]. Multimedia Tools and Applications, 2024, 83 : 28689 - 28704
  • [40] Multimodal recommendation algorithm based on Dempster-Shafer evidence theory
    Wang, Xiaole
    Qin, Jiwei
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (10) : 28689 - 28704