# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
## 4 Instance Verification
### Analysis of Results and Recommendations  
By analyzing the data in Table 6, it can be concluded that:  
(1) According to the principle of maximum membership, the results of the five risk evaluation methods all determine the fire risk level of the urban utility tunnel to be III (medium risk). The evaluation results are consistent with the actual situation, which verifies the feasibility and reasonableness of the method in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & \begin{tabular}{c} **Dynamic** \\ **Weights** \\ \end{tabular} & \
\begin{tabular}{c} **Static** \\ **Weights** \\ \end{tabular} &
\begin{tabular}{c} **Combined** \\ **Weights** \\ \end{tabular} \\ \hline Structural design \(C_{1}\) & 0.0156 & 0.0522 & 0.0198 \\ Fire-resistance class \(C_{2}\) & 0.0337 & 0.0431 & 0.0348 \\ Fire load \(C_{3}\) & 0.0659 & 0.0474 & 0.0638 \\ Electrical equipment condition \(C_{4}\) & 0.0898 & 0.0474 & 0.0849 \\ Fire zone setting \(C_{5}\) & 0.0160 & 0.0359 & 0.0182 \\ Fire detection system \(C_{6}\) & 0.0754 & 0.0526 & 0.0729 \\ Fire alarm system \(C_{7}\) & 0.0746 & 0.0398 & 0.0707 \\ Fire-fighting equipment configuration \(C_{8}\) & 0.0313 & 0.0526 & 0.0337 \\ Water supply and fire extinguishing system \(C_{9}\) & 0.0455 & 0.0478 & 0.0457 \\ Ventilation and smoke extraction system \(C_{10}\) & 0.0443 & 0.0332 & 0.0431 \\ Emergency lighting system \(C_{11}\) & 0.0358 & 0.0631 & 0.0389 \\ Safe route planning \(C_{12}\) & 0.0443 & 0.0485 & 0.0448 \\ Evacuation sign \(C_{13}\) & 0.0387 & 0.0441 & 0.0393 \\ Personnel security behavior \(C_{14}\) & 0.0655 & 0.0591 & 0.0648 \\ Personnel security awareness \(C_{15}\) & 0.0443 & 0.0411 & 0.0440 \\ Personnel emergency response capacity \(C_{16}\) & 0.0667 & 0.0493 & 0.0647 \\ Personnel working status \(C_{17}\) & 0.0444 & 0.0373 & 0.0436 \\ Fire control management system \(C_{18}\) & 0.0407 & 0.0413 & 0.0408 \\ Fire education training \(C_{19}\) & 0.0675 & 0.0600 & 0.0665 \\ Routine fire inspection \(C_{20}\) & 0.0348 & 0.0496 & 0.0365 \\ Fire emergency planning \(C_{21}\) & 0.0252 & 0.0546 & 0.0285 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Indicator weights. (2) Combined with the analysis results in Section 3.1, the improved cloud model optimizes the cloud entropy value compared with the traditional cloud model evaluation. Its membership degree assignment values of risk level are more logical. Comparing the evaluation results of the improved cloud model before and after evidence fusion, the maximum value of the improved cloud model is 0.3576, and the second value is 0.3231, with a difference of 9.65%. The maximum value of the improved cloud model by traditional evidence theory is 0.5953, and the second value is 0.4044, with a difference of 32.07%. The maximum value of the improved cloud model with improved evidence theory is 0.5665, the second value is 0.4212, and the difference ratio is 25.65%. Through the above comparative analysis, the evaluation results of evidence fusion have higher risk level discrimination and better practicality in the field of risk evaluation. Both evaluation results have higher risk level differentiation than the improved cloud model fusing traditional and improved evidence theory. Compared with the value of \(K^{-1}\) obtained by the traditional evidence theory, which is 1.62 \(\times\) 10\({}^{18}\), the value of \(K^{-1}\) obtained by the improved evidence theory based on dynamic and static weights is only 77,253 after fusing evidence 20 times. It proves that the improved evidence theory based on dynamic and static weights effectively reduces the conflict among evidence, and its fusion results are more appropriate to reality. The superiority of this paper's method was verified after a comprehensive comparison and analysis.