Development of a Fire Risk Assessment Program for Submerged Tunnels

Abstract

Submerged tunnels are an innovative infrastructure solution for connecting roads and railways, especially in areas where conventional bridge or overland tunnel construction is limited by deep waterways, narrow straits, or dense urban development. In such regions, submerged tunnels offer an efficient and less intrusive alternative that overcomes geographical constraints. However, unlike conventional ground-level or subsea tunnels, submerged tunnels have unique structural and environmental characteristics, which necessitate the development of a dedicated evaluation system for responding to fire and other disasters. In this study, a quantitative fire risk assessment program (SFT_QRA) was developed by reflecting the specific characteristics of submerged tunnels. The program was applied to both road and railway tunnels to obtain evaluation results. First, to more realistically reflect the fire risk within submerged tunnels, the latest statistical data were used to update fire occurrence probabilities and the proportion of vulnerable users. In addition, the optimal smoke control mode for structural stop zones in ultra-long tunnels was analyzed to derive strategies for establishing a safe evacuation environment. Second, an Excel VBA-based assessment program was developed to improve user convenience and was structured to enable fire analysis and evacuation simulations. Third, in order to verify the accuracy and reliability of the developed program, a comparative analysis was conducted against commercial quantitative risk assessment programs. As a result, the total risk error rate was 0.4% for road tunnels and within 5.0% for railway tunnels, showing similar levels of results. This study advances quantitative risk assessment methods by incorporating the unique features of submerged tunnels and implementing them in a validated program. Through this approach, it presents a practical solution that can contribute to the advancement of tunnel fire safety technologies and the overall enhancement of tunnel safety.

1. Introduction

Submerged tunnels are emerging as next-generation transportation infrastructure capable of replacing large-scale maritime bridges and subsea tunnels. They are gaining attention as innovative infrastructure solutions that can overcome geographical barriers and have the potential to significantly contribute to the development of integrated road and railway networks. Research and demonstration projects on submerged tunnel technology are actively being carried out around the world, and in particular,, Norway and China have examined its feasibility as part of large-scale national projects (Shin and Park, 2007) [1]. As a cutting-edge tunnel technology capable of stable operation even in deep waters and strong tidal environments, submerged tunnels are attracting increasing attention. Especially as an efficient means for constructing integrated transportation networks connecting roads and railways, they are an innovative infrastructure solution for connecting roads and railways, especially in areas where conventional bridge or overland tunnel construction is limited by deep waterways, narrow straits, or dense urban development.

In 1972, a night train fire in the Hokuriku Tunnel in Japan resulted in many deaths due to carbon monoxide poisoning. This incident prompted the Japanese government to strengthen tunnel safety standards by enhancing fire detection and early response equipment and by introducing structural safety designs, including parallel evacuation passages and smoke control systems. In 1999, the Mont Blanc Tunnel fire occurred, where the lack of an adequate ventilation system and emergency response measures led to a significant number of casualties. In 2008, the Channel Tunnel fire emphasized the importance of fire protection and evacuation systems inside tunnels. More recently, in 2025, fires broke out in the I-80 Green River Tunnel in Wyoming and the Caldecott Tunnel in California, United States. In the case of the I-80 Green River Tunnel, the absence of sprinklers and fire suppression systems allowed the fire to spread uncontrollably, and the inadequate ventilation system caused smoke to fill the tunnel quickly, making evacuation difficult. In contrast, the Caldecott Tunnel’s ventilation fans effectively minimized smoke spread, and the tunnel was reopened approximately three hours after the fire was extinguished. In response to these incidents, national governments and international organizations have revised safety standards. PIARC’s recommendations and updates to NFPA 502 have introduced requirements for longitudinal ventilation, evacuation cross passages, and structural fire resistance. These standards have had a significant influence on the assumptions used in the SFT_QRA model, particularly with regard to evacuation intervals and the classification of fire scenarios. Establishing thorough fire protection systems and response strategies can help minimize damage.

However, unlike conventional tunnels, submerged tunnels require even more comprehensive and robust systems. Submerged tunnels differ from conventional ground-level and subsea tunnels in terms of structural and environmental characteristics, and the risks in the event of fire or other disasters tend to be significantly higher compared to those in above-ground or mountain tunnels due to limited ventilation, constrained evacuation routes, and high-water-pressure environments. In particular, complex evacuation procedures, the difficulty of smoke flow control, and the need to ensure the reliability of ventilation systems pose serious safety challenges. Moreover, comprehensive technical evaluations are required, including considerations for long-term maintenance and environmental impact.

Despite these challenges, public awareness regarding the safety of submerged tunnels remains low, and research on fire prevention and risk assessment for such tunnels is still insufficient (Lu et al., 2024) [2]. Therefore, the establishment of a fire and emergency response system tailored to these conditions is urgently required. In South Korea, the idea of building an ultra-long subsea tunnel between Jeju Island and Mokpo has been discussed for many years. However, the project remains in its early research phase and has not yet been implemented.

In this study, recent statistical data were used to refine fire occurrence probabilities and the proportion of vulnerable users, and optimal smoke control modes for structural rescue bases were derived through simulation. A VBA-based program was developed, and fire analysis and evacuation simulation functions were integrated to construct a practical evaluation system. The outcomes of this study are highly significant in that they present a completed, practical evaluation tool that takes into account the unique fire risk factors of submerged tunnels. In the future, it is expected to contribute to the establishment of safety standards for submerged tunnels and the advancement of fire protection technologies.

2. Literature Review

Research on fire safety in road and railway tunnels has been actively conducted for a long time, incorporating various experimental studies and simulation techniques. Notably, Ingason et al. (2015) [3] provided comprehensive insights into tunnel fire safety by addressing fire dynamics, smoke control, and evacuation strategies. Additionally, Persson (2002) [4] applied quantitative risk analysis (QRA) to minimize casualties in road tunnel fires, highlighting that hazardous material transport accidents—such as those involving gasoline, propane, or TNT—pose significant risks and necessitate appropriate tunnel design and safety measures. However, submerged tunnels represent an innovative underwater transportation infrastructure that requires a distinct approach to fire safety. Unlike conventional tunnels, submerged tunnels are fully situated underwater, necessitating specialized ventilation systems, fire-resistant materials, and emergency evacuation strategies. Despite these critical requirements, research on fire safety in submerged tunnels remains insufficient.

Yoo et al. (2015) [5] assessed the fire risk of the Mokpo-Jeju subsea railway tunnel and determined the optimal spacing for evacuation passageways. To conduct the study, a quantitative risk assessment (QRA) was applied to analyze fire spread and evacuation scenarios within the tunnel. The findings indicated that a double-track tunnel with partition walls provides the safest evacuation environment.

Ryu and Choi (2018) [6] modified the existing quantitative risk assessment method for large tunnels to develop a quantitative fire risk assessment method specifically for small road tunnels designed for small vehicles. Through this study, they identified the optimal distance between cross passages and the most effective tunnel ventilation method, confirming that the proposed method can be utilized in the design and fire safety planning of small road tunnels.

Shirin (2021) [7] identified key risk factors associated with fire and explosions inside submerged tunnels and proposed design considerations for fire and explosion response. The study emphasized that tunnel ventilation systems should be designed with longitudinal ventilation to manage fire and toxic gas emissions. It was suggested that the maximum airflow velocity should not exceed 10 m/s for unidirectional tunnels and 8 m/s for bidirectional tunnels. Additionally, the study highlighted the importance of enhancing structural fire resistance through the application of Double Hull Steel Lining and the necessity of implementing safety measures, such as escape bays at regular intervals and automated detection systems.

Li et al. (2024) [8] conducted a study to evaluate the fire risk of the Shenzhen-Zhongshan underwater tunnel in China. A 1/30 scale model of the actual tunnel was constructed, and 140 small-scale fire experiments were carried out by varying fire intensity and ventilation fan operation modes to reproduce various scenarios. Based on the results, a new sidewall smoke extraction system was developed for optimal smoke control, which significantly improved smoke extraction efficiency. It is considered that if a quantitative fire risk assessment is additionally applied to this system, fire safety in underwater tunnels could be further enhanced.

Zhang et al. (2024) [9] proposed a computer vision-based real-time tunnel fire risk and evacuation safety assessment system. Using YOLOv7 and DeepSORT algorithms, vehicles were classified based on size, the number of occupants, fuel load, and heat release rate, and the classified vehicles were monitored to predict fire load in real time. In addition, a digital twin was constructed to simulate fire scenarios and evacuation processes, and real-time evacuation scenarios were evaluated based on vehicle distribution and occupant information. Such real-time simulation helps enable prompt and accurate decision-making. However, this study is limited to general road tunnels, and it is difficult to apply to submerged tunnels (both road and railway) as in this study. Therefore, there is a need for a practical program suitable for submerged tunnels that can be directly used by operators.

Despite the numerous studies that have been conducted, current studies on submerged tunnels still present several limitations, particularly concerning evacuation and smoke control technologies. Due to the fully enclosed and underwater nature of these tunnels, evacuation routes are highly constrained, and high hydrostatic pressure poses additional challenges in ventilation system design. In particular, for long or bidirectional tunnels, longitudinal ventilation alone may not be sufficient to effectively control smoke propagation during fire events. Furthermore, the structural characteristics of submerged tunnels limit the installation of intermediate emergency exits, making evacuation highly dependent on cross passages or escape bays. However, the optimal spacing and accessibility of these features remain insufficiently studied. In addition, few studies have quantitatively evaluated the performance of smoke extraction systems under diverse fire scenarios specifically tailored to underwater tunnel environments. Existing models also tend to simplify human behavior and smoke dynamics, which may lead to either underestimation or overestimation of actual risks. Future research should focus on incorporating more advanced human behavior modeling, conducting comparative analyses of different ventilation strategies, and performing large-scale or scaled-down experimental validations to enhance the reliability and effectiveness of fire safety measures in submerged tunnels.

Unlike previous studies that focused only on conceptual design, explosion analysis, or evacuation planning, this research quantitatively assessed fire and evacuation scenarios in submerged tunnels using actual data and computational programs. Furthermore, by incorporating fire safety data from existing road and railway tunnels, this study developed fire safety standards and evacuation strategies specifically tailored for submerged tunnel environments. The reliability of the proposed program was validated by comparing it with commercial software, and the findings are expected to contribute to the practical application of fire safety standards in the future design and operation of submerged tunnels.

3. Development of Quantitative Risk Assessment Program for Submerged Tunnels

3.1. Overview

The Submerged Tunnel Quantitative Risk Assessment Program (SFT_QRA) was developed to perform quantitative risk assessments for both road and railway tunnels. The program is designed to allow users to conduct quantitative risk assessments for either road or railway tunnels through selectable options within a single platform. The program was primarily developed using Excel-based VBA, ensuring easy accessibility without requiring additional software. The basic settings are configured through Excel VBA, while fire analysis utilizes precomputed numerical simulation data imported from a Fire Dynamics Simulator (FDS). A Fractional Effective Dose (FED) analysis is conducted using a custom-developed evacuation module. The quantitative risk assessment process for road and railway tunnels follows a structured sequence: hazard analysis and fire scenario development, fire analysis, evacuation analysis, risk calculation, and social risk assessment.

3.2. Main Considerations

The Submerged Tunnel Quantitative Risk Assessment Program (SFT_QRA) is designed to conduct assessments separately for road and railway tunnels. In the case of road tunnels, an evacuation analysis is performed based on the number of people in vehicles inside the tunnel, while for railway tunnels, the analysis considers passengers aboard the traveling train. During the simulation, the program calculates the Fractional Effective Dose (FED) values based on the inhalation of hazardous gases such as high-temperature air, reduced visibility, and CO concentration. Upon the completion of the simulation, the program estimates the number of fatalities based on evacuation routes—for road tunnels, it assesses evacuation passageways or the main tunnel, and for railway tunnels, it assesses the main tunnel and nearby vertical shafts. By probabilistically analyzing different cases of hazardous gas exposure, the program allows for the estimation of fatalities and provides a quantitative assessment of evacuation scenarios.

In the case of fire analysis, the program allows for the import of existing numerical simulation results from FDS (Fire Dynamics Simulator) and applies them by storing data on temperature, visibility, and CO concentration at breathing height in a database. For road tunnels, the frequency of fire scenarios is derived from statistical survey data compiled in accordance with current relevant standards. Specifically,

Table 1. Road tunnel fire incident rate.

Category	Year	Car	Bus	Truck	Total
Cases of fire incidents (cases/year)	2016	1940	386	1391	3717
	2017	1849	389	1386	3624
	2018	1787	355	1379	3521
	2019	1739	351	1392	3482
	2020	1622	320	1374	3316
	Total	7906	1520	7067	16,493
Traffic volume (106 Veh·km)	2016	194,705	18,355	59,682	272,742
	2017	194,281	21,564	61,570	277,415
	2018	205,518	20,737	60,472	286,727
	2019	212,721	19,818	62,372	294,911
	2020	224,024	19,857	63,688	307,569
	Total	1,031,249	100,331	307,784	1,439,364
Accident rate by vehicle type (cases/100 million km·Veh)	2016	1.04	1.53	2.41	1.38
	2017	1.00	1.71	2.36	1.36
	2018	0.97	1.62	2.47	1.35
	2019	0.89	1.91	2.33	1.28
	2020	0.83	1.48	2.23	1.20
	Average	0.95	1.64	2.36	1.31

presents a summary of road tunnel fire incidents that occurred over a five-year period, providing the foundational data used to estimate scenario frequencies in the risk assessment. Fire scenarios are categorized into general road tunnels and small-vehicle-only road tunnels, and corresponding branching ratios are provided as default values. Users can modify these branching ratios according to their specific requirements for greater flexibility. Similarly, for railway tunnels, the program provides predefined fire scenarios. Users can input branching ratios based on the train type, operating direction, post-fire maneuverability, and initial fire suppression status. In particular, these values are calculated using railway tunnel fire incident data, as summarized in

Table 2. Railway tunnel fire incident rate.

Category	Train Operating Distance (1,000,000 km)	Number of Fire Incidents (Cases)	Fire Incident Rate (Cases/1,000,000 Train·km)
2012	113.8	2	0.0176
2013	117.9	1	0.0085
2014	118.3	3	0.0254
2015	121.7	1	0.0082
2016	125.9	1	0.0079
2017	133.7	2	0.0150
2018	137.1	3	0.0219
2019	138.5	0	0.0000
2020	140.9	0	0.0000
2021	142.4	1	0.0070
Total	1290.2	14	0.0109

. This table compiles historical fire statistics from railway tunnels, allowing the program to generate realistic scenario frequencies that reflect actual incident trends. By integrating these data-driven calculations, the tool enhances the reliability of fire risk assessments for railway tunnel environments.

3.3. Fire Analysis Methods for Road and Railway Tunnels

Fire analysis for road tunnels is carried out based on a one-dimensional airflow prediction program called TVSDM (Tunnel Ventilation Smoke Dynamic Model), which assumes airflow in only one axis (longitudinal direction). This program supports various ventilation strategies—such as longitudinal, transverse, and large exhaust shaft systems—and predicts the longitudinal airflow velocity in the tunnel during fire scenarios. Based on the predicted airflow, the fire intensity and corresponding flow conditions for each scenario are generated. These outputs are then used as input data for the FDS (Fire Dynamics Simulator), with an Excel-based automated system facilitating the creation of FDS input files. Users can enter tunnel geometry, length, and the number of mesh grids; the program then combines this information with the predicted airflow data to automatically generate FDS executable files for simulation.

Figure 1 illustrates the parameter input window for road tunnel evacuation analysis. Users input basic tunnel specifications such as traffic direction, geometry, and traffic volume, along with fire intensity and evacuation delay parameters—including vehicle blockage time, fire detection time, and alarm broadcast delay. Once the number of vehicles inside the tunnel is entered, the total number of evacuees is estimated using average occupancy data by vehicle type. The evacuation passage spacing is divided into six equal segments to calculate evacuation distances, and a distance map is generated to support evacuation scenario modeling.

In the case of railway tunnels, the SES (Shaft Effect Simulator) program is used to predict airflow during a fire in a one-dimensional manner based on parameters such as the presence of vertical shafts, train specifications, and tunnel dimensions. Similarly to road tunnels, the FDS input files are automatically generated using the Excel-based system, and the fire analysis is conducted using the predicted airflow data.

Figure 2 shows the input interface for evacuation analysis in railway tunnels. Users input the tunnel geometry, train type, number of cars, and occupancy to calculate the total number of evacuees. Additionally, the train stopping time, door opening time, and evacuation delay time are entered to reflect evacuation delays in the simulation.

The calculation of the Fractional Effective Dose (FED) is based on equations from previous studies, incorporating thermal exposure, visibility reduction, and CO inhalation. The FED for each evacuee is computed and converted into a value ranging from 0.1 to 1.0. This value is used to estimate fatalities and quantify the fire-related evacuation risk for both road and railway tunnels using the same methodology.

4. Validation of Submerged Tunnel Quantitative Risk Assessment Program

4.1. Verification of Road Tunnel Quantitative Risk Assessment Results

To validate the Quantitative Risk Assessment Program (SFT_QRA), a comparison was made with the existing commercial program, Road QRA. The parameters used to verify the quantitative risk assessment of a general road tunnel are shown in

Table 3. Applied parameters for road tunnel quantitative risk assessment verification.

Category			Length (m)	Cross-Sectional AREA (m2)	Height (m)	Gradient (%)				Tunnel Perimeter (m)
Tunnel specifications	General		764	83.399	7.5	2.0				35.919
	Small vehicles only		1000	35.7	3.9	1.0				31.652
Category			Car	Bus		Truck				Total
				Small	Large	Small	Medium	Large	Special
Traffic volume		General	3192	6	67	1152	86	56	1	4560
		Small vehicles only	26,635	461	-	3590	-	-	-	30,686
Category			Applied content
Driving method		General	Two-way, two-lane
		Small vehicles only	One-way, two-lane
Fire protection facilities		General	No smoke control system and no evacuation passage installed
		Small vehicles only	Smoke control system and evacuation passage installed (Evacuation passage interval: 170 m)

, while

Table 4. Results of road tunnel quantitative risk assessment.

Category			SFT_QRA	Road_QRA	Remarks
Total risk	General		6.9052 × 10−4	9.4917 × 10−4	27.2% (Reduction)
	Small vehicles only		3.1059 × 10−3	3.1186 × 10−3	0.41% (Reduction)
Estimated fatalities/10,000 years	General		6.90 (Persons)	9.49 (Persons)	27.2% (Reduction)
	Small vehicles only		0.31 (Persons)	0.31 (Persons)	0.00% (Reduction)
One fatality	Frequency	General	5.9969 × 10−5	7.9959 × 10−5	25.0% (Reduction)
		Small vehicles only	1.0875 × 10−6	1.0941 × 10−6	0.60% (Reduction)
	Return period	General	16,675 (years)	12,506 (years)	25.0% (Reduction)
		Small vehicles only	919,540 (years)	913,995 (years)	0.60% (Reduction)

presents the comparative results of the quantitative risk assessment for a general road tunnel. The total risk calculated using SFT_QRA was found to be 27.2% lower than that of Road QRA. This difference is attributed to the fact that Road QRA applies the accident occurrence rates for each vehicle type as stipulated by the Ministry of Land, Infrastructure and Transport (2020) [10], which are based on accident rates recorded over five years (2017). According to the guidelines, when no specific technical data is available, referenced data can be cited and applied without additional constraints. However, this approach may lead to discrepancies between the applied statistical data and current accident statistics. As a result, the total risk is reduced, and consequently, the estimated fatalities per 10,000 years, the frequency of one fatality occurrence, and the return period all yield lower values compared to the results of the currently applied quantitative risk assessment. This indicates the necessity of revising the current guidelines and updating the relevant statistical data. In this regard, SFT_QRA, which allows for the application of the latest statistical data, is considered a more appropriate tool for quantitative risk assessment.

The total risk for small-vehicle-only road tunnels was found to be 0.41% lower in SFT_QRA compared to Road QRA. The input parameters used in SFT_QRA and Road QRA were identical, and the resulting analysis showed a 0.41% reduction, as presented in Table 4. This difference is attributed to the fact that both SFT_QRA and Road QRA utilize FDS-based fire analysis as the fundamental approach for quantitative risk assessment. Although the time-step settings in FDS were identically subdivided in both programs, the division of a single time-step is not uniform, leading to discrepancies in the exact timing of recorded results. As a result, minor deviations exist in the recorded timeframes. However, these deviations are not statistically significant, with a maximum difference of approximately 0.05 s. It is considered that this small timing discrepancy in fire duration affected the maximum fatality FED analysis. Therefore, the final analysis results are considered identical within a maximum error margin of 0.4%.

A quantitative risk assessment was conducted for general road tunnels and small vehicle-only road tunnels using the SFT_QRA developed in this study and the existing commercial Road QRA program under identical input conditions. The results confirm that, apart from discrepancies caused by differences in the application range of accident probabilities by vehicle type in fire incident statistics, there were no significant differences between the two programs for general road tunnels. However, regarding the recommendations outlined in the current relevant guidelines, an additional feature was implemented in the developed program to allow for the differentiation of application years when applying statistical data.

4.2. Verification of Railway Tunnel Quantitative Risk Assessment Results

The criteria for the evaluation and verification of general railway tunnels and rescue stations are presented in

Table 5. Applied parameters for railway tunnel quantitative risk assessment verification.

Category		Length (m)	Cross-Section Type	Cross-Sectional Area (m2)	Gradient (%)	Tunnel Perimeter (m)
Tunnel specifications	General	1500	Single track	2.66	±0.3, ±2.4	23.60
			Twin track	67.23	±0.9, ±2.2	32.91
	Rescue station	3000	Single track	2.66	±0.3, ±2.4	23.60
			Twin track	67.23	±0.9, ±2.2	32.91
Category		Number of formations	Car capacity	Occupancy rate (85%)		Occupancy rate (120%)
Traffic volume	General	6	392	333		470
	Rescue station	6	392	333		470
Category		Applied content
Service frequency	General	59 times per day
	Rescue station	59 times per day
Existence of rescue station	General	Rescue station present
	Rescue station	No rescue station

. The total risk of a general railway tunnel was found to be 3.12% lower in SFT_QRA compared to Rail QRA (

Table 6. Results of railway tunnel quantitative risk assessment.

Category				SFT_QRA	Road_QRA	Remarks
Total risk		General		2.1211 × 10−4	2.1895 × 10−4	3.12% (Reduction)
		Rescue station		1.0236 × 10−4	1.0775 × 10−4	5.0% (Reduction)
Estimated fatalities/10,000 years	General			2.12 (persons)	2.18 (persons)	2.75% (Reduction)
	Rescue station			1.02 (persons)	1.07 (persons)	4.67% (Reduction)
One fatality		Frequency	General	1.1992 × 10−6	1.2792 × 10−6	6.25% (Reduction)
			Rescue station	1.0738 × 10−6	1.1303 × 10−6	5.0% (Reduction)
		Return period	General	833,855 (years)	781,739 (years)	6.67% (Reduction)
			Rescue station	931,258 (years)	884,696 (years)	5.0% (Reduction)

). This reduction is attributed to the same reason observed in the QRA of road tunnels—despite analyzing the same fire scenario file, FDS inherently refines the time-step, introducing a discrepancy of up to 1/100th of a second. This time-step difference affects the calculation of the Fractional Effective Dose (FED) used in determining the maximum number of fatalities.

The total risk in the case of a rescue station installation was found to be 5.0% lower in SFT_QRA compared to Rail QRA. This reduction is attributed to the same reason observed in the QRA of road tunnels—despite analyzing the same fire scenario file, FDS inherently refines the time-step, introducing a discrepancy of up to 1/100th of a second. This time-step difference affects the calculation of the Fractional Effective Dose (FED) used in determining the maximum number of fatalities. Even considering this discrepancy, the maximum error rate is estimated to be within 5%, making it unlikely to introduce a significant difference in the analysis results. Therefore, the results can be regarded as equivalent, and it is reasonable to interpret them as producing the same outcome.

A quantitative risk assessment was conducted to compare SFT_QRA, which was developed to include general railway tunnels and rescue station implementation, with the existing commercial QRA program, Rail QRA, under the same conditions. The results indicate that even when accounting for errors due to time-step refinement in fire analysis, the maximum error rate remained within 5.0%, ensuring the accuracy of the analysis.

5. Conclusions

This study developed a program (SFT_QRA) capable of quantitatively assessing fire risk in submerged tunnels (both road and railway types) and verified its reliability and validity through a comparison with commercial QRA programs. The main research findings are as follows:

1

This study utilized the latest road and railway tunnel fire statistics to reflect fire occurrence probabilities and the proportion of vulnerable users, aiming to develop a realistic and practical assessment program. However, this program was developed for research purposes and is not a commercially developed product. It is currently based on Korean datasets and operates in the Korean language. Future studies are required to adapt the program for international data and to develop an English-based version for broader applicability.

2

The program was developed based on Excel VBA to ensure easy accessibility for operators and was designed to facilitate the preparation of input data. It is also structured to import analysis results from the Fire Dynamics Simulator (FDS) and store and apply data such as temperature, visibility, and CO concentration at human breathing height in a database format.

3

The program was applied to various tunnel types, including general road tunnels, small-vehicle-only road tunnels, railway tunnels, and rescue station scenarios. Its high reliability was verified through a comparison with commercial QRA programs (Road_QRA amd Rail_QRA), showing a maximum error margin within 0.4–5.0%.

4

The program developed through this study reflects the unique fire risk characteristics of submerged tunnels and is expected to have high applicability in practical settings. Furthermore, its flexibility in applying up-to-date statistical data makes it a valuable foundational tool for policy and standard improvements.

5

While the SFT_QRA program offers a practical and validated method for quantitative risk assessment, it heavily relies on numerical simulations and statistical inputs. This approach inevitably involves simplifications, particularly in modeling human behavior during evacuations, and lacks empirical field data for calibration. Future studies should consider incorporating real-world evacuation drills or sensor-based monitoring data to enhance model accuracy.