Modelling the Effect of Smoke on Evacuation Strategies in Hospital Buildings

Abstract

Designing fire evacuation strategies for hospitals involves navigating complex infrastructure and accommodating the unique needs of patients, many of whom may have limited mobility or require specialized support during emergencies. This study examines critical egress parameters and their impact on evacuation efficiency in hospital environments, emphasizing configurations that can improve safety and reduce evacuation time. Although the inclusion of smoke effects in recent evacuation models is gaining traction, their combined impact with assisted evacuation scenarios in hospital settings remains underexplored. By integrating smoke propagation data into evacuation modelling, we analyze the effects of reduced visibility and smoke spread on egress routes and occupant behavior. Findings show that smoke effects significantly increase evacuation time estimates (around 50%) compared to traditional models (without accounting for smoke effects), highlighting the risk of underestimation in practical applications, particularly in regions where strict codal compliance is lacking. The study also reveals that stairway width, number, and location substantially affect evacuation times, with about 40% reduction achieved by increasing stairway width from 44 to 66 inches. Additionally, situational awareness enhancements, such as real-time information on fire location and safe exits, can improve evacuation efficiency by about 35%. For taller hospital buildings, the findings highlight the need for implementation of situational awareness in addition to optimized egress planning to achieve safe and efficient evacuation strategies.

1. Introduction

Hospital environments present unique challenges during emergency evacuations due to the high number of vulnerable occupants, including patients with reduced mobility or medical dependencies. Fire incidents in hospitals are not uncommon and often lead to severe consequences due to the complexity of the environment. In the United States alone, between 2012 and 2014 there were approximately 5800 medical facility fires, including hospital fires per year, resulting in significant deaths (5 deaths), injuries (150 injured), and property damage ($56 million) [1]. Since 2020, the number of hospital fire incidents has doubled across the globe following the onset of the COVID-19 pandemic [2]. The post-pandemic hospital environment, enriched with oxygen, has heightened the risk of rapid fire spread and severe injuries during such events. In more recent years, hospital fires in other countries, such as the 2021 Ibn al-Khatib Hospital fire in Iraq (82 deaths) [3] and the 2023 Changfeng hospital fire (29 deaths) [4] in China, have demonstrated the critical need for effective evacuation planning due to the uncertainty of these events and their consequences. Previous research on fire hazards in buildings shows that risks are amplified in high-occupancy and functionally complex facilities, where factors such as fuel load, ventilation, occupant mobility, and response coordination strongly influence the speed and severity of fire spread [5]. These insights reinforce the need for evacuation models that capture both the complex infrastructure of hospital buildings and the diversity of occupant needs.

Traditional evacuation models are primarily based on self-evacuation in office or residential settings [6,7]. In contrast, hospital evacuations often involve assisted evacuation, where medical staff must help bedridden patients, patients using wheelchairs, or those dependent on life-supporting devices [8]. This creates bottlenecks and delays that are not typically seen in other building types. Moreover, the pre-evacuation phase, in which medical staff prepare patients for evacuation, introduces additional delays and complexity. Thus, research has been conducted to collect or build the pre-evacuation database required for tailored hospital evacuation modelling [9,10,11,12,13,14]. This factor further distinguishes hospital evacuations from other scenarios.

In recent years, tools such as a Fire Dynamics Simulator (FDS) have emerged as powerful options for modelling fire behavior in buildings and its impact on occupant evacuation [15,16,17,18]. These tools allow for the simulation of critical factors such as smoke spread, heat distribution, and the effects of ventilation systems. Smoke propagation data generated from such simulations can significantly enhance the realism of evacuation models by capturing fire-induced conditions that affect egress routes, including the spread of toxic gases and actual reduced visibility [19,20,21]. However, its application in hospital evacuations, particularly involving assisted evacuation, remains underexplored.

Existing studies on assisted evacuation in hospitals emphasize the importance of accounting for heterogeneous occupant mobility. Ronchi et al. [9] explored the calibration of pre-existing evacuation models for hospitals by introducing assisted evacuation scenarios. Their research highlighted that traditional evacuation models, which do not account for the time required to assist patients or the use of medical equipment, could significantly underestimate evacuation times. Similarly, Zou et al. (2019) [22] employed a cellular automata model to simulate the impact of wheelchair users on evacuation speed, concluding that prioritizing such patients could lead to more efficient evacuations. However, these studies often lacked detailed fire dynamics, which are critical to understanding the real-time challenges posed by fire emergencies in complex occupancies like hospitals.

The integration of smoke effects into evacuation models for hospital settings provides a more comprehensive approach to understanding these challenges. By simulating fire dynamics in real-time, studies can reveal how factors such as fire location, smoke spread, and heat transfer affect evacuation routes and times. When combined with evacuation models, it becomes possible to account for the additional challenges posed by assisted evacuation scenarios.

This study presents an integrated simulation framework that incorporates smoke propagation data and assisted evacuation modelling to assess hospital evacuation strategies under realistic fire conditions. The model accounts for factors such as reduced visibility, varied occupant mobility, occupant behavior, and key egress parameters, including stairway number, width, and location, as well as fire origin. It also examines the role of situational awareness in improving evacuation efficiency. The parametric study is then compared to the results of a 20-story hospital building without smoke propagation data, as presented by Kodur et al. [23], to evaluate the significance of incorporating smoke data in evacuation modeling. The findings contribute to the development of evacuation strategies suited to diverse healthcare settings and can support planning in low-resource environments by adapting recommendations to the prevailing building design and code compliance levels, thus enhancing preparedness and response planning across varied regulatory contexts.

2. Methodology

2.1. Description of Building

To evaluate the role of smoke during the total evacuation of a hospital building, a model structure based on a representative office building located in Denver, CO, USA [24] is considered. It was calibrated using the modeling strategy presented by Ronchi et al. [9] and subsequently modified to reflect hospital occupancy requirements. The geometric dimensions were adjusted to comply with relevant prescriptive codes, including NFPA 101 [25] and the International Building Code (IBC) [26], as well as ISO 21542:2021 [27]. The same floor plan has also been utilized in the previous studies on evacuation strategies for office and hospital buildings [6,23].

The building is a 20-story structure, with each level approximately 3.05 m (10 feet) in height. It features a rectangular floor layout covering around 2675 square meters (36.58 × 73.15 m) or 28,800 square feet (120 × 240 feet), as shown in Figure 1. At the building’s core, there are two stairwells (labelled A and B in Figure 1), six occupant elevators, and two service elevators. The building includes 16 rooms designated for wheelchair patients and another 16 rooms for bedridden patients (see Figure 1). For modelling simplicity, it is assumed that at the onset of evacuation, wheelchair and bedridden patients remain in their designated rooms, reducing variables related to patient movement before evacuation. Additionally, all floors are assumed identical, each with a capacity of 120 occupants, consistent with the occupant load standard for hospitals specified in NFPA 101 [25], which is 240 ft²/person or 22.3 m²/person. The design and layout of all egress components comply with the standards set by the IBC 2018 [26], NFPA 101 [25], and ISO 21542:2021 [27]. A 3D view of the building is presented in Figure 2.

2.2. Selection of FDS Model

Given the study’s focus on evaluating emergency scenarios in hospital settings, PyroSim version 2024.1.0702 [28] was selected for its user-friendly graphical interface to FDS model, which simplifies the setup and execution of FDS-based simulations. This feature makes PyroSim particularly valuable for analyzing complex environments in emergency scenarios. It integrates seamlessly with FDS, allowing for precise configuration of fire events and offering visualization tools that improve understanding of smoke movement, heat effects, and egress times. This setup supports critical decision-making in building design and fire safety measures for medical facilities, particularly regarding compartmentalization and safe evacuation strategies.

The base plan of the hospital building was created in AutoCAD 2023 and imported into PyroSim 2024.1.0702 to develop the model. If the grid cells are too large, they may fail to precisely capture changes in environmental parameters within the fire scene. Conversely, if the grid cells are too small, the simulation’s accuracy will increase, potentially resulting in excessively lengthy or impractical calculations. To address this issue, a realistic fire was modelled to represent the 3rd to 6th stories of the hospital building, as this was identified as the most critical egress parameter in Kodur et al. [23]. A grid size of 0.2 m × 0.2 m × 0.2 m (see Figure 3) was adopted for a more precise simulation of smoke propagation. The perspective view of the PyroSim model is shown in Figure 3.

Polyurethane foam (GM37), which is a common material in hospital bed mattresses, was used as the fuel type, with heat release rate per area (HRRPUA) of 500 kW/m², accounting for the heat release rate of polyurethane foam with other miscellaneous burning material [29]. A burning area of 1 m × 1 m was considered in the room next to stairway A, as shown in Figure 3. The input characteristics for the PyroSim model are presented in

Table 1. Input characteristics for the PyroSim model.

Input		Characteristics
HRRPUA		500 kW/m2
Material	Fuel Type	Polyurethane foam (GM37)
	Chemical Formula	C1.0H1.8O0.17N0.17
	CO Yield (YCO)	0.024
	Soot Yield (Ys)	0.113
Burner Area		1m × 1m

.

Preliminary fire simulations indicated that smoke propagation created worst evacuation scenario at the end of one hour, with no further escalation of the worst-case scenario beyond this point. Therefore, to save simulation time, a one-hour fire simulation was conducted (see Figure 4) and the FDS data were imported to pathfinder for further evaluation of the evacuation scenario. The visibility versus time graph for the corridor and stairway is presented in Figure 5. The location of measurement point is shown in Figure 5a, while the visibility versus time graphs for the corridor and stairway are presented in Figure 5b and Figure 5c, respectively. The visibility for occupants decreased rapidly for the corridor where the escape route for the smoke was minimal. On the other hand, the visibility in the stairway fluctuated with the amplitude of the graph dampening with the increase in time and approached the tenability limit of 5 m specified by NFPA 101 [25] at around 1 hr. This fluctuation in visibility can be attributed to the stack effect, as well as buoyancy-driven smoke migration, where openings on the upper floor facilitated smoke movement, allowing it to escape into other areas and causing intermittent changes in visibility [30].

2.3. Pathfinder Model

The computer software Pathfinder 2024.1.0813 [31] was used to perform evacuation simulations. Pathfinder simulates occupant movement within a building, accounting for dynamic challenges like congestion, queuing, and bottlenecks. It also allows for integration of FDS data directly from PyroSim. This makes it easier to compute the evacuation time in accordance with a real fire hazard environment. Given that this study involves a hospital with an asymmetric layout and a complex egress system, the steering model was selected to simulate the evacuation scenarios.

Pathfinder Data Inputs

The hospital building’s floor plan (refer to Figure 1) was designed using AutoCAD 2023 and then imported into Pathfinder 2024.1.0813 while maintaining the original dimensions. Pathfinder categorizes variables associated with human actions and decision-making into two main categories, namely, profiles and behaviors [9]. Effective simulation of assisted evacuation scenarios in a hospital setting depends on the precise adjustment of these variables.

 (a) 

Evacuation Profiles

Evacuation profiles define occupant speed, size, and visibility distributions during the evacuation. In a hospital context, the profile determines the unimpeded walking speed for each individual or group during evacuation, with values drawn from a pseudo-random variable based on a distribution [9]. Factors such as occupant density, space configuration, and obstacles impact walking speeds during horizontal evacuations (e.g., floor-level movement).

This study defines four profiles to represent assisted evacuations, as follows: “Assist_Bed”, “Assist_WC”, “Assistant”, and “Non-Assisted”. The “Assist_Bed” profile represents bedridden patients, “Assist_WC” represents wheelchair users, “Assistant” represents medical staff supporting patients, and “Non-Assisted” represents visitors, doctors, and other staff capable of self-evacuation. Travel speeds for each profile are based on the available literature [11,32]. Furthermore, the speed of each profile is affected by smoke due to the visibility constraint which is governed by an equation presented in Fridolf et al’s work [33].

Each hospital floor has 120 occupants, categorized as follows: 16 “Assist_Bed”, 16 “Assist_WC”, 48 “Assistant”, and 40 “Non-Assisted”. The study assumes that one assistant helps each wheelchair patient, while two assistants help each bedridden patient during vertical egress. The speed of the person being assisted controls the movement speed. The stair ascent and descent speed are determined according to the k-values defined in the SFPE Handbook [31], which depends on the steep slope of the stairway. It is assumed that the staff assigned to assist the bedridden and wheelchair patients are solely responsible for their descent. The validation for vertical egress for assisted evacuation is presented in the Pathfinder verification and validation document [34]. Occupant profile details are shown in

Table 2. Details of occupant profile on each floor.

Profile Name	No. of Occupant Per Floor	Profile Detail	Evacuation Mode	Travel Speed (m/s)	Source for Travel Speed
Assisted_Bed	16	Bedridden Patient	Assisted by staff	0.52 (1.7 ft/s)	Rahouti et al. [11]
Assisted_WC	16	Wheelchair Patient	Assisted by staff	0.52 (1.7 ft/s)	Rahouti et al. [11]
Assistant	48	Nurses and other medical staff with supporting roles	Assisting the Patient	1.19 (3.92 ft/s)	SPFE handbook for density less than 0.05 person/ft2 [32]
Non-Assisted	40	Healthy Patient, Visitors and doctors with no supporting roles	Self-Evacuation	1.19 (3.92 ft/s)	SPFE handbook for density less than 0.05 person/ft2 [32]

, and specifications for hospital beds and wheelchairs are listed in

Table 3. Details of patient assistance devices in the hospital.

Assistance Device	Size (m)	Assistant Required Per Device
Hospital bed	0.76 × 2.15 × 1 (2.5 × 7.05 × 3.28) ft	2
Wheelchair	1 × 1.32 × 1 (3.28 × 4.33 × 3.28) ft	1

.

 (b) 

Behavioral Roles

Behavioral roles define the function of each profile, determining whether an occupant will seek or provide assistance, and which exits to use. Since pre-evacuation time is crucial for assisted evacuations, specific wait times are applied to each profile.

In this study, the following three behavior categories were developed: “Assisting”, “Assisted”, and “go to any exit”. “Assisting” involves helping occupants, “Assisted” requires occupants to wait for help, and “go to any exit” allows occupants to choose any available exit. To account for preparation time for bedridden and wheelchair patients, wait times of 90 s for “Assisting” and 60 s for “Assisted” behaviors were assigned based on previous studies [10,11,12,13,32].

 (c) 

Evacuation Simulation Settings

It is assumed that occupants who do not need assistance or are not assisting others will begin self-evacuation simultaneously, using only stairways (no elevators). Figure 6 illustrates the dimensions of components used in evacuation time calculations, following IBC 2018 [26] and NFPA 101 [25].

The simulation includes specific steering parameters, with a steering update interval and a minimum flow rate factor set to 0.1 s and 0.1, respectively. The steering update interval dictates how often the steering calculations refresh, influencing simulation speed [6]; a larger value speeds up simulation but may reduce accuracy in occupant decisions. The minimum flow rate factor affects door queue behavior, ensuring continuous flow at doors and discouraging occupants from changing doors in low-flow situations [6].

2.4. Model Validation

To ensure the accuracy of Pathfinder’s predictions, extensive verification and validation were conducted. Verification tests assessed the implementation of evacuation modes and occupant behaviors, including flow rates for egress components and tests on grouping, merging, collision handling, and speed. Validation tests, on the other hand, compared simulations against experimental data from various published studies, covering factors like one-way and two-way corridor flows and turning and merging in T-junctions. For more detailed information on these tests and their results, refer to the Pathfinder verification and validation document [34].

Furthermore, to assess the predictive accuracy of the proposed model in estimating total evacuation time in a hospital setting, we validated it using real-world evacuation data obtained from the literature [35]. The simulation-based validation approach is also consistent with ISO 16738:2015 [36], which outlines recommended procedures for verifying and validating fire safety engineering calculation methods.

2.4.1. Post-Anesthesia Care Unit

Lovreglio et al. [35] investigated the pre-evacuation time and movement phase during an announced fire evacuation drill across different hospital units in New Zealand. Eight evacuation drills were conducted across various hospital units, and the corresponding data, such as pre-evacuation time, travel speed, and total evacuation time, were extracted.

Our study focuses on one of the hospital units, specifically the Post-Anesthesia Care Unit (PACU), presented in the paper, for the validation of the Pathfinder model. The layout of the PACU, along with the corresponding Pathfinder model, is presented in Figure 7.

A total of eight bedridden patients and four wheelchair users were present in the unit, along with eight staff members. The blue arrow indicates the direction of movement for wheelchair patients assisted by staff, while bedridden patients were evacuated through both exits marked in red during the evacuation drill. The evacuation drill was considered complete when all individuals had crossed the red markers at the two doorways. The evacuation profiles and behavioral parameters mentioned in previous sections were used as inputs for the Pathfinder model.

The total evacuation time for the Pathfinder model was 228 s, compared to 270 s in the actual evacuation drill. The 15.56% variation between the Pathfinder model and the actual evacuation drill is attributed to real-world factors such as pre-evacuation delays, staff coordination challenges, movement speed variability, congestion, and human decision-making, which are often simplified in simulations, and thus reasonable.

2.4.2. General Ward

To further assess the predictive accuracy of the Pathfinder model, validation was extended by using data from the evacuation of a General Ward Unit (GW) for a hospital in New Zealand, as presented in Lovreglio et al. [35]. The layout of the GW, along with the corresponding Pathfinder model, is presented in Figure 8.

The General Ward Unit consisted of eight hospital beds occupied by bedridden patients, three ambulant patients capable of evacuating independently, and thirteen staff members responsible for assisting with the evacuation process. During the drill, ambulant patients exited the unit without assistance, while bedridden patients were evacuated under the supervision of staff members. The evacuation was considered complete once all individuals had successfully exited the designated evacuation point marked by red line (see Figure 8). The Pathfinder simulation was configured using the same evacuation profiles and behavioral parameters as outlined in previous sections.

The total evacuation time predicted by the Pathfinder model was 324 s, whereas the actual evacuation drill recorded a time of 331 s, resulting in a 2.11% variation between the simulated and real-world evacuation times. This minor discrepancy is attributed to real-world factors such as slight variations in pre-evacuation times, as human decision-making processes introduce uncertainties that are often simplified in simulations. Additionally, variations in movement coordination, particularly among staff members assisting bedridden patients, contribute to minor deviations in evacuation speed. Other complexities, such as momentary hesitations and unaccounted interpersonal interactions during the drill, may have further influenced the observed evacuation time.

Despite these real-world influences, the small variation of 2.11% indicates a high level of agreement between the Pathfinder model and the observed evacuation drill. This result supports the model’s applicability for simulating hospital evacuations, particularly in general ward environments where a mix of ambulant and non-ambulant patients necessitates complex evacuation procedures.

2.5. Need of Smoke Propagation Data in Evacuation Model

The integration of smoke propagation data into evacuation models has proven to be a valuable advancement for accurate life safety assessments in fire scenarios. Research has demonstrated that coupling FDS with evacuation models significantly enhances the reliability of evacuation simulations by capturing the effects of fire dynamics on human behavior [15,16,17].

To gauge the relevance of smoke propagation data in conventional hospital evacuation models, a comparative study is conducted, utilizing the result of the evacuation model from Kodur et al. [23], which has the same hospital building plan integrated with an addition of FDS data from PyroSim. The three-staircase scenario is compared, wherein the position of the respective stairway has been presented in Figure 6. It is observed that there is a significant increase in evacuation time when FDS data are integrated into the evacuation model. The result of the comparative study is presented in

Table 4. Evacuation time with and without smoke data in a hospital building.

Varied Parameter (No. of Stairway)	Evacuation Time (min) with Smoke Data	Evacuation Time (min) (Without Smoke Data)
A-B-C	256	103
A-B-D	169	77
A-B-E	166	70

.

It is evident that relying on egress parameters for emergency evacuation in a hospital building without incorporating smoke propagation data can lead to a significant underestimation of evacuation time and should not serve as the foundation for an emergency evacuation framework. Therefore, a comprehensive study of the critical egress parameters influencing hospital evacuation, including smoke propagation data, is conducted.

2.6. Effect of Change in Location of Source of Fire in a Story

Fire occurrence is arbitrary in nature and could result from numerous different causes. However, the effect of fire on evacuation time would be more severe if it directly impedes the egress path of the occupants during the evacuation. To assess whether the change in location of a source of fire would have any effect on the total evacuation time of a hospital building, three different cases are modelled and observed. Case I represents the fire occurring near Staircase A and Staircase C. Case II represents fire occurring near Staircase B, while Case III represents fire occurring near Staircase E. The building plan showing the location of the three cases is presented in Figure 9. All three cases are modelled to have the source of fire near the staircase to simulate the worst-case scenario. The Simulations are performed in PyroSim 2024.1.0702, changing the fire position, and the subsequent fire data are used in Pathfinder 2024.1.0813 to measure the total evacuation time.

The result from the simulation is presented in

Table 5. Evacuation time change in source of fire.

Case	Position of Fire	Evacuation Time (min)
		A-B-C	A-B-D	A-B-E
I	Near A	256	169	166
II	Near B	248	203	159
III	Near E	260	176	157

. To assess whether the change in source location has any significant impact on the total evacuation time, the Analysis of Variance (ANOVA) method is used to statistically compare the data. ANOVA shows an F-statistic of 0.014 and a p-value of 0.986, which indicates that there is no statistically significant difference in evacuation times across the three fire positions. The Box plot showing the distribution of evacuation times for each fire position has been presented in Figure 10. Thus, further analysis of egress parameters governing assisted evacuation in hospital building is conducted by considering only Case I.

3. Effect of Smoke and Fire Spread on Evacuation Times

3.1. Parametric Study Details

A comprehensive parametric study is conducted to assess the impact of critical factors on evacuation efficiency, utilizing Pathfinder for egress simulation and PyroSim for detailed fire dynamics modelling. This study investigates how configurational, environmental, procedural, and behavioral elements affect the total evacuation time in a hospital setting, focusing on a range of scenarios to capture realistic conditions. In the evacuation analysis, variables such as the location, number, and width of stairways are modified to understand their influence on evacuation speed and congestion during fire emergencies with smoke spread. Additionally, the study examines the role of assisted evacuation by varying the degree of assistance provided to occupants with mobility challenges, highlighting how different levels of support affect overall evacuation time. Furthermore, the effect of the location of fire on the total evacuation time is analyzed.

3.1.1. Number of Stories

The evacuation process in hospital buildings is uniquely impacted by building height due to the specific needs of patients, many of whom require assistance to evacuate safely. Although hospitals are typically shorter than residential or office buildings, the number of stories can significantly influence evacuation time. The high proportion of patients with temporary or long-term disabilities means that assisted evacuation becomes necessary, especially in vertical movement situations. Moving patients down multiple stories can be challenging and often requires specialized equipment, slowing the overall evacuation flow. As the building height increases, so does the physical strain on both patients and staff, contributing to slower movement, increased congestion, and extended evacuation time. The presence of smoke further complicates these challenges in taller hospital buildings. As smoke moves vertically, visibility is reduced, and the need for staff to navigate stairwells with limited sight lines and potential smoke exposure makes assisted evacuation even more demanding. The risk of disorientation and reduced speed in smoky conditions can lead to increased congestion and delays. In this study, four hospital building heights, namely, 5, 10, 20, and 30 stories, are assessed, each featuring a three-staircase layout (stairways A, B, and C, as shown in Figure 6), to understand the evacuation challenges associated with different building heights.

It is observed that the evacuation time for hospital buildings above 10 stories is significantly higher than the available safe evacuation time prescribed for such structures (see Figure 11). For a 30-story hospital building, the total evacuation time reaches 358 min, which is nearly three and a half times that of a 10-story building, requiring 97 min, while a 20-story building has an evacuation time of 256 min. In contrast, a five-story hospital building has a total evacuation time of 23 min. The result also shows a notable increase in evacuation times when FDS data are considered. For the same building, the total evacuation times without FDS data (no consideration to smoke effects), as reported by Kodur et al. [23], were 128, 103, and 28 min for 30-, 20-, and 10-story hospital buildings, respectively. Therefore, in addition to implementing essential measures, such as widening egress paths, as recommended by IBC 2018 [26] and NFPA 101 [25], incorporating various situational awareness strategies is crucial to ensure safe and efficient evacuation for hospital buildings with more than 10 stories.

3.1.2. Location of Stairways

The placement of stairways is a key factor influencing egress efficiency in hospital buildings, especially during assisted evacuations. Hospital environments accommodate a wide range of occupants with varying mobility needs, including the use of wheelchairs, walkers, and other mobility aids, as well as differing travel speeds. To improve evacuation efficiency, it is crucial to balance travel distances for each occupant. In line with this, building codes provide detailed guidelines on exit path locations to enhance evacuation effectiveness. For example, IBC 2018 [26] mandates a maximum horizontal travel distance of 125 feet to an exit, while NFPA 101 [25] allows up to 150 feet. Both codes also specify minimum separation requirements between staircases, recommending half of the maximum diagonal dimension for general buildings and one-third of this dimension for buildings equipped with automatic sprinkler systems. These standards ensure that multiple egress routes are available in case primary exits become blocked by fire or smoke.

In this study, three configurations of stairway locations are analyzed using Pathfinder for evacuation modelling and PyroSim for FDS modelling. Each staircase is marked alphabetically, A through E, as shown in Figure 6. Staircases A to D are positioned within the building core, while staircase E is located outside the core. The first two configurations include all three staircases within the core, with staircases A, B, and C in Case 1, and staircases A, B, and D in Case 2. In the third configuration, two stairways are within the core, and one is positioned outside the core (A, B, E).

The results indicate that the stairway arrangement in Case 3 (stairways A, B, and E, with two stairways within the core and one outside) has the shortest total evacuation time of 166 min. In comparison, the evacuation time increases slightly to 169 min for the Case 2 arrangement (stairways A, B, and D), which is similar to the time for Case 3. However, for the Case 1 arrangement (stairways A, B, and C), the evacuation time significantly increases to 256 min, representing a 54% increase over the Case 3 arrangement (see Figure 12). The extended evacuation time for the A, B, and C arrangement in Case 1 is primarily due to increased congestion and bottlenecks, as stairways A and C are located close to each other, separated only by a wall (refer to Figure 6). Although stairways located within the core are more accessible under normal circumstances, it is observed that during emergency fire evacuation with smoke, managing slow-moving traffic is easier when one of the stairways is located outside the core of the building.

3.1.3. Number of Stairways

IBC 2018 [26] and NFPA 101 [25] set minimum requirements for the number of egress stairways in hospital buildings based on floor-by-floor occupant loads. Specifically, two stairways are needed when a floor has fewer than 500 occupants, three are required for loads between 500 and 1000, and four stairways are necessary for floors with more than 1000 occupants. These guidelines apply individually to each floor without considering cumulative load across multiple levels. Increasing the number of stairways helps to distribute occupants more evenly, reducing bottlenecks and minimizing evacuation delays. However, stairways occupy valuable floor space, so the number and placement of stairways must be carefully planned for practicality and efficient long-term use.

This study evaluates three stairway configurations. The first uses two stairways (A and B), the second includes three stairways (A, B, and E), and the third configuration combines two stairways (A and B) on the top 10 stories with three stairways (A, B, and E) below the 10th floor. It is assumed that occupants are trained to utilize the additional stairway on lower floors in emergencies.

As shown in Figure 13, the simulation results indicate that the second configuration, with three stairways, achieves the fastest evacuation time of 166 minutes. In contrast, the first configuration, with two stairways, results in a longer evacuation time of 244 minutes, representing a 47% increase. Although current codes do not factor in cumulative occupant loads when specifying the number of stairways, this cumulative load significantly impacts evacuation efficiency. Merging occupants from upper floors can increase congestion on lower levels, extending total evacuation time. Adding extra stairways on lower floors can mitigate this issue, as demonstrated in the third configuration. Here, evacuation time is reduced to 176 minutes, 28% less than in the first configuration and approaching the time of the second configuration. These results are consistent with findings by Kodur et al. [23], who analyzed the 20-story hospital building without considering the smoke effects. However, the evacuation time is considerably higher for hospital buildings with smoke effects accounted for. This necessitates a combined approach, where strategies balancing the number of stairways should be combined with extra information provided to occupants to use the resources optimally.

3.1.4. Stairway Width

The width of a stairway plays a significant role in managing the flow of occupants during an evacuation. In hospital settings, where assisted evacuations are common and involve a substantial number of wheelchairs and beds, determining an appropriate stairway width is essential for efficient evacuation. NFPA 101 [25] specifies that the minimum width for staircases in high-rise hospitals should be 44 inches. This requirement is based on cumulative occupant load. For buildings with more than 2000 occupants, the minimum width recommended increases to 56 inches. Increasing the width of stairways generally improves traffic flow and reduces evacuation time. However, expanding the egress width occupies substantial floor area, which could impact the building’s economic feasibility.

In this study, three stairway widths were evaluated for assisted evacuation in a hospital, as follows: 44 inches, 56 inches (about a 25% increase over the minimum width as prescribed by NFPA 101 [25]), and 66 inches (representing a 50% increase over the minimum width). The dimensions of assistance devices used in the Pathfinder model are shown in Table 3. Additionally, normal occupants are represented as cylinders in Pathfinder, with dimensions of 0.46 m (1.5 feet) in width and 1.8 m (5.9 feet) in height.

The analysis (Figure 14) shows that increasing the stairway width from 44 inches to 56 inches and then to 66 inches reduces the total evacuation time in a notable manner, with times recorded at 256 min, 214 min, and 155 min, respectively. This represents a reduction in evacuation time of approximately 16.4% when increasing the width from 44 inches to 56 inches, and a 39.5% reduction when increasing from 44 inches to 66 inches. However, increasing stairway width alone may not be an effective solution for reducing evacuation time. Additionally, relying solely on stairway width to decrease evacuation time could be risky, as fire-induced blockages could render the stairway unusable, leading to severe consequences.

3.1.5. Mobility and Speed of Occupants

Variations in occupant speed significantly impact traffic flow during an evacuation. The proportion of low-speed occupants is particularly relevant in hospital settings, which typically house a diverse group of individuals, including a large percentage with reduced mobility. This becomes even more critical when considering assisted evacuation in hospitals, where many occupants require help to exit. Current prescriptive codes [25,26] do not account for variations in occupant speed when specifying egress dimensions for emergency evacuations.

This study investigates three scenarios to evaluate the impact of low-mobility occupants on evacuation time. The first scenario represents a self-evacuation case, modeling the hospital as an outpatient ward without assistive devices or low-speed individuals. In the second scenario, one-third of the occupants on each floor are low-mobility individuals, including medical staff aiding wheelchair users and bedridden patients. The third scenario increases this proportion, with two-thirds of the occupants per floor categorized as low-speed evacuees.

As shown in Figure 15, the total evacuation time increases from 27 min in the base scenario to 145 min when one-third of occupants evacuate at a low speed. When this proportion increases to two-thirds, evacuation time rises to 244 min. Smoke compounds these challenges by reducing visibility, which further slows evacuation and increases the likelihood of congestion. Low-speed occupants, especially those needing assistance, obstruct the path of faster-moving evacuees, who are unable to bypass them due to limited egress width, higher occupant density, and smoke conditions. The space occupied by assisting devices, such as wheelchairs and beds, restricts egress width even more, worsening the delays. This highlights the adverse effect of low-speed occupants on overall evacuation flow. To address these issues, strategies like phased evacuation, enhanced visibility measures, and dedicated exits for low-speed occupants should be implemented to manage and improve flow rates effectively in the presence of smoke.

3.1.6. Location of Fire

In hospital buildings, evacuation during a fire is a complex process which is influenced by factors such as occupant density, stairway configuration, and the spread of smoke. Smoke plays a critical role in evacuation scenarios, as it can quickly obscure visibility, restrict access to stairways, and intensify the urgency for safe egress. The challenges are heightened in hospitals due to the diverse mobility needs of occupants, many of whom require assistance to evacuate safely. Understanding how fire and smoke originating from different levels impact evacuation times is essential for designing effective emergency response strategies that ensure the safety of all occupants. This parametric study examined the impact of fires at various building levels, focusing on how smoke obstructing key stairways affects overall evacuation efficiency in a hospital setting.

In this study, the impact of fire originating from three different locations within the building is analyzed, with a focus on how smoke affects evacuation times. In the first scenario, the fire starts on the 3rd floor, and smoke and gases from the fire obstruct stairway A between the 3rd and 6th floors (see Figure 6 for stairway A’s location). This setup simulates a fire on a lower floor, where smoke rises quickly, blocking key evacuation routes and complicating movement. In the second scenario, the fire begins on the 9th floor, blocking stairway A from the 9th to the 12th floor, representing a mid-level fire. The third scenario involves a fire on the 15th floor, resulting in smoke obstructing stairway A from the 15th to the 18th floors, simulating a fire towards the upper part of the building. The simulation assumes only two stairways (A and B) for evacuation purposes.

Evacuation times, as shown in Figure 16, reveal that evacuation becomes most critical when smoke and fire are located on the lower floors. When smoke from a fire on the 3rd to 6th floors obstructs stairways, the total evacuation time reaches 244 min, which is 84% and 26% higher than for fires starting on the top and mid-level floors, respectively, with evacuation times of 132 min for the upper floors and 193 min for the mid-level floors. Smoke not only reduces visibility, but also limits access to stairways, leading to increased congestion and slower movement, especially in lower-floor fires where smoke accumulates and spreads through primary exit routes. These findings are consistent with those of Kodur et al. [23], who concluded that fires originating on lower levels pose the most significant challenges for evacuation planning; however, the integration of smoke effects with fire in the present study demonstrates that the fire scenario at the lower story is far worse for evacuation. Thus, stricter firefighting, as well as prevention regulations, needs to be implemented in the lower floors of the high-rise hospital buildings.

3.1.7. Role of Situational Awareness

As can be seen from the parametric study, the total evacuation time for the hospital building is considerably longer, even when using the best among the various egress parameters or combining the egress strategies; this can have catastrophic effect on occupants and emergency responders during the real fire hazard. In that regard, situational awareness plays a critical role in fire evacuation, as it enables occupants to make informed, timely decisions that can significantly impact their safety and evacuation efficiency. During a fire, conditions can rapidly deteriorate, with smoke reducing visibility, increasing disorientation, and obstructing escape routes. Situational awareness involves being aware of the fire’s location, understanding the fastest and safest routes to exit, and knowing the location of potential hazards. For individuals and staff in complex environments, such as hospitals, maintaining situational awareness becomes even more crucial. Staff trained in situational awareness can guide patients and visitors efficiently, avoiding congested or dangerous areas. Real-time information systems, such as emergency alerts or digital signage, can enhance situational awareness by providing up-to-date information about accessible exits, blocked stairways, and the safest evacuation paths. Clear signage and pre-existing knowledge could lead to quicker evacuations, with up to about 35% increase in speed in some cases [37]. By fostering situational awareness among occupants and using technology to communicate vital information, the likelihood of a safe and orderly evacuation can be significantly increased, even in rapidly changing fire conditions.

Various situational awareness methods have been researched to improve fire evacuation safety by enabling occupants to make informed, real-time decisions in rapidly changing conditions. Emergency wayfinding systems, such as dynamic exit signs and digital signage, guide people to the safest exits based on current conditions like smoke spread and congestion [38]. Indoor positioning systems (IPS), using technologies like Wi-Fi or RFID, can track occupants’ locations, allowing customized evacuation routes, which are especially useful in complex environments like hospitals [39]. Augmented reality (AR) technology, accessible through smartphones or AR glasses, can visually guide occupants with real-time evacuation routes and obstacle identification, enhancing awareness even in low visibility [40]. Mobile evacuation apps deliver real-time updates on fire locations and available exits, often using push notifications to alert users about specific risks nearby [33]. Voice evacuation systems offer personalized guidance, directing occupants to safe exits away from hazards [41]. Similarly, crowdsourcing and social sensing collect data from occupants through mobile apps or wearables, helping to identify crowded areas and optimize evacuation routes [42]. Together, these situational awareness methods create a dynamic, responsive evacuation environment that adapts to fire conditions, ultimately supporting safer and more efficient evacuations.

Since the state of the art is still underprepared for implementing these situation awareness approaches in real fire scenarios, we lack experimental data to exactly quantify the rate at which the total evacuation time can be reduced. Thus, a parametric approach is used in this study, wherein the application of the above-mentioned situational awareness results in changes in the occupant’s walking speed from 10 to 35%. A simplified assumption of increased occupant speed is considered, based on the premise that pre-existing knowledge of the real-time scenario would lead to less hesitation and a greater probability of avoiding crowding and bottlenecks. This adaptive change in walking speed is used in integration with the speed changes in fire evacuation due to smoke, which was presented by Fridolf et al. [33]. They conducted research on the impact of smoke on evacuation speeds, observing that as smoke density increases, walking speeds significantly decrease. Based on Fridolf et al. [33], where w is walking speed,

w = min (w_smoke-free, max (0.2, w_smoke-free − 0.34 × (3−v)))

(1)

where

w represents the walking speed in smoke conditions,

w_smoke-free is the walking speed in smoke-free conditions,

v represents the visibility level in meters.

Equation (1) represents the walking speed of occupants with respect to change in visibility due to smoke during the fire evacuation. This equation was modified to account for situational awareness, with a modifying factor ranging from 10 to 35%. The modified walking speed was used in the Pathfinder to see the changes in the total evacuation time. Two fire scenarios with two and three stairway arrangements were evaluated. The results obtained have been presented in

Table 6. Evacuation time for different modification factors considering the situational awareness.

Varied Parameter	Cases	Staircase Used	Evacuation Time (min)	Modification Factor for Walking Speed
Situational Awareness	2 Staircase	AB	205	10%
	2 Staircase	AB	154	20%
	2 Staircase	AB	159	30%
	2 Staircase	AB	142	35%
	3 Staircase	ABE	131	10%
	3 Staircase	ABE	99	20%
	3 Staircase	ABE	95	30%
	3 Staircase	ABE	105	35%

.

It was observed that there was significant reduction in the total evacuation time, with change in the walking speed of a heterogenous population. With a 10% modification factor, which slightly reduces walking speed, evacuation times are relatively high, reaching 205 min for the AB staircase configuration and 131 min for the ABE configuration. As the modification factor increases to 20%, walking speeds are further adjusted, leading to a noticeable reduction in evacuation times, to 154 min for AB and 99 min for ABE. When the modification factor is increased to 30%, evacuation times adjust accordingly to 159 min for AB and 95 min for ABE. At a 35% modification factor, evacuation times settle at 142 min for AB and 105 min for ABE (see Figure 17).

However, the results do not completely follow the increasing trend as the modification factor. This might be due to the abrupt congestion and the bottlenecks that are inherent to the evacuation modelling when the behaviors do not directly imply the situational awareness. Overall, these results suggest that increasing situational awareness among occupants can substantially reduce evacuation time, as individuals who are informed about the fire’s location and safe exits can navigate more effectively, leading to a more efficient evacuation process across both configurations. Considering the 35% modification factor, the change in total evacuation time has been plotted in Figure 17.

Table 7. Evacuation time for different egress parameters.

Varied Parameter	Cases	Staircase Used	Near A	Near B	Near E	Evacuation Time (min) Kodur et al. [23] (Without Smoke Effects)	Remarks
			Evacuation Time (min)
No. of Stories	5 stories	ABC	23	32	24	-	-
	10 stories	ABC	97	100	100	49	-
	20 stories	ABC	256	248	260	103	-
	30 stories	ABC	358	380	408	128	-
Location of Stairway	3 stairways within core	ABC	256	248	260	103	-
	3 stairways within core	ABD	169	203	176	77	-
	2 stairways in core and 1 outside core	ABE	166	159	157	70	-
No of Stairways	2 stairways	AB	244	250	239	98	-
	3 stairways	ABE	166	159	157	70	-
	2 in top 10 and 3 in bottom	ABE	176	204	173	77	-
Staircase width	1.117 m (44 in.)	ABC	256	248	260	103	-
	1.422 m (56 in.)	ABC	214	220	217	94	-
	1.676 m (66 in.)	ABC	155	158	156	83	-
No. of low-speed occupant	None	AB	27	45	28	28	-
	One-third of the total occupants per floor	AB	145	167	169	79	-
	Two-third of the total occupants per floor	AB	244	250	239	98	-
Location of Fire	3rd to 6th story	AB	244	250	239	170	-
	9th to 12th story	AB	193	192	191	148	-
	15th to 18th story	AB	132	137	133	125	-
Situational Awareness	2 Staircase	AB	205	213	220	-	10% *
	2 Staircase	AB	154	166	167	-	20% *
	2 Staircase	AB	159	164	166	-	30% *
	2 Staircase	AB	142	164	167	-	35% *
	3 Staircase	ABE	131	142	142	-	10% *
	3 Staircase	ABE	99	116	114	-	20% *
	3 Staircase	ABE	95	117	102	-	30% *
	3 Staircase	ABE	105	118	118	-	35% *

* Modification Factor.

summarizes the total evacuation time for different egress parameters.

4. Limitations and Future Research

This study focused primarily on fire evacuation in smoke-obstructed conditions. As a result, certain factors, such as the health impact on occupants due to elevated concentrations of carbon monoxide, carbon dioxide, and other hazardous gases, were not specifically considered, even though these factors can influence evacuation effectiveness. Additionally, the simulation does not account for fire spread or the corresponding temperature changes, which could affect overall evacuation times. For feasibility, the fire is assumed to be confined to only three floors, rather than extending throughout the building’s height. Furthermore, the situational awareness was incorporated through a modification factor applied to walking speeds rather than by implementing a fully dynamic awareness model. Although the findings from this evacuation framework can generally be applied to assisted evacuation scenarios in accordance with local and international codes, special consideration is required for hospitals that are poorly designed and do not comply with essential codal requirements, ensuring that evacuation strategies are adapted to these constraints.

Future research could expand on this study by including more realistic parameters, such as the impact of toxic gases on occupants’ health and mobility, as well as the effect of rising temperatures on evacuation behavior. Moreover, a more comprehensive approach to modelling situational awareness could be developed, incorporating real-time decision-making and adaptive responses based on evolving fire conditions. Future studies could also explore more dynamic simulations of fire spread across multiple floors, as well as strategies to improve the efficiency and accuracy of evacuation modelling in complex fire scenarios.

5. Conclusions

Based on the results, the following conclusions can be drawn:

• Incorporating smoke propagation and visibility loss into hospital evacuation models yields more realistic predictions and avoids significant underestimation of evacuation times that can occur when these effects are omitted.
• The effectiveness of fire evacuation in hospital settings depends strongly on egress parameters, particularly the number, width, and placement of stairways, as well as the location of the fire. Strategic design, including at least one stairway outside the core and hybrid layouts with additional stairways on lower floors, can substantially reduce congestion and improve flow during lower story fire scenarios, where evacuation is more challenging and risk levels are significantly higher.
• Stairway capacity and occupant mobility should be addressed in combination. Increasing stairway width substantially improves evacuation efficiency, particularly in hospitals with a high proportion of low-speed evacuees.
• Optimized egress parameters alone are sometimes insufficient to ensure a safe evacuation framework. The incorporation of situational awareness measures such as alerts, dynamic signage, or mobile applications can further reduce evacuation times by enabling informed decision-making, minimizing disorientation, and improving occupant flow. Thus, more research should be conducted to explore this area.
• In regions with limited code enforcement, as can be prevalent in some developing countries, the proposed framework integrating smoke propagation data with assisted evacuation modelling provides a practical basis for scenario-based planning. Recommendations on stairway width, number, and placement can be adapted to local construction practices to improve safety.