Fuzzy Risk Assessment of Tunnels' Undercrossing Existing Structures. (17th March 2022)

• Record Type: Journal Article
• Authors: Wang, Chong-xu; Li, Jia-Wen; Wang, Hao
• Other Names: Santos Reyes J Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-03-17
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/4812245
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Numerical calculation is usually employed to analyse the safety risks of tunnel construction under existing structures. However, due to the fuzziness and randomness of the construction environment, the simulation results fluctuate significantly. Given the fuzziness and randomness encountered during the safety risk assessment of tunnels undercrossing existing structures, this study analyses and summarises the previous experiences and selects a relevant algorithm of the cloud model to solve the uncertainty problem during the assessment. The study proposes three forms of cloud models to evaluate the risks of tunnels undercrossing existing structures and performs a comparative analysis between them. Finally, the effectiveness and feasibility of the cloud model calculation method are verified through a case study. The research shows that (1) the accuracy of the one-dimensional and two-dimensional cloud models was high, and the difference was small, indicating that the cloud model method used to determine the risk level of tunnels underpassing existing structures was reasonably effective, (2) since the soft 'and' two dimensions operated computation, the distribution probability was dispersed, making the soft 'and' computation more affected by the index, resulting in lower accuracy than the one-dimensional and two-dimensional cloud models, and (3) compared with numerical calculation, the cloud model calculation was more convenient, reflecting the relationship between the indices and the relationship between the indices and the complex whole. The evaluation results used the comprehensive evaluation probability to determine the risk level so that the evaluation results were reliable.