The deep drainage tunnel project is an important measure to alleviate urban waterlogging. The construction of a deep drainage tunnel is a complicated process, involving many influencing factors, and there are correlations among these influencing factors, so the risk assessment is difficult. In this study, the ISM method, CRITIC method and cloud model are combined to build a risk assessment model for the small-diameter shield construction of a deep drainage tunnel. Firstly, the risk index system of small-diameter shield construction in a deep drainage tunnel is put forward. Secondly, the ISM method is used to divide the risk indicators and extract the key risk factors. Then, these key risk factors are weighted with the CRITIC method, and the cloud model is used to evaluate the construction risk of a small-diameter shield of a deep drainage tunnel. Finally, the feasibility and accuracy of the proposed method are verified by a practical case. It was found that the risk assessment method proposed in this study can not only effectively assess the level of security risk, but also identify the key risk factors and rank the importance of these factors. The results of this study can reduce the interference items and workload of risk assessment to a certain extent, and help provide managers with an accurate decision-making basis.

Shield construction of deep tunnels for urban drainage involves many risk factors, and potential safety hazards are difficult to monitor and identify directly. In order to improve the risk management level of shield construction in urban drainage deep tunnel, this study proposes a method for identifying risk factors by combining text mining technology and the entropy weight method. By using this method, 34 safety risk factors were successfully extracted from the safety accident reports of urban drainage deep tunnel shield construction and the related text data. The results of this study show that the text mining method could play an important role in the risk management of urban drainage deep tunnel shield construction; the introduction of the entropy weight method further improved the accuracy of risk factor identification. The results of this study not only enrich the research content of risk management in urban drainage deep tunnel shield construction but also provide theoretical guidance for managers to formulate risk management measures and optimize risk management procedures.

Small-diameter tunnels play a crucial role in urban infrastructure, managing functions such as sewage, rainwater, and electrical systems. However, the safety assessment of small-diameter tunnel shield construction faces difficulties due to unclear physical relationships and the limitations of traditional physical information models in predicting complex risks. To address this issue, the integration of physical information with data-driven analysis methods offers a promising approach. Combining these advantages, a hybrid model was proposed to establish a robust construction safety risk evaluation framework for small-diameter tunnels under geological conditions. The presently proposed method mainly consists of a clustering of risk factors, physical information stratification, and risk early warning. Specifically, the K-means clustering method optimized by the Harris Hawks algorithm was used for risk identification, the Analytic Hierarchy Process was used for risk analysis, and the physical information output from the risk analysis was used for risk warning. A case study was produced, utilizing the proposed hybrid model for the Wuhan East Lake Deep Tunnel project. The results show the risk transfer path through inadequate personnel safety awareness and protection, mechanical system failures and installation deviations, substandard material quality and improper stacking, outdated or immature construction technology, and environmental risks.