Next Article in Journal
Investigation of Parking Lot Pavements to Counteract Urban Heat Islands
Previous Article in Journal
Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method

College of Environmental and Safety Engineering, Changzhou University, Changzhou 213164, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(12), 7275; https://doi.org/10.3390/su14127275
Submission received: 6 May 2022 / Revised: 9 June 2022 / Accepted: 12 June 2022 / Published: 14 June 2022
(This article belongs to the Section Sustainable Urban and Rural Development)

Abstract

:
With the rapid development of urban modernization, traffic congestion, travel delays, and other related inconveniences have become central features in people’s daily lives. The development of subway transit systems has alleviated some of these problems. However, numerous underground subway stations lack adequate fire safety protections, and this can cause rescue difficulties in the event of fire. Once the fire occurs, there will be huge property losses and casualties. In addition, this can have a vicious impact on sustainable development. Therefore, in order to make prevention in advance and implement targeted measures, we should quantify the risk and calculate the fire risk value. In this study, through consulting experts and analysis of data obtained from Changzhou Railway Company and the Emergency Management Bureau, the fire risk index system of subway stations was determined. We calculated the index weight by selecting the combination weighting method of game theory to eliminate the limitations and dependence of subjective and objective evaluation methods. The idea of relative closeness degree in TOPSIS method iwas introduced to calculate the risk value of each subway station. Finally, the subway station risk value model was established, and the risk values for each subway station were calculated and sorted. According to expert advice and the literature review, we divided the risk level into five levels, very high; high; moderate; low and very low. The results shown that 2 subway stations on Line 1 have very high fire risk, 2 subway stations on Line 1 have high fire risk, 2 subway stations on Line 1 have moderate fire risk, 8 subway stations on Line 1 have low fire risk, and 13 subway stations on Line 1 have very low fire risk. We hope that through this evaluation model method and the results to bring some references for local rail companies. Meanwhile, this evaluation model method also promotes resilience and sustainability in social development.

1. Introduction

With the rapid development of China’s economy in recent years, China has become one of the fastest growing countries in the world. By 2030, the number of vehicles in China will reach 363.8 million according to the Hao [1] prediction model, which is a huge number. This trend not only appears in China, but also the world’s total number of vehicles will exceed 2 billion [2]. These vast amounts of data mean rapid growth in petroleum demand, which poses great challenges to sustainable development.
Nowadays, an increasing number of cities have begun to build subway systems. According to recent statistics, China will add 62 new subway lines in 2021, with a total mileage of 1281.59 km. Urban rail transit has enhanced travel convenience for the public, effectively mitigated urban road congestion, optimized how residents travel, and played a role in energy conservation. However, the marked increase in the construction of new subway systems have also resulted in some drawbacks. For example, the underground space typical of subway systems presents difficulties to fire rescue personnel that do not exist for aboveground fire rescue; such difficulties have placed fire rescue personnel under substantial additional strain. Subway fire accidents can cause tremendous loss of life and destruction of property. Special attention should be paid to the serious consequences of subway station accidents, such as the king’s cross railway station accident (in 1987, more than 31 causalities) and Daegu, Korea (in 2003, more than 198 causalities) [3]. These tragic casualties are caused by fires in subway stations [4,5,6]. There are many such accidents around the world. The main problem is that no correct and reasonable fire risk assessment has been conducted. There are no specific risk levels and corresponding fire protection measures. Therefore, to reduce the risk of accidents, it is a very important issue to carry out fire risk assessment.
Fire risk value is the specific value that should be calculated after the risk assessment. These specific values are used to reflect the current risk level of the evaluation target. Firstly, a large amount of primary data is needed to calculation the fire risk value. Secondly, experts are invited to score and consult. Finally, a huge number of mathematical calculations are carried out on the data and scoring results. Due to the frequent fire accidents in subway stations in recent years, in order to prevent more scientifically in advance, specific fire risk values are needed as a reference. When evaluation objectives emerge, we need to consider their risks.
Accordingly, many scholars have analyzed subway fires. Luo [7] evaluated the construction cross risk of subway transfer stations from two aspects: existing subway stations and new subway stations. Gao [8] applied the fuzzy consistent matrix and AHP to analyze risk factors for tunnel fire management, subway tunnel fire extinguishing systems, and crowd evacuation system indicators. Liu [9] used probability analysis method to analyze the structural vulnerability of subway station. Liu [10] applied AHP method and experts grading method to evaluate the risk of subway stations. Wu [11] used Bayesian network analysis to evaluate the risk of subway station fires. Zhang [12] proposed a simulation method for the most serious subway fire scenarios. Different fire scenarios were examined by using Fire Dynamics Simulator software, and the simulation results were used as a reference for evacuation scenarios. Peng [13] conducted an experimental study on the fire plume characteristics of subway car doors. A set of small-scale experiments was performed in a subway car with both ends open to examine the characteristics of fire smoke columns at different fire location. Lan [14] established a subway fire risk assessment model from four aspects: human factors, equipment-related factors, environmental factors, management factors. Wang [15] used the fuzzy AHP and set pair analysis to assess the risks for the construction environments of subway stations. The research showed that evaluating the fire risk of subway stations through the construction of fire risk evaluation index systems for subway stations is crucial. In the analysis of traditional fire characteristics, many scholars have also conducted simulation analysis [16,17,18,19,20,21,22,23]. Roh [24] used FDS software to study the impact of installation platform screen doors on passengers’ emergency evacuation time. The experimental results showed that the subway stations with platform screen doors have more possible evacuation time than that without installation, which is about 350 s. In addition, Corri [25] assessed the terrorist incidents in crowded places. Mehmet [26] proposed a stop safety index to evaluate pedestrian safety around bus stations. Margarita [27] studied safety management of the light rail transit in Spain and other countries.
However, these scholars have overlooked the effectiveness of objective data and scientific comprehensive evaluation through their complete reliance on computer simulations to assess the risk of subway fires. Traditional subjective and objective weighting evaluation methods, such as analytic hierarchy process, the entropy weight method and the fuzzy comprehensive evaluation method have subjective and objective limitations. The subjective evaluation method needs to rely too much on the experience and professional knowledge of experts, while the objective evaluation method has a strong requirement for the primary data. Once the change of the index value is small or the fluctuation is large, this kind of data is not suitable for the objective evaluation weighting method. Moreover, the objective weighting method conforms to the mathematical rule and has strict mathematical significance. But it often ignores the subjective intention of decision makers and cannot truly achieve comprehensive evaluation. In order to solve the limitations of previous scholars’ work, we propose an evaluation theory based on a game theory combined weighting-TOPSIS model. The game theory combination weighting method is a process of linear combination of weights obtained by different methods to seek the most reasonable index weight. This method obtains the final weight by solving mathematical equations with the idea of game, which not only takes into account the experience and professional knowledge of subjective experts, but also takes into account the standardization of objective data. This model effectively solves the limitations of previous work. The TOPSIS method is a commonly used and effective method in multi-objective decision analysis, also known as the distance method of superior and inferior solutions. It sorts according to the closeness between the limited evaluation objects and the idealized targets. It has applied the combination weighting method of game theory and the TPSIS method to the fire risk assessment of subway stations, which is a new attempt. We hope to effectively evaluate the fire risk of subway stations by using reasonable and scientific mathematical models. On this basis, it is expected to achieve the goal of reducing risks, enhancing fire safety awareness and improving the emergency rescue system.
This study proceeds as follows. Section 2 analyzes the risk assessment indicators and methods and introduces the technical route of the research. Section 3 introduces an engineering example and calculates its risk value. Section 4 analyzes the results of risk value and puts forward some suggestions.

2. Methodology

In this study, with the aim of establishing a fire risk assessment index system, we analyzed previous domestic and foreign subway fire accident causes, investigation reports, relevant laws and regulations. In addition, we invited experts to consult and obtained internal daily inspection report data from Changzhou Rail Company. After an extensive literature review, the AHP and entropy weight method were selected as the subjective and objective evaluation methods, respectively. The concept of game theory was introduced to reduce the error between the two methods. We combined the results of two evaluation methods to obtain the final comprehensive weight, which ensures that the results are accurate and reliable. The risk value model was established by calculating the numerical product of the comprehensive weighting of each index and its corresponding data. Finally, leveraging the opinions of experts and relevant literature, we established the risk level model. Subsequently, we determined the risk level for each subway station in the rail network. An overview of the research concept is shown in Figure 1. The assessment methods used in this article are compared with previous work, as shown in Table 1.
The research was conducted in three stages, with specific procedures as follows:
Stage 1: We collected the fire accident data of subway stations and consulted experts to analyze risks. The causes of disasters were analyzed, and relevant laws and regulations were scrutinized for the establishment of a subway station fire risk assessment index system.
Stage 2: Combined with the selected risk assessment indicators and the support of Changzhou Railway Company, the objective primary data required for the assessment indicators were obtained. The fire risk value model for subway stations was constructed by selecting an evaluation method suitable for the research object.
Stage 3: Based on expert opinions and relevant literature, the risk value classification model was constructed. The aforementioned model and methods were applied to the research on the Changzhou Rail Company, and the reliability of the model was corroborated through comparisons with engineering studies.
  • Establishment of an evaluation index system
To select the risk assessment indicators more accurately and scientifically, we conducted field research on the construction and the operation of branches of Changzhou Rail Company. We carefully considered their opinions and ideas, and comprehensively evaluated the fire risk during the all period. We ensured the inclusion of experts with diverse professional backgrounds, which included safety engineering, fire engineering, civil engineering, structural engineering, and municipal engineering. Thus, a favorable basis for selecting risk assessment indicators was established.
  • Analysis of the influencing factors for fires
In the analysis, multiple factors were considered, including the characteristics of the Changzhou Rail Company, field investigations, fire accident cause analyses, studies from the literature, and existing subway station fire risk assessment index systems. Some additional criteria were also considered, such as laws and regulations on fire protection in Changzhou: the building code for fire protection design (GB 50016-2014), the subway design code (GB50157-2013), the subway fire protection design code (GB51298-2018), the construction and acceptance of cable line electric equipment installation engineering standards (GB50168-2006), and the sprinkler system design code (GB50084-2017). The influencing factors for subway station fires were divided into human factors, building characteristics, fire prevention facilities, management factors, and factors related to construction and materials. On this basis, 21 secondary indicators were expanded. The specific risk assessment indicators are presented in Table 2.
Human factors mainly included the fire safety awareness of passengers and subway workers, factors related to passenger flow, and the number of subway workers present in a given area. Zhu [28] analyzed global subway fires from 2000 to 2019. Among the causes of subway fires in China, the number of fire accidents caused through electrical equipment failure was the largest, followed by inadequate fire safety management and passenger arson. In China’s subway stations, each station is equipped with a certain number of security personnel. Passengers must pass subway security inspections of their belongings similar to analogous inspections conducted in airport facilities. No dangerous goods such as lighters, explosives, or combustibles can be brought into the subway station. For first-level indicators of human factors, we obtained information regarding passenger flow and the number of subway workers in each station of Metro Line 1 from the Changzhou Rail Company. The rail company provided data support for the objective weighting of the entropy weight method as subsequently outlined.
Building characteristics are major indicating factors in subway station fire risk assessment. A subway station is essentially an underground building. Therefore, we accounted for four secondary indicators: subway station area B21, the station length B22, the station width B23, the distance between the building and the nearest fire station B24. Subways are typically constructed at a depth of more than 10 m underground [29]. Large rescue equipment and fire engines encounter difficulty entering the area because of a lack of adequate entry channels. In addition, compared with aboveground buildings [30], the environment in underground stations is closed and the space is narrow. When an accident occurs, rescue personnel must venture deep underground for rescue operations, which are limited by the narrow spaces. This results in the cross phenomenon of human flow, thereby affecting rescue efficiency. In this study, we considered the nearest fire station distance to each subway station for emergency rescue capabilities. According to the obtained data, the distance between each station of Changzhou Metro Line 1 and the nearest fire station does not exceed 3.5 km, which ensures that fires are extinguished promptly.
When a fire occurs, the firefighting facilities at the scene should be employed to [31] effectively slow down the development of the fire until the arrival of fire rescue personnel. Therefore, we accounted for fire facility-related factors in each station: automatic fire alarm system B31, fire extinguishing systems B32, fire separation facilities B33, smoke control facilities B34, fire accident broadcast communication facilities B35, fire emergency lighting and evacuation instructions B36. These fire prevention facilities ensure the safety of subway stations and play a key role in early fire monitoring and prevention. Therefore, factors related to fire prevention facilities must be carefully examined. The inspection of subway stations can be mainly divided into three categories. The first category is the self-examination of the staff in the subway station, which is also their daily work. Through daily inspection of the equipment and facilities inside the station, they record the inspection and write inspection reports. The second is the inspection of the subway company. The frequency of this examination is about 2–3 weeks. In addition to the way of inspection, some parameters that include the train-fire calorific value and the fire resistance limit of the fireproof coating are also measured by working instruments. On this basis, the subway company will also regularly test the fire protection system to detect the stability and integrity of the fire system. The third category is government inspection. Such examinations are generally based on the above two examinations. Government departments will invite experts in the field of industry to form inspection teams. They checked the situation of fire equipment and facilities and evaluated the conditions of fire prevention and control at the scene. Finally, they put forward opinions. Within the specified time, the subway company is required to carry out rectification. For those that seriously do not meet the engineering standards, it is required to stop operation and organize re-inspection.
We conducted on-site inspections of fire prevention facilities and equipment. We also examined the subway station fire equipment self-test reports and the relevant government inspection reports. Pictures of on-site investigation are shown in Figure 2. The following systems were examined: automatic fire alarm systems, gas fire extinguishing systems, fireproof doors, fireproof observation windows, ceiling screens, rail top air ducts, rail bottom air ducts, tunnel ventilation fans, jet fans, air valves, mufflers, wind pipes, evacuation lighting, and other fire prevention equipment. These objective assessment reports and field research surveys provided a realistic basis for us to assess the on-site factors in fire prevention facilities.
If the fire facility factor is a hard indicator of subway fire risk, then the management factor is a soft indicator of subway fire risk. Management activities require long-term input to have a favorable influence. According to data obtained from the Changzhou Metro Operations Branch, we considered four indicators: daily fire inspection B41, professional team building B42, emergency fire drills B43, and safety training B44. The subway operations branch conducts daily fire inspections and records the relevant inspection results. This requires the specific responsible person to address many types of dangerous incidents, record the closures and dates of rectification. This inspection report offers opportunities for guidance in our evaluation of management factors. According to the daily fire inspection reports of subway operations branches in 2020, the main problems were related to fire safety, education, training, risk management, external environment issues, equipment and facility problems. We found some examples in the inspection report, including the charging of security car batteries indoors, host failures of fire alarm system, leakage in the equipment monitoring room, failure to perform safety training, platform door control problems, and inadequate water supply in indoor control cable cabinet. Emergency drills and safety training are effective means to prevent fires [32]. In the construction phase of the weekly inspection report, we also found that there are many problems in the construction process, such as fire sealing being not standardized, exposed wiring on the fire damper, construction refuse not being removed, and fire hydrant pipeline leakage. These problems constitute unsafe factors for future fire risk. Pictures of on-site investigation are shown in Figure 3. In addition to ordinary fire emergency drills, Changzhou Metro also conducts other individual emergency drills, such as operation catenary disconnection emergency drills, earthquake emergency drills, operation train fault rescue emergency drills, and comprehensive antiterrorism attack emergency drills. More than 3000 subway workers receive fire safety training annually. These daily inspections and regular drills contribute to the overall readiness for the prevention of fires at key moments.
Few researchers have studied the influence of construction problems and the flammability of construction materials. We examined the fire resistance limit B51, the cable fire resistance limit B52, the train-fire calorific value B53, and the problem of construction quality B54. The fire resistance limit for fireproof sealing materials, wiring, and cables represents the maximum duration of normal operation for each component once a fire ignites [33]. In the construction materials used in the plugging of pipeline holes present in the subway system, a selection of fireproof glue, fireproof mud, fireproof coatings, and mineral wool board was examined [34]. The fire resistance of these materials largely determines the heat resistance of the wiring and exhaust pipes. Moreover, the fire resistance limit of wiring and cable directly determines the normal operation of both the fire extinguishing and automatic alarm systems. The reason is that these components require a circuit to operate. Train-fire heat is also a key parameter for investigating fire risk, and excessive heat causes serious thermal radiation in the direction of surrounding combustibles [35]. It has aggravated the severity of the fire. Finally, the problem of construction quality is a novel concept first proposed in this study. For the first time, the risk of potential fire safety hazards caused by unapproved processes in the construction stages is considered in relation to the subway operation stage. Few studies have considered the problems remaining after construction when examining subway fires. These problems are often investigated by the operating branch and require the original construction personnel to rectify them. The following scenarios serve as illustrations of this phenomenon. When proper construction technology standards are not adopted, sealing material may fall off and cannot effectively block leaks. Poor waterproofing treatment in the energy feed room may lead to the accumulation of water in the cable layer on rainy days. The manual fire valve may fail, causing the wiring to be exposed. The water gun head may be missing from the fire hydrant box in the station hall. The escalator evacuation indicator light may be dim. The maintenance mouth of the wall ditch in the comprehensive monitoring equipment room may not be blocked as required. Construction waste is not cleaned or removed, leaving the area prone to fire. Since the construction in these scenarios has already been completed, reworking a large area is difficult. With a lack of access to concealed works, only remedial measures can be implemented in some areas, which introduces uncertainty into the fire prevention capabilities of subway stations.

2.1. Risk Assessment Method

2.1.1. Analytical Hierarchy Process

AHP is a subjective weighting analysis method [36,37,38,39,40]. First, a hierarchical structure model is established, followed by experts scoring the relevant factors; subsequently, a judgment matrix is constructed. Finally, the weight of each index that meets the consistency standard is calculated by mathematical logic operation, and the consistency index (CI) and the consistency ratio (CR) were calculated based on Equations (1) and (2).
CR = CI RI < 0 . 10
CI = λ max n n 1
Equation (1) is used to determine whether the matrix meets the consistency requirements; if not, the calculation is repeated. The RI values are listed in Table 3.

2.1.2. Entropy Weight Method

The entropy weight method is an objective weighting evaluation method. The larger the information entropy value is, the lower the weighting is [41,42,43,44]. The specific calculation process is as follows:
(1)
The quantitative index values are forward or reverse processed.
positive indexes: (X − Min)/(Max − Min)
negative indexes: (Max − X)/(Max − Min)
Here, X is the primary data of each index, Max is the maximum value of the primary data of each index, and Min is the minimum value of the primary data of each index.
(2)
The standardized data are combined to calculate the information entropy of each index (Ej); the formula is as follows:
E j = 1 ln ( n ) i = 1 n p i j ln p i j , i = 1 , 2 n
(3)
The difference coefficient of each index(gj) is calculated according to the calculated information entropy, and the formula is as follows:
g j = 1 E j
(4)
The weighting Wj is calculated as follows:
W j = g j j = 1 m g j

2.1.3. Game Theory Combination Weighting

The limitations of analytic hierarchy process and entropy weight method are obvious. The former relies heavily on the experience, age and professional knowledge of experts. The latter has strict requirements for data format, and often affects the evaluation results because of the data format problem. Therefore, in order to solve the limitations of the application of these two methods, we propose a game theory combination weighting method to solve this problem. Game theory combinatorial weighting involves the linear combination of weightings obtained by different methods to seek the most accurate index weighting [45,46,47,48]. This study adopted a combination of the AHP and entropy weight method to avoid the deficiencies in either method alone, thus maximizing the accuracy of the estimation process. The specific steps of the game theory combinatorial weighting method are as follows:
(1)
The system of linear equations is equivalently transformed into optimal first derivative conditions by the matrix differential property as follows:
( ω 1 ω 1 T ω 1 ω 2 T ω 2 ω 1 T ω 2 ω 2 T ) [ α 1 α 2 ] = [ ω 1 ω 1 T ω 2 ω 2 T ]
(2)
After the optimal linear combination coefficient is obtained and normalized from Equation (8), the comprehensive weighting of game theory combinatorial weighting is finally obtained as follows:
W = α 1 * ω 1 T + α 2 * ω 2 T , α 1 * = α 1 α 1 + α 2 ; α 2 * = α 2 α 1 + α 2

2.2. Ranking Method

Based on the relevant literature, we construct the fire risk value model of Changzhou subway stations. The corresponding risk value is obtained by introducing the concept of relative closeness in TOPSIS method [49,50,51]. The specific steps of the relative closeness method are as follows:
(1)
denote the combination weighting matrix as follows:
β = ( β 1 β 2 β 3 β n )
The matrix after data standardization as follows:
X m = ( X 1 X 2 X 3 X n )
where m is the number of evaluation objects, n is the number of evaluation indicators.
(2)
Construct a weighted standardized decision matrix as follows:
Z m = ( Z 1 m Z 2 m Z 3 m Z k m ) = ( β 1 X 1 β 2 X 2 β 3 X 3 β k X k )
(3)
Determine the ideal solution and negative ideal solution with the following formula:
Z m + = max { Z 1 m , Z 2 m , , Z k m } , Z m - = min { Z 1 m , Z 2 m , , Z k m }
where Zm+ and Zm are positive and negative ideal solutions for each index of subway stations respectively.
(4)
The distance Dm+ and Dm from the feasible solution of any index to the positive and negative ideal solution are calculated respectively as follows:
D m + = k = 1 n ( Z k m Z m + ) 2 , D m = k = 1 n ( Z k m Z m ) 2
(5)
The relative closeness Cm is calculated, and the relative closeness is used to represent the fire risk value of each subway stations. The calculation formula as follows:
C m = D m D m + + D m

3. Case Study

3.1. Region of the Evaluation

Changzhou is located in the south of Jiangsu Province, between 31°09′–32°04′ N and 119°08′–120°12′ E, with an area of 4372 km2. The location map of Changzhou city is shown in Figure 4. We can clearly see the lakes around Changzhou. Changzhou is located in the Yangtze River Delta Economic Zone near Shanghai. The research target of this paper was the Changzhou Metro. The Changzhou Metro has two lines with a total length of 34.24 km. Lines 1 and 2 were opened on 21 September 2019, and 28 June 2021, respectively. Because Line 2 has been in operation for a relatively short period, the relevant data cannot constitute a scientific reference. Therefore, this study examined a total of 29 subway stations in Changzhou Metro Line 1 as the research area. The total length of the line is 34.24 km, including 31.635 km of underground track, 2.189 km of elevated track, 0.413 km of transition section, 27 underground stations, and 2 elevated stations. This study only analyzed underground stations; the two elevated stations in Line 1 were not considered within the scope of assessment.

3.2. Analysis Results for the AHP Method

We invited experts from different industry backgrounds to evaluate the indicators. The weight of each index in the AHP method was calculated using MATLAB. The weight table of AHP for primary indicators are listed in Table 4.
The judgment matrix and weight of human factor B1 are listed in Table 5.
The judgment matrix and weight of building factors B2 are listed in Table 6.
The judgment matrix and weighting of fire prevention facilities factor B3 are listed in Table 7.
The judgment matrix and weight of management factor B4 are listed in Table 8.
The judgment matrix and weight of construction and material factor B5 are listed in Table 9.
After calculating the weights of criterion layer and indicator layer, we obtained the comprehensive weight of AHP. The comprehensive weights calculated by the AHP are listed in Table 10.

3.3. Analysis Results for the Entropy Weight Method

The primary data and data standardization results are presented in Appendix A, Table A1 and Table A2. We collated the data in the daily inspection report record of the operation branch of Changzhou Rail Company and regarded the unsafe behavior of passengers and subway workers in the record as the criterion of the B11 index.
The weight of each part of the entropy weight method is listed in Table 11.

3.4. Analysis Results for Game Theory Combined with the Weighting Method

According to the subjective and objective weighting data in this study, the linear equations are as follows:
( 0 . 109 0 . 061 0 . 061 0 . 145 ) ( α 1 α 2 ) = ( 0 . 109 0 . 145 ) , α 1 = 0 . 577 , α 2 = 0 . 759 ,
Introduce into the formula 9 to solve. α1* = 0.432, α2* = 0.568. According to linear combination weighting, the final comprehensive weight is shown in Table 12. After reading a large number of previous references [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72], we compare the results calculated by the analytic hierarchy process method (AHP); entropy weight method (EW); and game and theory combined weight (GTCW). A comparison of results calculated by three different methods is shown in Figure 5.

3.5. Analysis Results for Ranking Method

According to the risk distribution and the degree of possible harm, the risk level of each subway station is determined by referring to the weighted score ratio of each factor. As listed in Table 13, the security risk level was divided into five levels from 1 (very high) to 5 (very low).
The fire risk values and ranking of each subway stations are listed in Table 14.
We use ARCGIS software to display the calculated risk value on the map. According to the risk level of each subway station, the corresponding colors in the table are marked. The fire risk level of Changzhou Metro Line 1 is shown in Figure 6.

4. Conclusions

To assess the risk of subway station fires, we proposed and analyzed the subway station fire risk assessment index system. Based on the combination weighting evaluation, the subway station fire risk value model was introduced. The final risk value was obtained and sorted using mathematical operations. The following conclusions were obtained regarding the combination weighting evaluation method:
(1) First, game theory combined weighting overcomes the limitations of subjective and objective evaluation methods. In the Figure 5, we can see clearly that the curve of game theory combination weighting method is in the middle of the other two curves. Whenever analytic hierarchy process or entropy weight method has a minimum or maximum weight, game theory combination weighting will correct it. The curve after linear weighting is closer to the real result, which effectively solves the limitations of analytic hierarchy process and entropy weight method. Meanwhile, the concept of relative closeness degree in TOPSIS method is introduced to represent the risk value, so that the risk value can be quantified and expressed more clearly.
(2) Second, according to the fire risk values for subway stations in Table 14, the two highest risk subway stations were CULTURAL PALACE and CHASHAN, and its risk level was very high. Four other subway stations also exhibited high and moderate risk, whereas eight subway stations had low risk levels, and 13 subway stations had very low risk levels. Regarding the weighting proportion of the evaluation index system, the top five factors were fire accident broadcasting and communication facilities B35, fire resistance limit B51, automatic fire alarm system B31, construction quality problems B54 and the width of stations B23. Among them, the remaining problems of construction quality remain a concern throughout the entire project life cycle. Therefore, more attention should be paid to the firefighting equipment, facilities of subway stations and the problems that occur in the facility construction stage. If the fire risk level of stations is very high, they should indeed close until the corresponding inspection meets the standard requirements. Moreover, if the fire risk level of stations is high, we believe that such subway stations should receive warnings. When the number of warnings reaches 3 or more, the site should be closed. Once the rectification is completed, the data in the Table A1 and the final risk value will change. To reduce the risk of fire, we offer some suggestions to the subway operation branch, which include strengthening the fire safety training awareness of personnel, increasing the number of emergency drills, handling security issues, and improving the emergency rescue system. The results of this assessment may serve as a reference for the local rail department and fire management department.
(3) Finally, although the risk value model was established through optimized combination weighting, the model still has several limitations. First, the legacy problem of construction quality is a novel concept, and accurately quantifying this factor through data indicators is challenging. Second, this study only examined and analyzed the subway station buildings, disregarding fires in the subway tunnels. Third, most of the subway stations are underground island buildings, and only a few are elevated platforms. Therefore, elevated platforms were not considered in this study. For a more comprehensive understanding of subway station fire risk, additional in-depth research is necessary.

Author Contributions

Conceptualization, W.J. and J.W.; methodology, W.J.; software, W.J; validation, J.W., Z.X. and Q.K.; formal analysis, J.J.; investigation, J.W.; resources, J.W.; data curation, W.J.; writing—original draft preparation, W.J.; writing—review and editing, Z.X.; visualization, J.J.; supervision, Q.K.; project administration, J.J.; funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Key Research and Development Plan (No.2021YFC3001203), the special Fund for Provincial Production Safety in Jiangsu Province (No.YJGL-TG-2020-1), the Science and Technology Program of Fire and Rescue Department Ministry of Emergency Management (2020XFCX33), the key research and development program of China (No.2019YFC0810701), research and Application Service Platform Project of API Manufacturing Environmental Protection and Safety Technology in China (2020-0107-3-1), and Postgraduate Research and Practice Innovation Program of Jiangsu Province (No.KYCX21_2885).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Primary data.
Table A1. Primary data.
STATIONB11/EAB12/IEB13/IEB21/m2B22/mB23/mB24/mB31/SETB32/SETB33/EAB34/SETB35/SETB36/SETB41/EAB42/IEB43/EAB44/EAB51/hB52/hB53/MWB54/EA
WUJIN YANJIANG RAILWAY STATION125484115,991273.71131.934618220945952500661211121.510.50
KEJIAOCHENG NAN043774213,142136.13132.334662325324352740271211221.510.50
KEJIAOCHENG BEI333654312,258207.224113.5346485259212526691051211221.510.55
YANZHENG DADAO447926825,662396.59916.53.434610402091045210271661211221.510.53
CHANGHONG ROAD133914311,259190.4113.3346547209325275977121111.51.510.53
XINTIANDI PARK639174013,741.45140.25133.434640820941527081651211131.510.53
HUTANG433904411,575.7183113.234628320920527481661211131.510.51
JUHU ROAD260254915,251.7284.8113346406209235210471951211231.510.53
CHA SHAN842275327,605494.975141.2346270209228521120326121123310.64
QINGLIANG TEMPLE243164312,269.4193.5110.857346240209210521005217121123210.510
TONGJIQIAO127434911,392186110.834629720918652604961211131.510.54
CULTURAL PALACE269596825,227317.133140.18236048620928052769125151123310.63
BOAI ROAD140944311,478194110.91734629520919852700961211131.510.54
CHANGZHOU RAILWAY STATION095985514,424178.001141.53553982092575270075151123310.52
CUIZHU247414214,421850121.734629820920152700671211231.510.52
CITIZENS’ SQUARE154615216,284730.5131.634644630420152459861211231.510.52
OLYMPIC SPORTS CENTER445374711,203.8183.95132.534633125914452344951211231.510.52
HE HAI139284722,688463112.434655835328252584186121123210.55
XINQU PARK127464717,435.96176.5132.434619021724052797136121123210.54
GLOBAL HARBOR155175212,198.93188.229112.43589622017052802661211231.510.62
FOREIGN LANGUAGE SCHOOL019984714,763.4286.4113.3346124217216527941571212131.510.57
BEIJIAO HIGH SCHOOL322934411,093186.005113.234637445789652712861211231.510.52
NORTH RAILWAY STATION127325314,619193.003133.335040446892152744671511231.510.62
XINQIAO16994111,361186114.234639744689852703671211231.510.51
TOURISM AND COMMERCE INSTITUTE021614618,629.64527.15115.434681740630731997371211231.510.53
XIN LONG131332713,796.97199.5133.634631635527431874351211231.510.41
FOREST PARK126944918,716.58655.66112.1346361382257311516185151123310.39
Table A2. Standardization of primary data.
Table A2. Standardization of primary data.
STATIONB11B12B13B21B22B23B24B31B32B33B34B35B36B41B42B43B44B51B52B53B54
WUJIN YANJIANG RAILWAY STATION0.8750.2080.6590.2970.1930.3640.3291.0000.9091.0000.5130.0000.8670.1330.5001.0001.0000.6671.0000.6670.000
KEJIAOCHENG NAN1.0000.4130.6340.1240.0000.3640.4061.0000.4420.8300.7520.0000.6620.0000.0001.0000.9000.6671.0000.6670.000
KEJIAOCHENG BEI0.6250.3000.6100.0710.1000.0000.6361.0000.5880.8070.7870.0000.7230.2671.0001.0000.9000.6671.0000.6670.500
YANZHENG DADAO0.5000.4600.0000.8820.3651.0000.6171.0000.0001.0000.9070.0000.4170.4670.5001.0000.9000.6671.0000.6670.300
CHANGHONG ROAD0.8750.3030.6100.0100.0760.0000.5981.0000.5221.0000.9870.0000.6460.1670.0001.0001.0001.0001.0000.6670.300
XINTIANDI PARK0.2500.3620.6830.1600.0060.3640.6171.0000.6691.0000.9770.0000.6890.4671.0001.0001.0000.0001.0000.6670.300
HUTANG0.5000.3020.5850.0290.0660.0000.5781.0000.8021.0001.0000.0000.6550.4670.5001.0001.0000.0001.0000.6670.100
JUHU ROAD0.7500.5980.4630.2520.2080.0000.5401.0000.6721.0000.9970.0000.4000.5671.0001.0000.9000.0001.0000.6670.300
CHA SHAN0.0000.3960.3661.0000.5030.5450.1951.0000.8161.0000.7690.0000.3381.0000.5001.0000.9000.0000.0001.0000.400
QINGLIANG TEMPLE0.7500.4060.6100.0710.0800.0000.1291.0000.8471.0000.7890.0000.4360.6330.0001.0000.9000.0000.6670.6671.000
TONGJIQIAO0.8750.2300.4630.0180.0700.0000.1181.0000.7871.0000.8160.0000.7780.2330.5001.0001.0000.0001.0000.6670.400
CULTURAL PALACE0.7500.7030.0000.8560.2540.5450.0000.0000.5871.0000.7110.0000.6370.3331.0000.0000.9000.0000.0001.0000.300
BOAI ROAD0.8750.3820.6100.0230.0810.0000.1411.0000.7891.0000.8020.0000.6960.2330.5001.0001.0000.0001.0000.6670.400
CHANGZHOU RAILWAY STATION1.0001.0000.3170.2020.0590.5450.2530.3570.6801.0000.7370.0000.6960.1671.0000.0000.9000.0000.0000.6670.200
CUIZHU0.7500.4540.6340.2021.0000.1820.2911.0000.7861.0000.7990.0000.6960.1330.0001.0000.9000.0001.0000.6670.200
CITIZENS’ SQUARE0.8750.5350.3900.3140.8330.3640.2721.0000.6290.6330.7990.0000.9020.2000.5001.0000.9000.0001.0000.6670.200
OLYMPIC SPORTS CENTER0.5000.4310.5120.0070.0670.3640.4441.0000.7510.8070.8620.0001.0000.2331.0001.0000.9000.0001.0000.6670.200
HE HAI0.8750.3630.5120.7020.4580.0000.4251.0000.5110.4440.7090.0000.7950.5330.5001.0000.9000.0000.6670.6670.500
XINQU PARK0.8750.2300.5120.3840.0570.3640.4251.0000.9000.9690.7560.0000.6130.3670.5001.0000.9000.0000.6670.6670.400
GLOBAL HARBOR0.8750.5410.3900.0670.0730.0000.4250.1431.0000.9580.8340.0000.6090.1330.5001.0000.9000.0001.0001.0000.200
FOREIGN LANGUAGE SCHOOL1.0000.1460.5120.2220.2110.0000.5981.0000.9700.9690.7820.0000.6160.4330.0001.0000.0000.0001.0000.6670.700
BEIJIAO HIGH SCHOOL0.6250.1790.5850.0000.0700.0000.5781.0000.7060.0420.0280.0000.6860.2000.5001.0000.9000.0001.0000.6670.200
NORTH RAILWAY STATION0.8750.2280.3660.2140.0800.3640.5980.7140.6740.0000.0000.0000.6590.1330.0000.0000.9000.0001.0001.0000.200
XINQIAO0.8750.0000.6590.0160.0700.0000.7701.0000.6810.0850.0260.0000.6940.1330.0001.0000.9000.0001.0000.6670.100
TOURISM AND COMMERCE INSTITUTE1.0000.1640.5370.4560.5480.0001.0001.0000.2360.2390.6811.0000.4430.0330.0001.0000.9000.0001.0000.6670.300
XIN LONG0.8750.2741.0000.1640.0890.3640.6551.0000.7670.4360.7181.0000.5480.0331.0001.0000.9000.0001.0000.3330.100
FOREST PARK0.8750.2240.4630.4620.7280.0000.3681.0000.7190.3320.7371.0000.0000.5331.0000.0000.9000.0000.0000.0000.900

References

  1. Hao, H.; Wang, H.W.; Yi, R. Hybrid modeling of China’s vehicle ownership and projection through 2050. Energy 2011, 36, 1351–1361. [Google Scholar] [CrossRef]
  2. Dargay, J.; Gately, D.; Sommer, M. Vehicle ownership and income growth, worldwide: 1960–2030. Energy J. 2007, 28, 143–170. [Google Scholar] [CrossRef] [Green Version]
  3. Lorenzo, C.; Giulia, G.; Gabriele, P. Human reliability in railway engineering: Literature review and bibliometric analysis of the last two decades. Saf. Sci. 2022, 151, 105755. [Google Scholar]
  4. Teodosiu, C.I.; Ilie, V.; Dumitru, R.G.; Teodosiu, R.S. Assessment of ventilation efficiency for emergency situations in subway systems by CFD modeling. Build. Simul. 2016, 9, 319–334. [Google Scholar] [CrossRef]
  5. Nezhad, H.; Zivdar, H.; Amirnia, A. Assessment of fire risk in passenger trains in tunnels using the FMEA model and Fuzzy theory (A case study in the Zagros railway). Curr. World Environ. 2015, 10, 1158–1170. [Google Scholar] [CrossRef]
  6. Matellini, D.B.; Wall, A.D.; Jenkinson, I.D.; Wang, J.; Pritchard, R. Modelling dwelling fire development and occupancy escape using Bayesian network. Reliab. Eng. Syst. Saf. 2013, 114, 75–91. [Google Scholar] [CrossRef]
  7. Luo, Z.H.; Zeng, L.; Pan, H.Z.; Hu, Q.J.; Liang, B.; Han, J.Q. Research on construction safety risk assessment of new subway station close-attached undercrossing the existing operating station. Math. Probl. Eng 2019, 2019, 3215219. [Google Scholar] [CrossRef] [Green Version]
  8. Gao, J.P.; Xu, Z.S.; Liu, D.L.; Cao, H.H. Application of the model based on fuzzy consistent matrix and AHP in the assessment of fire risk of subway tunnel. Procedia Eng. 2014, 71, 591–596. [Google Scholar] [CrossRef] [Green Version]
  9. Liu, T.; Chen, Z.Y.; Yuan, Y.; Shao, X.Y. Fragility analysis of a subway station structure by incremental dynamic analysis. Adv. Struct. Eng. 2017, 20, 1111–1124. [Google Scholar] [CrossRef]
  10. Liu, J.Y.; Du, Z.J.; Ma, L.X.; Liu, C.; Ma, J.J. Identification and assessment of subway construction risk: An integration of AHP and experts grading method. Adv. Civ. Eng. 2021, 2021, 6661099. [Google Scholar] [CrossRef]
  11. Wu, J.S.; Hu, Z.Q.; Chen, J.Y.; Li, Z. Risk assessment of underground subway stations to fire disasters using Bayesian network. Sustainability 2018, 10, 3810. [Google Scholar] [CrossRef] [Green Version]
  12. Zhang, L.M.; Wu, X.G.; Liu, M.J.; Liu, W.L.; Ashuri, B. Discovering worst fire scenarios in subway stations: A simulation approach. Autom. Constr. 2019, 99, 183–196. [Google Scholar] [CrossRef]
  13. Peng, M.; Shi, L.; He, K.; Yang, H.; Cong, W.; Cheng, X.D.; Richard, Y. Experimental study on fire plume characteristics in a subway carriage with doors. Fire. Technol. 2020, 56, 401–423. [Google Scholar] [CrossRef]
  14. Lan, Y.J.; Han, B.M.; Li, D.W. Research on safety risk assessment of large-scale railway passenger station. Appl. Mech. Mater 2013, 2547, 1923–1926. [Google Scholar] [CrossRef]
  15. Wang, J.W.; Liu, S.; Song, Y.H.; Wang, J.; Wu, H. Environmental risk assessment of subway station construction to achieve sustainability using the intuitionistic fuzzy analytic hierarchy process and set pair analysis. Discrete. Dyn. Nat. Soc. 2021, 2021, 5541493. [Google Scholar] [CrossRef]
  16. Rie, D.H.; Hwang, M.W.; Kim, S.J.; Yoon, S.W.; Ko, J.W.; Kim, H.Y. A study of optimal vent mode for the smoke control of subway station fire. Tunn. Undergr. Space Technol. 2006, 21, 300–301. [Google Scholar] [CrossRef]
  17. Lee, M.; Hur, N. A detailed CFD simulation of the 2003 DAEGU metro station fire. Int. J. Air-Cond. Refri 2012, 20, 1250014. [Google Scholar] [CrossRef]
  18. Wang, W.H.; He, T.F.; Huang, W.; Shen, R.Q.; Wang, Q.S. Optimization of switch modes of fully enclosed platform screen doors during emergency platform fires in underground metro station. Tunn. Undergr. Space Technol. 2018, 81, 277–288. [Google Scholar] [CrossRef]
  19. Meng, N.; Hu, L.; Zhu, S.; Yang, L. Effect of smoke screen height on smoke flow temperature profile beneath platform ceiling of subway station. Tunn. Undergr. Space Technol. 2014, 43, 204–212. [Google Scholar] [CrossRef]
  20. Giachetti, B.; Couton, D.; Plourde, F. Smoke spreading analysis from an experimental subway scale model. Fire Saf. J. 2016, 86, 75–82. [Google Scholar] [CrossRef]
  21. Giachetti, B.; Couton, D.; Plourde, F. Smoke spreading analyses in a subway fire scale model. Tunn. Undergr. Space Technol. 2017, 70, 233–239. [Google Scholar] [CrossRef]
  22. Karaaslan, S.; Dinler, N.; Yucel, N. Numerical fire simulation in subway station tunnel by using different combustion models. J. Fac. Eng. Archit. Gazi Univ. 2011, 26, 533–547. [Google Scholar]
  23. Wen, Y.M.; Leng, J.W.; Shen, X.B.; Han, G.; Sun, L.J.; Yu, F. Environmental and health effects of ventilation in subway stations: A literature review. Int. J. Environ. Res. Public Health 2020, 17, 1084. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Roh, J.S.; Ryou, H.S.; Park, W.H.; Jang, Y.J. CFD simulation and assessment of life safety in a subway train fire. Tunn. Undergr. Space Technol. 2009, 24, 447–453. [Google Scholar] [CrossRef]
  25. Corri, Z.; Laura, J.S.; Marha, G.; Margaret, H. Terrorist critical infrastructures, organizational capacity and security risk. Saf. Sci. 2018, 110, 1016. [Google Scholar]
  26. Mehmet, B.U.; Ayberk, K.; Anil, Y.; Eren, E.O.; Ashutosh, K. A stop safety index to address pedestrian safety around bus stops. Saf. Sci. 2021, 133, 105017. [Google Scholar]
  27. Margarita, N.; Dominique, B.; Laetitia, F. A proposed new approach to light rail safety management in Spain and other countries. Saf. Sci. 2019, 118, 740–751. [Google Scholar]
  28. Zhu, A. Statistical analysis of domestic and international subway fire accidents in 2000–2019. Urban Mass Transit. 2020, 23, 148–150. [Google Scholar]
  29. Chen, J.F.; Liu, C.; Meng, Y.Y.; Zhong, M.H. Multi-Dimensional evacuation risk evaluation in standard subway station. Saf. Sci. 2021, 142, 105392. [Google Scholar] [CrossRef]
  30. Feng, J.R.; Gai, W.M.; Yan, Y.B. Emergency evacuation risk assessment and mitigation strategy for a toxic gas leak in an underground space: The case of a subway station in Guangzhou, China. Saf. Sci. 2021, 134, 105039. [Google Scholar] [CrossRef]
  31. Luo, N.; Li, A.G.; Gao, R.; Tian, Z.G.; Hu, Z.P. Smoke confinement utilizing the USME ventilation mode for subway station fire. Saf. Sci. 2014, 70, 202–210. [Google Scholar] [CrossRef]
  32. Pedro, R.; Andrés, F. The great Valparaiso fire and fire safety management in Chile. Fire Technol. 2015, 51, 229–242. [Google Scholar]
  33. Zhong, B.; Jiang, Y.; Zhang, J. Research on new blast and fire stopping system for UHVDC converter station valve hall. Fire Sci. Technol. 2021, 40, 231–234. [Google Scholar] [CrossRef]
  34. Gao, R.; Li, A.G.; Lei, W.J.; Zhao, Y.J.; Zhang, Y.; Deng, B.S. Study of a proposed tunnel evacuation passageway formed by opposite-double air curtain ventilation. Saf. Sci. 2012, 50, 1549–1557. [Google Scholar] [CrossRef]
  35. Henrik, B.; Ove, N.; Atle, W.H.; Geir, S.B. Emergency preparedness for tunnel fires-A systems-oriented approach. Saf. Sci. 2021, 143, 105408. [Google Scholar]
  36. Irina, C.; Drita, K.; Tiberiu, I. AHP, a Reliable Method for Quality Decision Making: A Case Study in Business. Sustainability 2021, 13, 13932. [Google Scholar]
  37. Ibifuro, K.G.; Sarinova, S.; Linda, Y.; Ann, C.; David, S. Establishing the relative importance of specific sustainability themes that influence women’s choice of engineering as a career using the analytical hierarchy Process. Sustainability 2022, 14, 566. [Google Scholar]
  38. Amr, S.Z.; Bahaa, E.; Kotb, M.K.; Yang, H.; Abdulrazak, H.A.; Reda, M.H.A.; Elkadeem, M.R. A high-resolution wind farms suitability mapping using GIS and fuzzy AHP approach: A national-level case study in Sudan. Sustainability 2022, 14, 358. [Google Scholar]
  39. Zheng, X.L.; Chen, H.L.; Xue, S.; Zheng, C.S.; Qi, F.L. Study on explosion risk assessment of low-concentration gas safe combustion system based on FAHP-fuzzy fault tree. Qual. Reliab. Eng. Int. 2021, 38, 484–500. [Google Scholar] [CrossRef]
  40. Augustinas, M.; Andrej, B.; Olga, R.S.; Tatjana, V. Decision tree and AHP methods application for projects assessment: A case study. Sustainability 2021, 13, 5502. [Google Scholar]
  41. Omidvar, M.; Nirumand, F. An extended VIKOR method based on entropy measure for the failure modes risk assessment—A case study of the geothermal power plant (GPP). Saf. Sci. 2017, 92, 160–172. [Google Scholar]
  42. Shen, Z.Y.; Zhao, Q.Q.; Fang, Q.M. Analysis of green traffic development in Zhoushan based on entropy weight TOPSIS. Sustainability 2021, 13, 8109. [Google Scholar] [CrossRef]
  43. Chen, H.; Shang, Z.H.; Cai, H.J.; Zhu, Y. An optimum irrigation schedule with aeration for greenhouse tomato cultivations based on entropy evaluation method. Sustainability 2019, 11, 4490. [Google Scholar] [CrossRef] [Green Version]
  44. Li, Y.G.; Sun, M.H.; Yuan, G.H.; Zhou, Q.; Liu, J.Y. Study on development sustainability of atmospheric environment in Northeast China by rough set and entropy weight method. Sustainability 2019, 11, 3793. [Google Scholar] [CrossRef] [Green Version]
  45. Bryan, L.M.; Christina, L.B. Modeling decision and game theory based pedestrian velocity vector decisions with interacting individuals. Saf. Sci. 2016, 87, 116–130. [Google Scholar]
  46. Zou, Q.; Zhang, T.; Liu, W. A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2021, 235, 610–626. [Google Scholar] [CrossRef]
  47. Florian, D.; Markus, L.; Lotte, V.; Marcus, W.; Alexander, Z.; Frank, S. Public-private collaborations in emergency logistics: A framework based on logistical and game-theoretical concepts. Saf. Sci. 2021, 141, 105301. [Google Scholar]
  48. Yamamoto, T.; Ito, H.; Nii, M.; Okabe, T.; Morita, S.; Yoshimura, J. A single ‘weight-lifting’ game covers all kinds of games. Roy. Soc. Open. Sci 2020, 6, 191602. [Google Scholar] [CrossRef] [Green Version]
  49. Bian, T.; Zheng, H.Y.; Yin, L.K.; Deng, Y. Failure mode and effects analysis based on D numbers and TOPSIS. Qual. Reliab. Eng. Int. 2018, 34, 2268. [Google Scholar] [CrossRef]
  50. Hasan, S.; Mualla, G.Y.; Sebnem, Y.B. A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA. Qual. Reliab. Eng. Int. 2015, 32, 1791. [Google Scholar]
  51. Mehdi, T.; Mehdi, N. Estimating and ranking the impact of human error roots on power grid maintenance group based on a combination of mathematical expectation, Shannon entropy, and TOPSIS. Qual. Reliab. Eng. Int. 2021, 37, 2941. [Google Scholar]
  52. Eirik, B.A.; Maria, F.M.; Jon, T.S.; Frank, A.; Hakon, B.A. Prioritising investments in safety measures in the chemical industry by using the Analytic Hierarchy Process. Reliab. Eng. Syst. Saf. 2020, 198, 106811. [Google Scholar]
  53. Merve, B.; Evrencan, O. A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment. Reliab. Eng. Syst. Saf. 2021, 205, 107238. [Google Scholar]
  54. Krantiraditya, D.; Ashish, G.; Kritika, S.; Nirmal, F.X.; Maiti, J. An integrated RFUCOM-RTOPSIS approach for failure modes and effects analysis: A case of manufacturing industry. Reliab. Eng. Syst. Saf. 2022, 221, 108333. [Google Scholar]
  55. Jose, C.; Jorfe, F.; Nazare, R. Customized risk assessment in military shipbuilding. Reliab. Eng. Syst. Saf. 2020, 197, 106809. [Google Scholar]
  56. Silvia, C.; IIyas, M.; Julio, B.; Fortunato, C.; Antonella, C.; Joaquin, I.; Marco, L.C. A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm. Reliab. Eng. Syst. Saf. 2021, 205, 107265. [Google Scholar]
  57. Mohsen, N.; Hossein, M.R.; Nima, K.; Biswajeet, P. Forest fire induced natech risk assessment: A survey of geospatial technologies. Reliab. Eng. Syst. Saf. 2019, 191, 106558. [Google Scholar]
  58. Justyna, P.M.; Hanna, L.; Matthias, R. Decision-Tree based methodology aid in assessing the sustainable development of a manufacturing company. Sustainability 2022, 14, 6362. [Google Scholar]
  59. Truong, T.H.; Nguyen, A.T.; Luu, H.V.; Luong, T.L.; Do, D.H.; Luong, T.A.; Nghiem, X.H.; Luu, Q.D. Prioritization of factors impacting lecturer research productivity using an improved fuzzy analytic hierarchy process approach. Sustainability 2022, 14, 6132. [Google Scholar]
  60. Jawa, A.G.; Rozana, Z.; Eeydzah, A.; Khairulzan, Y.; Abdul, R.M.S.; Loganathan, V.M.; Muhamad, A.Y.; Noraziah, W.; Sitie, M.S. Effects of the COVID-19 pandemic on construction work progress: An on-site analysis from the Sarawak construction project, Malaysia. Sustainability 2022, 14, 6007. [Google Scholar]
  61. Ali, A.; Gholam, A.S. Integration of functional resonance analysis with multicriteria analysis for sociotechnical systems risk management. Risk. Anal. 2021, 42, 13796. [Google Scholar]
  62. Sudipa, S.; Andrej, L. Progressing the aerospace performance factor toward nonlinear interactions. Risk. Anal 2022, 42, 13877. [Google Scholar]
  63. Onur, H.; Saliha, C. Determination of emergency assembly point for industrial accidents with AHP analysis. J. Loss Prev. Process Ind. 2021, 69, 104386. [Google Scholar]
  64. Zaki, S.; Oleg, S.; Yuri, L. A novel tool for Bayesian reliability analysis using AHP as framework for prior elicitation. J. Loss Prev. Process Ind. 2020, 64, 104024. [Google Scholar]
  65. Laith, A.H.; Mohammad, A.K. Loss prevention in turnaround maintenance projects by selecting contractors based on safety criteria using the analytic hierarchy process (AHP). J. Loss Prev. Process Ind. 2015, 34, 115–126. [Google Scholar]
  66. Seyed, M.G.; Zahra, B.; Jamshid, M. An effective approach for assessing risk of failure in urban sewer pipelines using a combination of GIS and AHP-DEA. Process Saf. Environ. Prot. 2020, 133, 275–285. [Google Scholar]
  67. Ahmad, A.D.; Syeda, Z.H.; Noor, Q.; Vasililki, K.; Mahmoud, M.E. A stochastic approach to evaluating the economic impact of disruptions in feedstock pipelines on downstream production. Process Saf. Environ. Prot. 2022, 162, 187–199. [Google Scholar]
  68. Almutairi, K.; Dehshiri, S.J.H.; Dehshiri, S.S.H.; Mostafaeipour, A.; Hoa, A.X.; Techato, K. Determination of optimal renewable energy growth strategies using SWOT analysis, hybrid MCDM methods, and game theory: A case study. Int. J. Energy Res. 2022, 46, 6766–6789. [Google Scholar] [CrossRef]
  69. Tran, T.N.; Nguyen, T.V.; Shim, K.; An, B. A Game Theory based clustering protocol to support multicast routing in cognitive radio mobile ad hoc networks. IEEE. Access 2020, 8, 141310–141330. [Google Scholar] [CrossRef]
  70. Raihan, A.T.; Bauer, S.; Mukhopadhaya, S. An AHP based approach to forecast groundwater level at potential recharge zones of Uckermark District, Brandenburg, Germany. Sci. Rep. 2022, 12, 6365. [Google Scholar] [CrossRef]
  71. Sven, E.M.; Hakan, F.; Kazunori, H. Fire safety design based on calculations: Uncertainty analysis and safety verification. Fire. Saf. J. 1996, 27, 305–334. [Google Scholar]
  72. Brzezinska, D.; Bryant, P. Risk index method-A tool for building fire safety assessments. Appl. Sci. 2021, 11, 3566. [Google Scholar] [CrossRef]
Figure 1. Overview of the research concept.
Figure 1. Overview of the research concept.
Sustainability 14 07275 g001
Figure 2. On-site investigation: (a) tunnel ventilation fan, (b) smoke exhaust pipe and muffler, and (c) rail bottom air duct.
Figure 2. On-site investigation: (a) tunnel ventilation fan, (b) smoke exhaust pipe and muffler, and (c) rail bottom air duct.
Sustainability 14 07275 g002
Figure 3. On-site investigation: (a) fire blocking, (b) exposed wiring on the fire damper, (c) construction refuse not removed, and (d) fire hydrant leakage.
Figure 3. On-site investigation: (a) fire blocking, (b) exposed wiring on the fire damper, (c) construction refuse not removed, and (d) fire hydrant leakage.
Sustainability 14 07275 g003
Figure 4. Location map of Changzhou city.
Figure 4. Location map of Changzhou city.
Sustainability 14 07275 g004
Figure 5. Comparison of results calculated by three different methods.
Figure 5. Comparison of results calculated by three different methods.
Sustainability 14 07275 g005
Figure 6. Fire risk grade diagram of Changzhou Metro Line 1.
Figure 6. Fire risk grade diagram of Changzhou Metro Line 1.
Sustainability 14 07275 g006
Table 1. Comparison with previous methods.
Table 1. Comparison with previous methods.
MethodsCharacteristicAdvantagesLimitations
Analytic hierarchy processMethod of subjectively determining weightsystem analysisaffected by subjective factors of analysts
Entropy weight methodMethod of objectively determining weightStrong objectivityrequirement for data format
Safety checklist analysisThe safety level was assessed by item-by-item inspection according to the standard required checklist prepared in advanceeasyheavy workload
Preliminary hazard analysisAnalysis of risk and harmful factors in the systemeasy for operationaffected by subjective factors of analysts
Fault tree analysisDeductive method to calculate accident probability from basic event probabilitysoftware can be used heavy workload and distortion
Hazard and operability analysisThe results can be used to evaluate both design and operationdetailed resultsaffected by subjective factors of analysts
Game theory combination weightingLinear weighting based on game theoryIt is necessary to solve linear equations. The results are more convincing by combining subjective and objective methods
Technique for order preference by similarity to an ideal solution (TOPSIS)It is a comprehensive evaluation method that can make full use of the information of the primary dataThe results can accurately reflect the gap between the evaluation schemes
Game theory combination weighting-TOPSISWe hope that through this combination method to solve the limitations of previous work, and make the results more scientific and reasonable
Table 2. Evaluation of the index system for fire risk.
Table 2. Evaluation of the index system for fire risk.
Evaluation index system for fire riskHuman factor B1Fire safety awareness of passengers and subway workers B11
passenger flow B12
Number of employees in subway station B13
Building characteristics B2subway station area B21
the station length B22
the station width B23
the distance between the building and the nearest fire station B24
Fire prevention facilities factors B3automatic fire alarm system B31
fire extinguishing systems B32
fire separation facilities B33
smoke control facilities B34
fire accident broadcast communication facilities B35
fire emergency lighting and evacuation instructions B36
Management factor B4daily fire inspection B41
professional team building B42
emergency fire drills B43
safety training B44
Construction and material factors B5fire resistance limit of material B51
fire resistance limit of cables B52
the train-fire calorific value B53
the problem of construction quality B54
Table 3. AHP weighting table.
Table 3. AHP weighting table.
n123456789
RI000.580.901.121.241.321.411.45
Table 4. Weight table of primary indicators.
Table 4. Weight table of primary indicators.
B1B2B3B4B5CR
B111/31/71/41/6CR = 0.078
<0.1
B2311/61/31/5
B376153
B4431/511/4
B5651/341
ω0.0390.0680.4900.1220.281
Table 5. Weight of human factor B1.
Table 5. Weight of human factor B1.
B1B11B12B13CR
B11135CR = 0.046
<0.1
B121/314
B131/51/41
ω0.1090.3450.547
Table 6. Weight of building characteristic B2.
Table 6. Weight of building characteristic B2.
B2B21B22B23B24CR
B21131/41/6CR = 0.078
<0.1
B221/311/51/6
B234511/3
B246631
ω0.1040.0570.2770.561
Table 7. Weight of fire prevention facilities factor B3.
Table 7. Weight of fire prevention facilities factor B3.
B3B31B32B33B34B35B36CR
B31174536CR = 0.073
<0.1
B321/711/51/41/61/3
B331/45121/34
B341/541/211/53
B351/363516
B361/631/41/31/61
ω0.4250.0320.1340.0890.2710.050
Table 8. Weight of management factor B4.
Table 8. Weight of management factor B4.
B4B41B42B43B44CR
B411745CR = 0.066
<0.1
B421/711/51/3
B431/4513
B441/531/31
ω0.5950.0540.2380.113
Table 9. Weight of construction and material factor B5.
Table 9. Weight of construction and material factor B5.
B4B51B52B53B54CR
B5111/431/5CR = 0.063
<0.1
B524151/3
B531/31/511/7
B545371
ω0.1090.2810.0540.557
Table 10. Weight table of the AHP.
Table 10. Weight table of the AHP.
Criterion LayerWeightIndicator LayerWeightComprehensive Total Weight
human factor B10.039fire safety awareness of passengers and subway workers B110.1090.004
passenger flow B120.3450.013
number of employees in subway station B130.5470.021
building characteristic B20.068subway station area B210.1040.007
the station length B220.0570.004
the station width B230.2770.019
the distance between the building and the nearest fire station B240.5610.038
fire prevention facilities factor B30.490automatic fire alarm system B310.4250.208
fire extinguishing systems B320.0320.016
fire separation facilities B330.1340.066
smoke control facilities B340.0890.044
fire accident broadcast communication facilities B350.2710.133
fire emergency lighting and evacuation instructions B360.0500.025
management factor B40.122daily fire inspection B410.5950.073
professional team building B420.0540.007
emergency fire drills B430.2380.029
safety training B440.1130.014
construction and material factor B50.281fire resistance limit B510.1090.031
the cable fire resistance limit B520.2810.079
the train-fire calorific value B530.0540.015
the problem of construction quality B540.5570.157
Table 11. Entropy method weighting method.
Table 11. Entropy method weighting method.
Criterion LayerWeightIndicator LayerWeight
human factorB10.040fire safety awareness of passengers and subway workers B110.009
passenger flow B120.018
number of employees in subway station B130.013
building characteristicB20.250subway station area B210.065
the station length B220.066
the station width B230.101
the distance between the building and the nearest fire station B240.018
fire facilities factorB30.330automatic fire alarm system B310.009
fire extinguishing systems B320.008
fire separation facilities B330.018
smoke control facilities B340.014
fire accident broadcast communication facilities B350.273
fire emergency lighting and evacuation instructions B360.008
management factorB40.108daily fire inspection B410.032
professional team building B420.051
emergency fire drills B430.020
safety training B440.005
construction and material factorB50.272fire resistance limit B510.212
the cable fire resistance limit B520.021
the train-fire calorific value B530.007
the problem of construction quality B540.032
Table 12. Game theory combinatorial weighting.
Table 12. Game theory combinatorial weighting.
Criterion LayerIndicator LayerWeight
human factor B1fire safety awareness of passengers and subway workers B110.007
passenger flow B120.016
number of employees in subway station B130.016
building characteristic B2subway station area B210.040
the station length B220.039
the station width B230.066
the distance between the building and the nearest fire station B240.027
fire facilities factor B3automatic fire alarm system B310.095
fire extinguishing systems B320.011
fire separation facilities B330.039
smoke control facilities B340.027
fire accident broadcast communication facilities B350.213
fire emergency lighting and evacuation instructions B360.015
management factor B4daily fire inspection B410.050
professional team building B420.032
emergency fire drills B430.024
safety training B440.009
construction and material factor B5fire resistance limit B510.134
the cable fire resistance limit B520.046
the train-fire calorific value B530.010
the problem of construction quality B540.086
Table 13. Risk levels for fires.
Table 13. Risk levels for fires.
Serial NumberRisk LevelRange of ScoresColor
1Very high0.81–1
2high0.61–0.80
3moderate0.41–0.60
4low0.21–0.40
5Very low0–0.20
Table 14. Fire risk value of subway station.
Table 14. Fire risk value of subway station.
STATIONRisk ValueRisk LevelRankColor
CULTURAL PALACE0.828Very high1
CHA SHAN0.826Very high2
YANZHENG DADAO0.799high3
HE HAI0.657high4
FOREST PARK0.439moderate5
TOURISM AND COMMERCE INSTITUTE0.429moderate6
XINQU PARK0.369low7
CITIZENS’ SQUARE0.341low8
CHANGZHOU RAILWAY STATION0.331low9
JUHU ROAD0.299low10
WUJIN YANJIANG RAILWAY STATION0.288low11
CUI ZHU0.239low12
NORTH RAILWAY STATION0.219low13
FOREIGN LANGUAGE SCHOOL0.216low14
XINTIANDI PARK0.189Very low15
XIN LONG0.179Very low16
KEJIAOCHENG NAN0.174Very low17
GLOBAL HARBOR0.172Very low18
QINGLIANG TEMPLE0.142Very low19
OLYMPIC SPORTS CENTER0.127Very low20
BOAI ROAD0.117Very low21
KEJIAOCHENG BEI0.116Very low22
HU TANG0.098Very low23
CHANGHONG ROAD0.094Very low24
TONGJIQIAO0.074Very low25
BEIJIAO HIGH SCHOOL0.070Very low26
XIN QIAO0.047Very low27
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ju, W.; Wu, J.; Kang, Q.; Jiang, J.; Xing, Z. Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method. Sustainability 2022, 14, 7275. https://doi.org/10.3390/su14127275

AMA Style

Ju W, Wu J, Kang Q, Jiang J, Xing Z. Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method. Sustainability. 2022; 14(12):7275. https://doi.org/10.3390/su14127275

Chicago/Turabian Style

Ju, Weiyi, Jie Wu, Qingchun Kang, Juncheng Jiang, and Zhixiang Xing. 2022. "Fire Risk Assessment of Subway Stations Based on Combination Weighting of Game Theory and TOPSIS Method" Sustainability 14, no. 12: 7275. https://doi.org/10.3390/su14127275

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop