Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
Abstract
:1. Introduction
- (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].
2. Assessment Index System for Urban Utility Tunnels Fire Risk
3. Fire Risk Assessment Model of Urban Utility Tunnels
3.1. The Improved Cloud Model Based on Cloud Entropy Optimization
3.1.1. Cloud Model
3.1.2. Cloud Entropy Optimization
3.2. Evidence Theory
3.2.1. Basic Probability Distribution Function
3.2.2. The Improved Synthesis Rules of Evidence Theory Based on Dynamic and Static Weights
4. Instance Verification
4.1. Determining Membership Degree of Indicator Level Based on the Improved Cloud Model
4.2. Determining the Basic Probability Distribution Function for the Indicator
4.3. Evidence Fusion Based on Dynamic and Static Weights
4.4. Analysis of Results and Recommendations
5. Conclusions
- (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.
- (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.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Target | First-Level Indicator | Secondary Indicators | Evaluation Criteria |
---|---|---|---|
Fire risk of urban utility tunnels A | Tunnel fire protection capacity B1 | Structural design C1 | The complexity of the internal structure of tunnels |
Fire resistance class C2 | The overall fire-resistance rating of tunnels | ||
Fire load C3 | The quantity of combustible substances contained in tunnels | ||
Electrical equipment condition C4 | Electrical equipment aging and breakage rate | ||
Fire zone setting C5 | The reasonableness of fire partitioning | ||
Fire control capability B2 | Fire detection system C6 | The advancement of fire detection systems | |
Fire alarm system C7 | The advancement of fire alarm systems | ||
Fire-fighting equipment configuration C8 | The perfection of fire-fighting equipment | ||
Water supply and fire extinguishing system C9 | The capability of water supply and fire-fighting systems | ||
Ventilation and smoke extraction system C10 | The advancement of ventilation and smoke extraction systems | ||
Emergency evacuation capability B3 | Emergency lighting system C11 | Emergency lighting illumination and duration | |
Safe route planning C12 | The rationality of safe route planning | ||
Evacuation sign C13 | The rationality of indicator setting | ||
Personnel prevention and control capacity B4 | Personnel security behavior C14 | The standardization of personnel operations | |
Personnel security awareness C15 | The strength of personnel security awareness | ||
Personnel emergency response capacity C16 | The strength of personnel emergency response capacity | ||
Personnel working status C17 | The physical and psychological state of personnel | ||
Security management capability B5 | Fire control management system C18 | The perfection of fire control management systems | |
Fire education training C19 | The frequency of fire education training | ||
Routine fire inspection C20 | The strength and frequency of routine fire inspections | ||
Fire emergency planning C21 | The perfection of fire emergency planning |
Risk Level | I | II | III | IV | V |
---|---|---|---|---|---|
Interpretation Index interval | Extremely low risk [0, 2) | Low risk [2, 4) | Medium risk [4, 6) | High risk [6, 8) | Extremely high risk [8, 10) |
Indicators | Expert 1 | Expert 2 | Expert 3 | Expert 4 | Expert 5 | Average Value |
---|---|---|---|---|---|---|
Structural design C1 | 4 | 6 | 5 | 4 | 6 | 5.0 |
Fire-resistance class C2 | 5 | 7 | 6 | 5 | 7 | 6.0 |
Fire load C3 | 4 | 5 | 5 | 4 | 4 | 4.4 |
Electrical equipment condition C4 | 5 | 6 | 4 | 3 | 5 | 4.6 |
Fire zone setting C5 | 3 | 4 | 3 | 2 | 3 | 3.0 |
Fire detection system C6 | 3 | 4 | 3 | 5 | 6 | 4.2 |
Fire alarm system C7 | 4 | 5 | 5 | 4 | 3 | 4.2 |
Fire-fighting equipment configuration C8 | 5 | 4 | 6 | 4 | 5 | 4.8 |
Water supply and fire extinguishing system C9 | 5 | 6 | 7 | 5 | 4 | 5.4 |
Ventilation and smoke extraction system C10 | 3 | 2 | 2 | 3 | 2 | 2.4 |
Emergency lighting system C11 | 3 | 3 | 2 | 3 | 3 | 2.6 |
Safe route planning C12 | 2 | 2 | 3 | 2 | 2 | 2.4 |
Evacuation sign C13 | 3 | 1 | 2 | 1 | 1 | 1.4 |
Personnel security behavior C14 | 2 | 4 | 5 | 4 | 3 | 3.8 |
Personnel security awareness C15 | 3 | 2 | 2 | 3 | 2 | 2.4 |
Personnel emergency response capacity C16 | 4 | 5 | 4 | 4 | 5 | 4.4 |
Personnel working status C17 | 2 | 2 | 3 | 3 | 2 | 2.4 |
Fire control management system C18 | 1 | 2 | 1 | 3 | 2 | 1.8 |
Fire education training C19 | 4 | 5 | 4 | 3 | 4 | 4.0 |
Routine fire inspection C20 | 5 | 7 | 6 | 6 | 5 | 5.8 |
Fire emergency planning C21 | 6 | 5 | 4 | 5 | 6 | 5.2 |
Indicator C20 | Rule | The “50% Correlative Degree” Rule | Cloud Entropy Optimization Algorithm |
---|---|---|---|
I | (1, 0.3333, 0.03333) | (1, 0.8493, 0.08493) | (1, 0.5394, 0.05394) |
II | (3, 0.3333, 0.03333) | (3, 0.8493, 0.08493) | (3, 0.4633, 0.04633) |
III | (5, 0.3333, 0.03333) | (5, 0.8493, 0.08493) | (5, 0.5610, 0.05610) |
IV | (7, 0.3333, 0.03333) | (7, 0.8493, 0.08493) | (7, 0.7642, 0.07642) |
V | (9, 0.3333, 0.03333) | (9, 0.8493, 0.08493) | (9, 0.5535, 0.05535) |
Indicators | Dynamic Weights | Static Weights | Combined Weights |
---|---|---|---|
Structural design C1 | 0.0156 | 0.0522 | 0.0198 |
Fire-resistance class C2 | 0.0337 | 0.0431 | 0.0348 |
Fire load C3 | 0.0659 | 0.0474 | 0.0638 |
Electrical equipment condition C4 | 0.0898 | 0.0474 | 0.0849 |
Fire zone setting C5 | 0.0160 | 0.0359 | 0.0182 |
Fire detection system C6 | 0.0754 | 0.0526 | 0.0729 |
Fire alarm system C7 | 0.0746 | 0.0398 | 0.0707 |
Fire-fighting equipment configuration C8 | 0.0313 | 0.0526 | 0.0337 |
Water supply and fire extinguishing system C9 | 0.0455 | 0.0478 | 0.0457 |
Ventilation and smoke extraction system C10 | 0.0443 | 0.0332 | 0.0431 |
Emergency lighting system C11 | 0.0358 | 0.0631 | 0.0389 |
Safe route planning C12 | 0.0443 | 0.0485 | 0.0448 |
Evacuation sign C13 | 0.0387 | 0.0441 | 0.0393 |
Personnel security behavior C14 | 0.0655 | 0.0591 | 0.0648 |
Personnel security awareness C15 | 0.0443 | 0.0411 | 0.0440 |
Personnel emergency response capacity C16 | 0.0667 | 0.0493 | 0.0647 |
Personnel working status C17 | 0.0444 | 0.0373 | 0.0436 |
Fire control management system C18 | 0.0407 | 0.0413 | 0.0408 |
Fire education training C19 | 0.0675 | 0.0600 | 0.0665 |
Routine fire inspection C20 | 0.0348 | 0.0496 | 0.0365 |
Fire emergency planning C21 | 0.0252 | 0.0546 | 0.0285 |
Assessment Method | I | II | III | IV | V | Risk Level | ||
---|---|---|---|---|---|---|---|---|
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 × 1018 | 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 |
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Niu, Q.; Yuan, Q.; Wang, Y.; Hu, Y. Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory. Appl. Sci. 2023, 13, 2204. https://doi.org/10.3390/app13042204
Niu Q, Yuan Q, Wang Y, Hu Y. Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory. Applied Sciences. 2023; 13(4):2204. https://doi.org/10.3390/app13042204
Chicago/Turabian StyleNiu, Qunfeng, Qiang Yuan, Yunpo Wang, and Yi Hu. 2023. "Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory" Applied Sciences 13, no. 4: 2204. https://doi.org/10.3390/app13042204
APA StyleNiu, Q., Yuan, Q., Wang, Y., & Hu, Y. (2023). Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory. Applied Sciences, 13(4), 2204. https://doi.org/10.3390/app13042204