Abstract
Frequent extreme climate events and rapid global urbanization have amplified the occurrence of accidents such as waterlogging or the overflow of pollution in big cities. This has increased the application scenarios of large-sized deep drainage tunnel projects (LSDDTPs). The scientific and accurate evaluation of the construction safety risks of LSDDTP can effectively reduce the corresponding economic losses and casualties. In this paper, we employed the hierarchical holographic model to construct the safety risk list of LSDDTPs in terms of the risk source and construction unit. Based on social network analysis, we then screened key indicators and calculated the weights of all secondary indicators from the correlation between risk factors. We subsequently developed a construction safety risk assessment model of LSDDTPs based on the matter-element extension method. The Donghu Deep Tunnel Project in Wuhan, China, was selected as a case study for the proposed method. The results of empirical research demonstrated that eight indicators (e.g., failure to effectively detect the change of the surrounding environment of the tunnel project) were key factors affecting the construction safety risk of IV, which is within the acceptable risk level. Our proposed model outperformed other methods (the fuzzy comprehensive evaluation, analytic hierarchy process, entropy weight method, and comprehensive weight method) in terms of scientific validity and research advancements.
Highlights
With the continuous advancement of global climate change and urbanization, problems such as insufficient drainage system capacity and low drainage standards in major cities around the world have become increasingly prominent [1]
Large-sized deep drainage tunnel projects (LSDDTPs) refer to the drainage tunnels buried in deep underground spaces, which are typically set in areas where waterlogging occurs, with a dense population, complex underground pipelines, and where the existing drainage system is difficult to reform [2]
Each risk can be regarded as a point, and the relationship between risks can be regarded as the relationship between points. erefore, the social network analysis (SNA) can be used to determine the relationship between risks, achieving both an overall analysis of the risk network and an individual analysis of risks based on the relationship between risks and influences
Summary
With the continuous advancement of global climate change and urbanization, problems such as insufficient drainage system capacity and low drainage standards in major cities around the world have become increasingly prominent [1]. Determining how to correctly identify and reduce the construction safety risks of LSDDTPs and effectively avoid potential accidents caused by risk factors is a practical problem that must be solved. Following the construction of the social network structure, the SNA can evaluate the objective law of interactions between individuals and factions through the relationships amongst points [22]. SNA is an effective tool for the investigation of the relationship between factors and can determine structural problems through both overall and individual analysis. Erefore, the SNA can be used to determine the relationship between risks, achieving both an overall analysis of the risk network and an individual analysis of risks based on the relationship between risks and influences. E basic steps of the MEEM in the risk assessment process are as follows: Step 1: determine the evaluation matter element and classical domain. Assuming that the evaluation system of the research object includes m indicators C1, C2, . . . , Cm, the established evaluation model can be expressed as [26] R (U, C, U)
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