Fire safety status and evacuation of medical facility considering elevated oxygen concentrations

doi:10.21203/rs.3.rs-2103246/v1

Download PDF

Research Article

Fire safety status and evacuation of medical facility considering elevated oxygen concentrations

https://doi.org/10.21203/rs.3.rs-2103246/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

The prevalence of infectious diseases and rapid population expansion has increased the number of medical facilities. Due to the patients' limited mobility, these hospitals are more susceptible to fire disasters. Both Pathfinder and Fire Dynamic simulator was used to calculate the required safe evacuation time (RSET), heat release rate, visibility, temperature, CO, and oxygen effects on temperature, and available safe evacuation time (ASET). The safe evacuation of the medical hospital was then evaluated by comparing the ASET and RSET. The simulation findings showed that the egress guides depend on delay time when delay time increases the egress guides dropped. We also studied the importance of egress route decisions and suggest that at least 20 to 30 egress guides are on duty in the medical facility. The safety criteria for the medical facility have been proposed based on the delay time with the normalized egress guides. The high oxygen concentration in a hospital can put the medical staff and patients in danger and limit the required safe egress time to less than 150s. The proposed measures can be used to assess the evacuation safety of a typical medical hospital in use relatively quickly and efficiently.

Hospital

evacuation

Pathfinder

Fire Dynamic simulator

Egress delay time (RSET)

In recent years, evacuation has become a prominent topic. Based on pedestrian age, personality, gender, health status, and many other factors pedestrian behaviors exhibit significant variation. The vast throng will interact strongly on a physical and psychological level. Studies on pedestrian mobility and evacuation are primarily focused on public settings, including schools[1], shopping malls[2], [3], and subway stations[4]. There has only been little research on hospital evacuations [5], [6], as well as wheelchair evacuations [7], [8], and [9].

To minimize the egress time for the elderly medical facilities under various conditions fire doors open/closed, sprinkler systems closed/open using the Fire Dynamic simulator, and Pathfinder simulations [18].To quantitatively evaluate ASET and RSET, Rie et al. used artistic simulation to provide an information transfer function between agents that permits bypass of evacuation by danger as well as the individual setting of the evacuation route and start time for each evacuee[12].In Hunt et al., the building Exodus model has been used and represents the modeling theory behind and Algorithm is presented for both vertical and horizontal evacuation[13]. According to Mufeng Xiao et al. utilizing Pathfinder to apply the physical features of library patrons and the movement speed that reflects these traits, the evacuation time might be decreased[14]. Hung et al [15].'s investigation of potential fire safety risks and recommendations for small old people rehabilitation facilities for improving fire safety were provided. Many other related types of research are being done using underground complexes for a fire emergency. One of the same research was done by Li et al[16].

Fire safety guides do not recommend using an elevator during a fire emergency evacuation but Boonngam and Patvichaichod examined pedestrian fire egress scenarios of a multi-story building using a pathfinder. They found that the evacuation efficiency increased by 5.84% when occupants used the elevator as an evacuation route during a fire scenario [17]. Ronchi et al.[18], Wal et al.[19],and Fu et al[20] conducted a pedestrian behavioral study on fire during a fire emergency. Abdelghany et al.[21], studied ways to improve pedestrian egress models. Cho et al. [22], and Xie et al. [23] have done similar studies to improve egress models by taking way-finding instructions into account. Numerous kinds of research have been done on pedestrian egress simulation to gauge fire safety [24]. Annunziata al. conducted fire exercises and then carried out egress simulations based on the results he claimed that in case of a fire, the smolder lowers the occupant's extreme speed and emergency lighting in the ward doesn’t provide adequate visibility when there is smoke [25]. Tsungjung Cheng discussed the evacuation route design in a hospital building in Taiwan[26]. But even in many papers, investigations, and findings, hospitals lack proper planning and lack standardized evacuation procedures. The international fire standards and guidelines need national cooperation [24]. Some researchers used pathfinder to simulate a large population in different facilities. A computer simulation was performed by Maohua Zhong et al.to analyze the exodus of a sizable population from a metro station [27]. Similarly, Jiawen et al. studied fire emergency evacuation from subway stations by using pathfinder software [28].

In response to fire and explosion hazards, several safety institutions and public health management authorities have developed recommendations that include directions for the safe handling of oxygen. NFPA and public sources cite a claim that the fire and explosion on April 24, 2021, in an oxygen tank in Baghdad caused at least 82 fatalities and hundreds of injuries, with the number of deaths likely to rise owing to severe burns, the source added[29]. Intensive OT in a confined space can result in an uncontrolled environment enriched with oxygen, which constitutes approximately 21% of the Earth's atmosphere under normal conditions, but when its concentration exceeds 23%, it can cause fire and explosion hazards[30]. Medical oxygen is not a direct hazard, but it can cause problems if it comes into contact with oils, greases, or fats.

It tends to self-ignite and strongly promotes the combustion of substances (including ointments, gels, and disinfectants). Fire safety analyses have confirmed this, demonstrating that intensive OT and the presence of combustible materials in hospitals facilitate rapid ignition and the spread of fires [31]. Materials that do not burn in air, such as fire-resistant materials, can burn vigorously in oxygen-enriched or pure oxygen-enriched air [32]. Additionally, oxygen considerably raises both the flame temperature and the rate of burning. Electrical devices particularly specialized medical equipment, power cables, extension cords, air conditioners, disinfectants containing alcohol or hydrocarbons, and special medical equipment are examples of igniting devices that can start fires when the air in hospitals is enriched with oxygen [33]. The fact that oxygen is tasteless, odorless, and colorless adds another layer of complexity. The possibility of fire also increases because there are no physiological signs that would immediately notify staff of the presence of an uncontrolled oxygen-enriched atmosphere[34].

The evaluation of egress safety in nursing institutions, where egress guides are crucial due to bad patient conditions, is lacking in the literature even though many studies on fire or egress simulation have been undertaken thus far. Additionally, no studies have been done to increase the egress safety of nursing institutions that are already in use. This study assessed egress safety from a medical hospital using fire and evacuation simulations. (1) The Pyro-sim fire dynamic simulator was used to determine the safe evacuation time (ASET). Volume concentration key variables (temperature, visibility, CO, \({O}_{2}\), and \({CO}_{2}\)) and tenability criteria were analyzed. (2) Using Pathfinder egress simulations were run based on occupant characteristics, and the required safe egress time (RSET)—the amount of time needed for all inhabitants to leave the building—was computed. (3) By contrasting the ASET and RSET, pedestrian evacuation safety was assessed considering evacuation guidelines, and delay time. (4) The normalized egress guidelines and delay time were used to construct the egress safety criteria. (5) python was used for data analysis.

The hospitals are vulnerable to fire incidents because of patients’ reduced mobility. Several guides are required to evacuate the patients from the medical facility in case of a fire incident. This study is based on fire and pedestrian egress simulations by considering different delay times and increasing egress guides. Occupants’ safety is proposed by changing the route pattern during evacuation. Overall evacuation delay times and the number of guides are the main variables in this study. Based on simulations, egress safety criteria have been suggested, and these criteria can be used to evaluate the safety of a medical Hospital.

Figure 1 is the illustration of the proposed floor plan of the medical facility used in this study. The prototype medical facilities have two floors with multiple rooms. The wall's height is 2.75m with a floor area of 1173m². The evacuation safety was evaluated in this study by comparing RSET and ASET. A fire dynamic simulator is used for the required safe evacuation time for all patients, and Pathfinder is used to address egress simulations [27]. Pathfinder is an agent-based model which is used to predict behaviors based on occupant characteristics. A fire dynamic simulator developed by NIST is used to determine visibility, carbon monoxide, oxygen, and carbon dioxide. When the values of these gases exceeded the suggested deadline is very harmful to occupants’ health and limits the evacuation during a fire disaster. Below Table 1 reports the critical value limit criteria proposed by the national fire protection association (NFA) [29]. The Pyro-sim analysis results were used in this study to compute the times necessary for visibility, CO, \({CO}_{2}\), \({O}_{2}\), and temperature to meet each tenability criterion.

Table 1

**Reported tenability limit for 5-minute exposure to common gases**[35].
Physical property	Gas species	Incapacitation	Criteria
	Co	6000-8000ppm	Less than 1400 ppm
Allowable toxicity limit	\({\text{O}}_{2}\)	10–13%	more than 15%
	\({CO}_{2}\)	7–8%	less than 5%
Allowable visibility			greater than 5m
	\(℃\)	Reported tolerance time(sec)
	126	420
Temperature (\(℃\))	180	240
	205	180
Temperature limit			greater than 60 degrees

In this simulation Fire Dynamic simulator, the 2019 version was used [user guide], Fig. 2a illustrates the FDS model used for the fire dynamic simulation, and Fig. 2b shows the burner location and pedestrian evacuation routes. An elevator and two staircases (STA, STB) were built as egress routes. It was assumed that the second floor of the prototype medical hospital was under fire due to an unknown reason. We have used short-form STA and STB for our convenience in this study. In a fire emergency, using elevators to leave the building is typically prohibited. But some countries permit the use of elevators in emergencies but under wholly distinct operations. Medical facilities use beds, and wheelchairs (self or aided) to evacuate the patients. The fire simulation was run for 600s with a \(20℃\) beginning temperature assumption. The input features of the FDS model are listed in Table 2. The mesh size and D for fire simulation for this simulation are based on guidelines preferred by the FDS user guide.

\({D}^{*}={\left(\frac{{Q}^{\bullet }}{{\rho }_{\infty }{C}_{p}{T}_{\infty }\sqrt{g}}\right)}^{\frac{2}{5}}\) ………………………………………………………………….1

Table 1

Input fire characteristics
Input
Total heat release	29,854.0kW
Analysis Time	600seconds
	Fuel type	Polyurethane foams (GM29)
	Formula	CH1.8
Materials	Co yield\(\left( {Y}_{co}\right)\)	0.042
Materials	Soot yield \(({Y}_{S}\))	0.228
Air species	Interval	Visibility(m)
	Installed height	Carbon monoxide(ppm)
		Carbon dioxide
		Oxygen (%)
Compartments	Fire growth rate	medium	0.01172kW/m²
	Burning area	1m×1m
	bed	4665
	wardrobe	270
Max HRR	chair	454
	curtain	240
	Refrigerator	2125
	TV	239

By using Eq. (1) mesh size was set at (0.25×0.25 ×0.28) m. Where, Q is the overall heat release rate in kW, the specific heat \({C}_{p}\) is 1.005 kJ/kg-K, the ambient temperature (T) is 293 K, and the acceleration (g) caused by gravity is 9.8 m/s². The fuel chemical characteristics are chosen from the values listed in the Society of Fire Protection Engineers handbook [30]. The National Center for Forensic Science's database [31] was used to determine the heat release rate of each combustible, which was presumed to be beds, tables, and chairs. Eq. (2) was used to model the t-squared fire curves depicted in Fig. 3 by calculating the heat release rate (Q) as a function of time:

\(Q={\alpha t}^{2}\left(kW\right)\) ……………………………………………………………………………….……2

Where Q is the total heat release,t is the time, and \(\alpha\)is the fire growth coefficient. The classification of \(\alpha\)normally includes "slow," "medium," "rapid," and "ultrafast." The "Structural Design for Fire Safety" was used as the basis for the medium level \((\propto =0.0117 )\)that was used in this investigation[36]. The smoke exhaust system and fire extinguisher were assumed to be off for the fire simulation, and the burner area was1m².

4.2. Simulation Results

4.2.1 Tenability Limits

To evaluate life safety hazards using a numerical simulation tool, quantitative tenability criteria are required. An average individual exposed to an extreme temperature for more than a few minutes is likely to get burned and die. Table 1 reported the tolerance time values concerning temperature and also for other toxic gases.

Figure 4 shows the smoke behavior on the second floor of the hospital building. When the fire first started during time t it began to spread across the room as shown in Fig. 4a. After about 105s it covers the whole room and fills in the hallway running towards stairs (STA and STB), and the elevator. After 195s smoke covered the stair (STA) and moved towards STA. For about 210 seconds smoke covered STB, and at 315s it covered the elevator area as shown in Fig. 4. Figure 5 shows the simulation results for each of the three egress routes (STA, STB, and the elevator) for the fire duration (t) in terms of temperature, visibility, \({O}_{2}\), CO, and \({CO}_{2}\). The tenability criteria are shown by the black highlighted line in each graph and are considered available safe egress time for each factor. Table 1 listed the tenability criteria for each species. The visibility criteria for STA (194s), STB (215s), and an elevator(310s) are shown in Fig. 5a. The temperature at STA was highest above 70 degrees and achieved tenability criteria but the temperature for STB and elevator were 54 degrees, and 40 degrees shown in Fig. 5b. The CO, and, \({CO}_{2}\) modeling results are shown in Figs. 5c, and 5d respectively. The highest CO and \({CO}_{2}\) concentration was achieved in STA. The simulation results for \({O}_{2}\) is shown in Fig. 5e, respectively. The tenability criteria were attained by STA and STB. All species were not met the tenability criteria, based on assumed fire duration(t). The visibility mostly influences the safe egress time of the occupant. That’s why we set 192s as the acquired safe egress time for occupants in this prototype medical hospital.

Another oxygen concentration simulation was performed in the corridor which is located in front of the fire incident room. In the above simulation, the oxygen concentration was within the normal range but for the below simulation initial oxygen concentration varies from 21–25%. Oxygen concentration impacts the value of temperature, visibility, and other quantities as shown in Fig. 6. The period available for medical staff to conduct self-evacuations and other forms of evacuation is directly impacted by the dynamics of the fire growth. It is anticipated that, under typical hospital room conditions, a fire would start and spread at a normal rate, taking 5 minutes to reach flash over. However, this period would be drastically shortened if exposed to an atmosphere high in oxygen. The calculated ASET time, which is related to the occurrence of human-critical circumstances (high temperature, pungent smoke), is even less than the authorized duration of 5 minutes.

According to the simulation analysis, the medical staff only have 150s in an oxygen-rich environment to egress patients and themselves. This is a relatively very small available egress safe time (ASET) and it is not easy because of fire pre-detection time delays(fire alarm delays). Some serious factors also need to be taken into account. The visibility in the corridor was considered. The results showed that due to extreme oxygen concentration the time required to cross the corridor was limited to 50s at 25% oxygen concentration and declined evacuation drastically. Thus, it indicated that the oxygen concentration can cause serious risk for medical staff in case of a fire incident.

5.1. Simulation Model

The sampled Pathfinder model for pedestrian evacuation is shown in Fig. 7. In this simulation, the total number of evacuees and their profile parameters such as evacuee’s velocity, evacuee width, and pre-movement time were considered. South Korean Administration Rule for senior citizens act suggests a minimum of 23.6m² for each patient [35]. In this simulation, we included 110 patients and divided them into their health statuses such as severe, medium, low and normal, etc. According to the patient’s health status, we categorized the patients into different groups and egress route choices as shown in Table 3. During a fire incident, 12 patients were bed bound,30 patients were in a wheelchair with assistance,20 patients were in a wheelchair (without assistance),30 were in guided wheelchairs (with assistance), and 40 were output patients. However, some were seriously ill so they were evacuated with their beds with the assistance of egress guides.

5.2 Simulation settings

The evacuation characteristics of patients and hospital staff are chosen by considering the real scenarios of the hospital as well as the type of profiles and their speed. The evacuation type and the total number of patients for each profile are listed in Table 3.

Table 3

Evacuation type and the number of patients.
Evacuation type	Health status	Behaviors	Number of patients	Ratio (%)
Bed bound	severe	Wait for assistance	12	9.8
Wheelchair (guider)	High	Wait for assistance	30	24.5
Wheelchair (self)	medium	-Go to the exit	20	16.4
Output patient	below	-Go to the exit	40	32.8
Visitors	Healthy	-Go to the exit	20	16.4
Total			122	100

Table 4

Bed type for seriously ill patients.
Bed type	Bed size(m)	Assistance needed(per/bed)
Young	0.5×0.9×0.88	1
Adults Bed	0.9×2×1	2

The patient and egress guide characteristics and profiles are listed in Table 5. Lee et al. based on egress type the speed, patient’s width, and egress guides were determined [2]. The hospital pedestrian egress rules and regulations suggest at least15 20 seconds required as pre-evacuation time [35]. One doctor,4 nurses,2 physical therapists,2 helpers, and more than 60 caretakers were needed to serve 120 patients. These members of staff were imagined to be egress guides for patients during the simulations. Staff in nursing hospitals often works in shifts, with fewer people working night shifts than day hours. As a result, in this study, hospital staff were assumed as variables during egress simulations. For the egress simulation model, there were between 10 to 80 workers, spaced every 10. It was expected that half of the workers in each case were listed in Table 6.

Table 5

Profile parameters
	Profile	Mobility feature	Count	Area covered	Speed(m/s)	Preparation time(s)
	Doctors	Move without assistance	1–3	0.402	1.10–1.50
	Nurse	Move without assistance	2–6	0.357	1.10–1.50
Hospital staff	Emergency team	Move without assistance	5–50	0.403	1.10–1.50
	Others/firefighters	Move without assistance	2–5	0.358	1.10–1.50
	Bed	Need of assistance		0.723	0.34–0.50	20–25
Visitors	Walk			0.402	1.0-1.4
Patients	Wheelchair (guider)	Need of assistance		0.71	1.3	10–15
	Wheelchair (self)			0.7	1.4	10–15
	Walk			0.49	0.6

Table 6

The number of hospital staff.
	Doctors	Nurses	Physio- therapists	General worker	Total guides
1	1	2	1	1	5
2	2	1	1	7	10
3	2	2	1	10	15
4	2	3	1	14	20
5	2	2	2	14	25
6	2	2	1	25	30
7	2	4	2	27	35
8	1	3	1	36	40
9	1	4	2	38	45
10	2	5	1	42	50

During a fire, the delay time is the period it takes for a patient and egress guide to begin exiting. The delay period is the sum of a fire detection time, fire notification time, and pedestrian pre-movement time. The delay can be minimized by promptly considering notifications or warning signals or by closely monitoring closed circuitry cameras (CCTVs). The egress delay time, however, increases if there is a lack of personnel who are qualified to manage fire emergencies, CCTVs, and broadcasting facilities, raising the RSET.

In this study, the evacuation safety time was assessed in terms of delay time by running simulations with the delay time set to 0-360s by an interval of 60s. The number of occupants and their evacuation time is shown in Fig. 8 (selecting egress guides = 20).

Table 7

shows the time delays variables and egress time
Guides		Random route allocation (delay time(s))
0	0	60	120	180	240	300
10	407	467	527	587	647	707
15	342	407	467	527	587	647
20	293	336	396	456	516	576
25	242	315	375	435	495	555
30	241	290	350	410	470	530
35	171	276	336	396	456	516
40	168	233	293	353	413	473
45	159	226	286	346	406	466
50	162	218	278	338	398	458
55	161	215	275	335	395	455
60	149	205	265	325	385	445

5.2. Simulation Results

The RSET for each case was determined to be the moment when everyone had safely evacuated the building. The table shortlists the number of evacuees who failed to egress during available egress time (ASET). When the egress time was set at 192s which was the lowest visibility attained during fire simulations. About 22 persons failed to exit the building when there were 10 egress guides present and a delay of 0 seconds. When the number of numbers of egress guides was 10 at a delay time was 0 seconds around 22 people failed to egress the building. When the egress guide's number reached 20 the evacuees safely evacuated the building. When the delay time is maintained and increased the egress guides everybody egressed safely. When delay time increased above 192 seconds everybody failed to egress. When the number of egress guides increases, the number of egress evacuees tends to rise as well.

Table 8

Number of people who failed to egress
Number of Egress guides	Delay time(s)
	0	60	120	180	240
10	22	29	34	40	47
15	48	31	37	47	52
20	0	10	39	47	56
25	0	6	40	55	60
30	0	0	18	54	66
35	0	0	15	55	71
40	0	0	0	25	74
45	0	0	0	28	80
50	0	0	0	34	84
55	0	0	0	30	88
60	0	0	0	32	89

The British standard institute defined the egress delay for public safety [37] as shown in Table 9. The delay times are categorized as P1(< 180s), P2(300s), and P3(> 480s) based on the professional skills of the fire safety team and the staff during a fire event and also the accessibility of closed-circuit cameras (CCTVs) and messaging devices. When designing for life safety the ASET>\(\varDelta {t}_{esc}\) where \(\varDelta {t}_{esc}=\varDelta {t}_{a}\)+\({\varDelta }_{tevac}\)

\(\varDelta {t}_{esc}\) is the egress time to reach a place of safety;

\(\varDelta {t}_{evac}\) is the required time after alarm for evacuees to egress to a safe place;

\(\varDelta {t}_{a}\) = The time of ignition of the alarm.

Table 9

Egress delays for public safety
Facility type	P1	P2	P3
Medical facilities, elderly nursing homes, residential areas (A large number of evacuees need assistance)	Less than 180	300	Greater than 480

P1: Voice communication or live; P2: Visual warning and using trained staff; P3: Fire warning system, and staff without relevant training.

RSET is often chosen based on the occupancy type’s egress safety time during performance-based egress safety design. The number of egress guides and the delay time should be taken into account when evaluating the medical hospital’s egress safety, according to data from prior studies. In general, the number of evacuees in a hospital can be estimated based on the size of the building, the number of patients, the number of staff, and the number of caretakers. As a result, the normalized number of egress guides is an objective statistic that can be used to evaluate the egress safety criteria because it considers both the area and capacity of the facility as well as the number of patients. The pedestrian evacuation safety criteria are shown in Fig. 10. Using the average number of egress guides and the delay time at a typical prototype medical facility, one can determine whether an evacuation was successful or unsuccessful. The left space indicates successful egress, whereas the red region indicates failure. These suggested criteria can be used to assess egress safety and set up supplemental measures in typical nursing institutions that are currently in operation. In other words, using proposed criteria based on the number of egress guides and delay time computed by accounting for the broadcasting facilities, CCTVs, and staff competency in handling fire situations, it is possible to determine whether the egress safety in a functioning medical hospital is safe or unsafe.

There are two ways to improve egress safety when it has been deemed dangerous. The most important factor is a delay, delay time should be decreased by improving staff expertise in responding to a fire. By installing CCTVs or using building broadcasting equipment, the evacuation can be smooth and successful. To assure safe egress or shorten the delay time, the number of egress guides needed can be determined using the provided criterion. Medical facilities can be improved egress safety while lowering costs and boosting guiders' skills.

The following results were achieved after conducting simulations of a model medical hospital's fire and evacuation.

Using FDS, the visibility, CO, , oxygen, and temperature of a typical prototype medical hospital were examined based on the length of the fire. The tenability of the criteria was computed by taking the lowest value as ASET and comparing the results for other egress routes. The reduction in visibility brought on by the spread of smoke served as the primary basis for determining the ASET. Prototype medical hospital evacuation simulations were run using Pathfinder software by taking the egress characteristics such as the number of egress guides and delay time(s) of the evacuees. According to the simulation’s findings, the RSET increased as the delay time and egress guide number decreased, and at least 30 egress guides should be on duty to tackle any emergency for this prototype hospital and can adjust 60s delay during a fire incident.

Fires in a highly concentrated environment can spread quickly and can reduce the response to the fire as well as the evacuation time. This study suggests the separation of critical health care wards from normal hospital wards. By contrasting the ASET and RSET, the evacuation life safety of the prototype medical facility was assessed. The normalized number of evacuation guides was proposed based on delay time simulation findings. The suggested egress criteria can be used for a typical medical hospital to evacuate occupants quickly and efficiently.

The study only gives an insight into the medical facility without a smoke exhaust system. The egress safety system with smoke exhaust would greatly impact the evacuation and need to study in the future.

L. Chen, T.-Q. Tang, Z. Song, H.-J. Huang, and R.-Y. Guo, “Child behavior during evacuation under non-emergency situations: Experimental and simulation results,” Simul. Model. Pract. Theory, vol. 90, pp. 31–44, 2019.
L. Jian, Y. Lizhong, and Z. Daoliang, “Simulation of bi-direction pedestrian movement in corridor,” Phys. A Stat. Mech. its Appl., vol. 354, no. 1–4, pp. 619–628, Aug. 2005, doi: 10.1016/j.physa.2005.03.007.
C. Ahn, J. Kim, and S. Lee, “An analysis of evacuation under fire situation in complex shopping center using evacuation simulation modeling,” Procedia-Social Behav. Sci., vol. 218, pp. 24–34, 2016.
X. Yang, H. Dong, and X. Yao, “Passenger distribution modelling at the subway platform based on ant colony optimization algorithm,” Simul. Model. Pract. Theory, vol. 77, pp. 228–244, 2017.
W. Zhang and Z. Yao, “A reformed lattice gas model and its application in the simulation of evacuation in hospital fire,” in 2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010, pp. 1543–1547.
Z. Jiang, P. Zhang, R. Shang, and X. Tian, “Investigation and simulation on human evacuation behaviour in large hospital building in Shenyang,” Procedia Eng., vol. 71, pp. 101–106, 2014.
T. Shimada and H. Naoi, “An experimental study on the evacuation flow of crowd including wheelchair users,” Fire Sci. Technol., vol. 25, no. 1, pp. 1–14, 2006.
S. Tsuchiya, Y. Hasemi, and Y. Furukawa, “Evacuation characteristics of group with wheelchair users,” 2007.
M. Hashemi, “Dynamic, stream-balancing, turn-minimizing, accessible wayfinding for emergency evacuation of people who use a wheelchair,” Fire Technol., vol. 54, no. 5, pp. 1195–1217, 2018.
D. Lee, I. Heo, D. Khaliunaa, S.-J. Han, and K. S. Kim, “Performance-Based Evaluation on Egress Safety of Nursing Hospitals Considering Egress Guides and Delay Time,” J. Korean Soc. Hazard Mitig., vol. 20, no. 3, pp. 149–157, 2020.
J.-B. Kim, J.-O. Kim, and E.-S. Back, “A study on the evaluation of evacuation safety function of an elderly care hospital,” Fire Sci. Eng., vol. 24, no. 3, pp. 9–19, 2010.
S. Hong, J.-S. Jang, H.-A. Park, and D.-H. Rie, “A Study on Evacuees Risk Assesment for Application of Spatial Risk Information,” Fire Sci. Eng., vol. 29, no. 2, pp. 8–12, 2015.
A. L. E. Hunt, S. M. V Gwynne, and M. Strategies, “in Hospital Evacuation,” Fire Technol., vol. 56, no. 5, pp. 2209–2240, 2020, doi: 10.1007/s10694-020-00971-5.
M. Xiao et al., “Simulation and optimization of fire safety emergency evacuation in university library,” AIP Adv., vol. 11, no. 6, p. 65323, 2021.
S.-M. Hung, S.-C. Wang, S.-W. Chien, C.-H. Su, and L.-P. Chen, “The Enhancement of Fire Safety in Small-Scale Senior Citizen Welfare Institutions Based on Fire Protection Defense-in-Depth Strategy,” Sustainability, vol. 14, no. 6, p. 3196, 2022.
X. Li, W. Chen, C. Wang, and M. A. Kassem, “Study on Evacuation Behavior of Urban Underground Complex in Fire Emergency Based on System Dynamics,” Sustainability, vol. 14, no. 3, p. 1343, 2022.
H. Boonngam and S. Patvichaichod, “Fire evacuation and patient assistance simulation in a large hospital building,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 715, no. 1, p. 12004.
E. Ronchi and D. Nilsson, “Fire evacuation in high-rise buildings: a review of human behaviour and modelling research,” Fire Sci. Rev., vol. 2, no. 1, pp. 1–21, 2013.
C. N. van der Wal, D. Formolo, M. A. Robinson, and S. Gwynne, “Examining evacuee response to emergency communications with agent-based simulations,” Sustainability, vol. 13, no. 9, p. 4623, 2021.
L. Fu, S. Cao, W. Song, and J. Fang, “The influence of emergency signage on building evacuation behavior: An experimental study,” Fire Mater., vol. 43, no. 1, pp. 22–33, 2019.
A. Abdelghany, K. Abdelghany, H. Mahmassani, and W. Alhalabi, “Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities,” Eur. J. Oper. Res., vol. 237, no. 3, pp. 1105–1118, 2014.
J. Cho, G. Lee, and S. Lee, “An automated direction setting algorithm for a smart exit sign,” Autom. Constr., vol. 59, pp. 139–148, 2015.
H. Xie, L. Filippidis, S. Gwynne, E. R. Galea, D. Blackshields, and P. J. Lawrence, “Signage legibility distances as a function of observation angle,” J. fire Prot. Eng., vol. 17, no. 1, pp. 41–64, 2007.
I. Heo, S.-J. Han, H. Kang, S. J. Hwang, Y. H. Ju, and K. S. Kim, “Performance-based evaluation on evacuation safety of studio residential buildings according to installation of smoke exhaust system,” J. Korean Soc. Hazard Mitig., vol. 18, no. 7, pp. 1–8, 2018.
A. D’Orazio, L. Grossi, D. Ursetta, G. Carbotti, and L. Poggi, “Egress from a hospital ward during fire emergency,” J Homepage, vol. 10, pp. 1–10, 2020.
C. Tsungjung, “Research on the Inpatient Evacu alion Behavior and the Route of Escape m Hospital,” Urban. Archit., vol. 6, p. 11, 2011.
M. Zhong, C. Shi, X. Tu, T. Fu, and L. He, “Study of the human evacuation simulation of metro fire safety analysis in China,” J. Loss Prev. Process Ind., vol. 21, no. 3, pp. 287–298, 2008.
J. Qin, C. Liu, and Q. Huang, “Simulation on fire emergency evacuation in special subway station based on Pathfinder,” Case Stud. Therm. Eng., vol. 21, p. 100677, 2020.
Verzoni,A., “NFPA Journal -Deadly Trend.,” NFPA. J. 2021. Available online: https://www.nfpa.org/News-and-Research/Publications-and-media/NFPA-Journal/2021/Summer-2021/News-and-Analysis/Dispatches (accessed Sep. 14, 2022).
S. S. Suhaili et al., “OVERVIEW OF FIRE SAFETY MANAGEMENT,” vol. 11, no. 12, pp. 89–97, 2020, doi: 10.34218/IJARET.11.12.2020.009.
M. M. Islam, S. M. A. Ullah, S. Mahmud, and S. M. T. U. Raju, “Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients,” SN Comput. Sci., vol. 1, no. 5, p. 274, 2020, doi: 10.1007/s42979-020-00300-1.
European Industrial Gases Association – COVID 19., “Oxygen e-Learning. Available online:” https://www.eiga.eu/elearning-courses/oxygen/ (accessed Sep. 14, 2022).
L. P. Smith and S. Roy, “Operating room fires in otolaryngology: risk factors and prevention.,” Am. J. Otolaryngol., vol. 32, no. 2, pp. 109–114, 2011, doi: 10.1016/j.amjoto.2009.11.004.
R. Sharma, H. Bakshi, and A. Banerjee, “Fire Safety Hazards: How Safe Are Our Hospitals?,” Indian J. community Med. Off. Publ. Indian Assoc. Prev. Soc. Med., vol. 45, no. 1, pp. 104–105, 2020, doi: 10.4103/ijcm.IJCM_182_17.
P. J. DiNenno, D. Drysdale, C. L. Beyler, W. D.Walton, R. L. P.Cruster, J. R. Hall and J. M. Watts., SFPE Handbook for Fire Engineering, 3rd Editio. N.F.P.A., U.S.A., 2002.
Andrew H. Buchanan & Anthony K. Abu, Structural design for fire safety, Second Edi. University of Canterbury, New Zealand, 2017.
D. B. S. B. S. D. Fire, “Safety Engineering in Buildings. Part 1: Guide to the Application of Fire Safety Engineering Principles,” Br. Stand. Inst., 1997.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Fire safety status and evacuation of medical facility considering elevated oxygen concentrations

Status:

Version 1

Abstract

Figures

1. Introduction

2. Research Significance

3. Evacuation Safety Assessment Procedure

4. Fire Simulation Model

4.2. Simulation Results

4.2.1 Tenability Limits

5. Occupants Egress Simulation Model

5.1. Simulation Model

5.2 Simulation settings

5.2. Simulation Results

Conclusions

References

Additional Declarations

Status:

Version 1

	Doctors	Nurses	Physio- therapists	General worker	Total guides
1	1	2	1	1	5
2	2	1	1	7	10
3	2	2	1	10	15
4	2	3	1	14	20
5	2	2	2	14	25
6	2	2	1	25	30
7	2	4	2	27	35
8	1	3	1	36	40
9	1	4	2	38	45
10	2	5	1	42	50

	Doctors	Nurses	Physio- therapists	General worker	Total guides
1	1	2	1	1	5
2	2	1	1	7	10
3	2	2	1	10	15
4	2	3	1	14	20
5	2	2	2	14	25
6	2	2	1	25	30
7	2	4	2	27	35
8	1	3	1	36	40
9	1	4	2	38	45
10	2	5	1	42	50

	Doctors	Nurses	Physio- therapists	General worker	Total guides
1	1	2	1	1	5
2	2	1	1	7	10
3	2	2	1	10	15
4	2	3	1	14	20
5	2	2	2	14	25
6	2	2	1	25	30
7	2	4	2	27	35
8	1	3	1	36	40
9	1	4	2	38	45
10	2	5	1	42	50