Novel model for risk identification during karst excavation. (May 2021)

• Record Type: Journal Article
• Authors: Lin, Song-Shun; Shen, Shui-Long; Zhou, Annan; Xu, Ye-Shuang
• Is Part Of: Reliability engineering & system safety. Volume 209(2021)
• Publication Date: 2021-05
• Subjects: I-AHP -- Fuzzy TOPSIS -- Risk identification -- Expert confidence index -- Karst excavation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107435
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Novel approach based on I-AHP and TOPSIS for risk identification is developed;. Expert confidence index is introduced to increase the reliability of experts' questionnaire;. Attributes of factors are considered in proposed model;. A case concerning risk analysis in karst excavation is presented. Abstract: This study proposes a novel fuzzy model for identifying high-risk factors during excavations in urban karst geological environments. The proposed model incorporates the interval analytic hierarchy process (I-AHP) into the technique for order preference by similarity to an ideal solution (TOPSIS). The developed model considers the complex geological conditions, monitoring data, management quality, and surrounding environment. I-AHP is employed to assign the weights of the criteria, while TOPSIS is applied to identify high-risk factors. An expert confidence index is introduced to increase the reliability of the evaluated results. A case study of karst excavation at Ma'anshan Park Station on Guangzhou Metro Line 9 is analysed to validate the proposed model. The results indicate that high-risk factors can be identified in different excavation stages. The evaluated results concerning variations in risk factors can provide a guide for adopting construction measures to mitigate risk and reduce accident occurrence.