# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
## 5 Conclusions  
This paper uses literature research and survey methods to construct a comprehensive evaluation index system for urban utility tunnels fire risk. It combines the improved cloud model and evidence theory to quantitatively evaluate the fire risk of urban utility tunnels. The following conclusions are drawn from the research:  
1. The improved cloud model based on cloud entropy optimization calculates the membership degree of index level, which considers the fuzziness and clarity of risk level division and solves the problem of randomness and fuzziness of fire risk assessment.
2. The improved evidence theory based on dynamic and static weights achieve the combined weighting of the dynamic and static weights of indicators by using the game theory for reference, which makes up for the shortcomings of the single assignment method and makes the weight distribution more reasonable. Additionally, it efficiently reduces the conflict of evidence fusion in the traditional evidence theory and improves the accuracy of the evaluation results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Assessment Method** & **I** & **II** & **III** & **IV** & **V** & \(\theta\) & \(K^{-1}\) & **Risk Level** \\ \hline Traditional cloud model 1 & 0.0238 & 0.1025 & 0.2163 & 0.0007 & 0.0000 & & III \\ Traditional cloud model 2 & 0.1205 & 0.3853 & 0.4846 & 0.0622 & 0.0002 & & III \\ The improved cloud model & 0.1226 & 0.3231 & 0.3576 & 0.0844 & 0.0047 & & III \\ The improved cloud model with traditional evidence theory & 0.0003 & 0.4044 & 0.5953 & 0.0000 & 0.0000 & 0.0000 & \(1.62\times 10^{18}\) & III \\ The improved cloud model with the improved evidence theory & 0.0087 & 0.4212 & 0.5665 & 0.0027 & 0.0001 & 0.0008 & 777,253 & III \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fire risk assessment results of the urban utility tunnel.  
3. The method proposed in this paper is verified by an example. The fire risk level of this urban utility tunnel is medium, which is consistent with the actual situation, proving the rationality and feasibility of the method. By comparing the evaluation results of the traditional cloud model, the improved cloud model and the improved cloud model with traditional evidence theory, the effectiveness and superiority of this method are proved. At the same time, corresponding management and control measures are proposed for the indicators of utility tunnels with a high fire risk weight, which have a certain reference value.
4. Compared with other scholars' research, it can be found that the literature [9] uses the fuzzy analysis method, which has strong subjectivity. The literature [11] simply uses Bayesian network instead of weight adjustment strategy, resulting in weak reliability. Although this method has high accuracy and good applicability, it does not use dynamic evaluation methods. Future research will consider how to combine the indicator results and the actual situation to conduct dynamic trend assessment and prediction so as to better conduct fire risk management.  
Conceptualization, Q.N. and Q.Y.; data collection, Y.W.; methodology, Q.Y. and Y.H.; consulting experts, Y.H.; writing--original draft, Q.N. and Q.Y.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Henan Scientific and Technological research project (201300210100), the Zhengzhou Natural Science Project (21 ZZXTCX10) and the Innovative Funds Plan of Henan University of Technology (2022 ZKCJ03).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.  
The authors declare that there is no conflict of interest.