# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
## 1 Introduction  
The urban utility tunnel is a new type of underground tunnel integrating gas, electricity, communications, water, heating, and other municipal facilities. It plays an important role in improving the urban environment and increasing the utilization of space resources [1; 2]. However, because of the relatively closed underground space, the complex structure of various pipeline arrangements, and many combustible materials, once a fire occurs, it will not only lead to the paralysis of the urban operation system but also cause severe economic losses and casualties. Therefore, it is essential to conduct a risk assessment and early warning for the fire risk of urban utility tunnels [3; 4]. Scholars from various countries have systematically researched fire safety and fire risk evaluation for urban utility tunnels [5; 6]. [PERSON] et al. [7] created a full-scale fire model for sloped tunnels, taking into account the tunnel geometry, slope, and fire source location, which helps to optimize the design of the tunnel's smoke exhaust system and ensure fire safety. [PERSON] et al. [8] used PyroSim software to build a simulation model to study the smoke spread law in utility tunnels using fire protection measures such as air baffles, fire doors, and smoke exhaust equipment. [PERSON] et al. [9] used fault tree and fuzzy analysis methods for metro fire risk assessment. The former is used to analyze fire mechanisms and formulate corresponding fire prevention measures. The latter is used to determine the membership of the fire risk level. The analysis method using the combination of the two is more reliable. [PERSON] et al. [10] built a fire risk evaluation model for utility cable tunnels based on Bayesian networks and the butterfly knot method. They proposed strong support for fire prevention by analyzing the antecedents and consequences of the development process of fire accidents. [PERSON] et al. [11] took the underground utility tunnels in ChangpingDistrict, Beijing, as a research example to establish a risk evaluation and sensitivity analysis framework based on the Bayesian network. The key points of predicted incidents and the probability of risk occurrence are derived by the analysis of risk accidents in underground utility tunnels. To summarize the above studies, it was found that:  
1. The research on the fire safety of urban utility tunnels mainly focuses on the causative factors of fire accidents, spreading characteristics, and fire protection design. The research on fire risk evaluation is less [12]. 2. There are some problems in the fire risk evaluation, such as single index weighting and strong subjectivity. These ignore the impact of randomness, fuzziness, and uncertainty of risk evaluation on the evaluation results. In terms of risk evaluation, the cloud model can transform qualitative concepts and quantitative values. It can replace the traditional membership solution function and comprehensively consider the evaluation problem's randomness and fuzziness [13; 14]. The evidence theory has a solid ability for multi-information fusion. The reliability of risk assessment can be improved by effectively fusing the evaluation results through evidence theory [15; 16]. This paper proposes a fire risk assessment method for urban utility tunnels based on an improved cloud model and evidence theory. On the one hand, this method takes advantage of the improved cloud model with cloud entropy optimization to deal with the problem of ambiguity and randomness in risk evaluation [17]. On the other hand, the combination weighting method based on game theory [18; 19] solves the combined weight of each indicator and carries out evidence weighting. It can identify and correct conflicting evidence, remedy the defects of evidence fusion in traditional evidence theory, make the evidence fusion results more reliable, and solve the uncertainty problem of risk evaluation.