Abstract
The health, safety, and environment (HSE) risk assessment of major sewage transport tunnel projects (MSTTPs) is of great significance to guarantee sewage treatment, ecological environment protection, and sustainable development. To accurately evaluate the HSE risk of MSTTPs at the construction stage and effectively deal with their randomness and ambiguity, a risk assessment model based on the structural entropy weight method (SEWM) and the cloud model is put forward in this paper. First, an index system for MSTTPs was constructed via a literature review and expert interviews, and the rough sets method was used to filter the indicators. Then, weights were calculated by the SEWM, which is able to consider both subjective and objective factors of the weight calculation. Finally, to clarify the randomness and ambiguity in the evaluation, the HSE risk level was determined by the cloud similarity. The model was applied to the Donghu Deep Tunnel Project in Wuhan, China, and the results demonstrated that its HSE risk level was medium, which was acceptable. The index related to construction safety had the largest weight. A humid environment, improper power utilization, and sludge and mud pollution were found to be the most influential risk indicators. The risk level could be intuitively and qualitatively judged by the figure evaluation cloud, providing a vivid and rapid evaluation tool for the emergency decision‐making of project managers, and the risk level could be quantitatively judged by the calculation of cloud similarity. Moreover, through the comparison with gray correlation degree, set pair analysis, and fuzzy comprehensive evaluation method evaluation results, we prove the scientificity and effectiveness of the proposed model. The research results provide a valuable reference for the project management of MSTTPs at the construction stage.
Highlights
In the past 30 years, urbanization in developing countries, especially China, has progressed rapidly, resulting in increased difficulties in urban sewage treatment and environmental pollution problems [1]
major sewage transport tunnel projects (MSTTPs) are usually located 30–60 m underground and their construction sites are typically closed construction environments, which have great impacts on occupational safety. e construction processes of MSTTPs are characterized by complicated technology, a strict construction period, high mechanization, and complex construction safety risk [4]. e accidents and pollution incidents caused by these risk factors may result in huge economic losses and casualties, making the MSTTPs unable to be completed on time
In combination with the research purposes of this study, the HSE risk of MSTTPs is Advances in Civil Engineering defined as the occupational health risk of workers, the construction safety risk, and the environmental pollution risk caused by the construction of MSTTPs. e occupational health risk of workers is geared toward the research category of public health science [7]
Summary
In the past 30 years, urbanization in developing countries, especially China, has progressed rapidly, resulting in increased difficulties in urban sewage treatment and environmental pollution problems [1]. E cloud model is able to analyze both qualitative descriptions and quantitative data and can handle both fuzziness and randomness of a risk system In recent years, it has become increasingly more widely used in the field of risk assessment. Liu et al [18] combined the cloud model with an artificial neural network and proposed a new method of urban flood risk assessment that could effectively deal with the randomness and fuzziness of urban flood risk factors. (1) For the first time, a HSE risk evaluation index system was constructed from three aspects of health, safety, and environment by using the methods of literature research and questionnaire survey. (3) Considering the fuzziness and randomness in the evaluation process, the qualitative concept of HSE risk evaluation index and the conversion of quantitative data were realized by the cloud model, which made the evaluation more reasonable.
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