A probabilistic occupant response model for fire emergencies

Fire Safety Journal 68 (2014) 41–51

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Fire Safety Journal journal homepage: www.elsevier.com/locate/firesaf

A probabilistic occupant response model for fire emergencies Xia Zhang n, Xiao Li, George Hadjisophocleous Department of Civil and Environmental Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6

art ic l e i nf o

a b s t r a c t

Article history: Received 6 May 2013 Received in revised form 2 April 2014 Accepted 31 May 2014 Available online 21 June 2014

The present paper describes a probabilistic occupant response model for fire emergencies, which is integrated into a fire risk analysis model called CUrisk. Based on the PIA process, i.e., Perception, Interpretation and Action, the occupant response model predicts the probabilities of occupants perceiving fire signals due to direct perception, receiving fire alarms due to the activation of local alarms, sprinklers, the central alarm and the voice alarm, being warned by the other occupants and fire department, and taking actions including pulling the fire alarm, warning other occupants, calling the fire department, and commencing evacuation. The occupant response model is applied to predict the probabilities of evacuation initiation for a number of scenarios that consider combinations of fire detection and alarm systems for a mid-rise building. The results of the model show that asleep occupants need much longer response time to start evacuating and have lower probabilities of starting evacuation than awake occupants, which are consistent with what is observed in reality. Additionally, fire protection systems with only local alarms and only sprinklers connected to the central alarm can be improved significantly with systems with smoke detectors alone or combined with sprinklers connected to a central alarm, which result in higher probabilities of evacuation initiation with shorter delay times. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Occupant response model Probabilistic model Fire emergency Fire detection system Evacuation Mid-rise building

1. Introduction Life safety is one of the most important and minimum requirements buildings must meet. Whether a building can meet this requirement depends on the ability of occupants in the building to successfully evacuate from it in the case of emergencies like a fire prior to the onset of untenable conditions in the egress routes. Therefore, the accurate prediction of the required evacuation time is one of the most critical parts of fire risk analysis and performance-based fire safety design. For fire emergencies, evacuation time comprises pre-evacuation response time and travel time. It is completely possible that the pre-evacuation response time dominates the evacuation time. For example, in experiments to study the evacuation of four mid-rise residential buildings during a simulated fire emergency, it was found that the pre-evacuation response times following central alarm sounding accounted for at least 2/3 of the total evacuation times [1]. In the World Trade Center attack incident of 2001, the average delays between the moments the tower buildings were impacted to the point of evacuation initiation was 6 min [2]. The pre-evacuation response time in a fire emergency of a single house was estimated to be from 30 s to 10 min [3]. n

Corresponding author. Tel.: þ 1 613 520 2600x1434. E-mail addresses: [email protected] (X. Zhang), [email protected] (X. Li), [email protected] (G. Hadjisophocleous). http://dx.doi.org/10.1016/j.firesaf.2014.05.017 0379-7112/& 2014 Elsevier Ltd. All rights reserved.

To predict the evacuation time, a great number of evacuation models have been developed [4–8]. While travel time has been included in all evacuation models, pre-evacuation response time is considered in only some of the evacuation models such as Fluid Model [9], Magnetic Model [10], EXITT [11], EvacuatioNZ [12], EXIT 89 [13], Pedgo [14], FDS þ Evac [15], GridFlow [16], Simulex [17], SGEM [18], CRISP [19], and Evacsim [20]. In these models, preevacuation response time, consisting of perception time and interpretation time, is simulated using four approaches. The first approach sets the pre-evacuation response time as a certain value specified by the user, as used in Fluid Model [9] and Magnetic Model [10]. Alternatively, pre-evacuation response time can also be calculated based on the assumption that perception time linearly decreases with the increase of fire cues, and actions will be taken following delays, as used in EXITT [11]. While this approach is easy to perform, it completely ignores the probabilistic characteristics of the perceptions and actions of occupants and the effects of fire detection and alarm systems on perception time. Actually, mean pre-evacuation times could vary from no more than 1 min to 190.8 min, according to a summary of pre-evacuation response times derived from 22 actual fires and evacuation drills [21]. The second approach specifies pre-evacuation response times as a distribution or a random number. This approach is used in EvacuatioNZ [12], EXIT 89 [13], Pedgo [14], FDS þEvac [15], GridFlow [16], Simulex [17], and SGEM [18]. This approach is better

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X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

Nomenclature floor i, j, k, n I P R RTI t Δt t0, t1, t2, T0 T1 T2 T3 T4 tmax Tmax v

floor number of a compartment compartment number probability of interpretation probability reliability or location relationship factor response time index time time step t3, t4 start times of fire detection states ambient temperature T0 þ5 1C T0 þ25 1C temperature rating of heat detectors or sprinklers flashover temperature, 600 1C time corresponding to maximum temperature Tmax maximum temperature ceiling jet gas velocity

than the first approach in that it includes the probabilistic characteristics of occupants' response. However, it is still not a general model since the effects of fire severity and fire detection and alarm systems on occupants’ response are not reflected. The third approach assumes that occupant response is probabilistically related to the perceived level of fire severity. This approach is used in CRISP [19] and Evacsim [20]. While it is more advanced compared with the first and second methods, perception time, perception probability, and the effects of fire detection and alarm systems are not considered. The fourth approach considers occupant response and evacuation as a result of a PIA process, i.e., Perception, Interpretation, and Action, which may interact with each other. It was proposed to be incorporated in a risk-cost assessment model [22,23]. This approach relates the variation of response probability with time to fire severity. According to the relationship between the occupants' location and the compartment of fire origin, occupants in a building are classified into three groups, i.e., occupants in the compartment of fire origin, occupants in the same level as the compartment of fire origin, and occupants in other levels. Occupants' perceptions consist of occupants' direct perception, warnings from fire detection and alarm systems, and warnings from other occupants and the fire department. Following perceptions, occupants will interpret their perceptions and take various actions including commencing evacuation. This approach is easy to understand and adopt, and includes the characteristics of fire detection and alarm systems. The calculation of the perceptions of occupants in all compartments is related to fire states in the compartment of fire origin. This may not be appropriate, as occupants generally perceive a fire from their own environment. Additionally, the classification of occupants into three groups makes the approach not appropriate to be applied to buildings with more than two storeys. The present paper proposes major improvements on the fourth approach mentioned above. Among other improvements, it is noteworthy to note the following aspects. The probabilities of occupants' perceptions, interpretations, and actions are related to the fire states in their own compartments and the classification of occupants based on the relationship between their locations and the compartment of fire origin is not necessary. Additionally, some of the estimated parameters are replaced with statistical data if applicable data are available. Meanwhile, the activation delays of fire detectors are included. These improvements make the use

δca-fd

availability of central alarm connection to fire department

Subscripts aw ca dp ev fd i, j, k, n la oo sp va pf sd hd wo

awake central alarm direct perception evacuation fire department compartment number local alarms occupants in other compartments sprinklers voice alarm pulling fire alarm smoke detectors heat detectors warning occupants in other compartments

of this approach applicable to buildings with more than two storeys. A computer model based on these improvements has been developed and integrated into a fire risk analysis model CUrisk [24] developed at Carleton University. The model is applied to simulate the occupant response of a mid-rise building in fire emergencies. It is difficult to validate the model with specific individual cases since no such quality data are available. Therefore, the simulation results are qualitatively compared to statistical data for home fires. 2. Brief summary of related models of the fire risk analysis model CUrisk The fire risk analysis model CUrisk [24] consists of a series of sub-models. Of these sub-models, the fire growth and smoke movement sub-model provides inputs for the occupant response model and other sub-models. The occupant evacuation sub-model takes the outputs of the response model and produces evacuation results, which are read into the risk analysis sub-model that determines whether occupants are affected by the fire and smoke conditions in the building. 2.1. Fire growth and smoke movement sub-model The two-zone fire growth and smoke movement sub-model used in CUrisk, detailed elsewhere [25,26], can predict fire growth and smoke movement of both the pre- and post-flashover phases of a fire. It calculates and outputs the heat release rate and the temperatures, concentrations of oxygen, carbon dioxide, carbon monoxide, in each layer of a multi-compartment building as well as the interface height. 2.2. Occupant evacuation sub-model The evacuation sub-model used in CUrisk is a probabilistic model based on the Monte Carlo methods [27]. It starts with the inputs of building and occupant properties, smoke conditions and the probabilities of evacuation initiation of occupants in each compartment, which are provided by the user, the fire growth and smoke movement sub-model, and the present occupant response model, respectively. The evacuation sub-model adopts a coarse

X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

network approach to describe the building and an individual perspective to describe occupants. Each occupant has independent actions and can be tracked separately in the simulation. During a Monte Carlo run, in each time step, a random number is generated for each occupant. If it is less than the normalised evacuation initiation probability of the occupants in the compartment where the occupant locates, he will start to evacuate. Otherwise, he will wait for the next time step. The evacuation sub-model produces statistical results related to evacuation times and other data needed by the risk analysis sub-model.

43

are less than 11 min. These values are recommended only if more appropriate values are not available. Otherwise, the recommended values can be replaced with better values through input files easily. Meanwhile, all time parameters are rounded to the nearest and conservative 30 s, and all reliability parameters are rounded to the nearest and conservative 0.05, since the present model cannot produce results with precision better than 30 s in time and 0.05 in reliability, according to the authors' estimation. The model adopts data mainly from statistical results for healthy people and therefore is not suitable at its present state for people with disabilities.

3. Probabilistic occupant response model for fire emergencies 3.1. Fire detection states The occupant response model predicts the probabilities of occupants perceiving fire signals, receiving fire alarms or warnings, and taking actions along with time under fire hazardous conditions. The model takes input data related to building and fire protection system characteristics, an occupant distribution, fire hazardous conditions, and fire department response from the files created by the user or produced by the fire growth and smoke movement sub-models briefly introduced in Section 2.1, and produces probabilities of evacuation initiation varying with time. These probabilities are used in the occupant evacuation sub-model briefly introduced in Section 2.2. Human behaviour during a fire is too complex to be predicted precisely. To produce a probabilistic prediction of such complex phenomena, the model utilises the concept of PIA process, i.e., Perception, Interpretation and Action [22]. In this process, occupant response begins from perceiving fire cues or receiving fire alarms or warnings. Occupant perception can come from sources including direct perception, local alarms, sprinkler activation, the central alarm, voice alarm, other occupants, and the fire department. Following perception, occupants will take time to interpret these sources and then take actions. The actions considered in the model include pulling the fire alarm, warning other occupants, calling the fire department, and commencing evacuation to outside. While warnings such as the central and voice alarms will reach occupants throughout the building simultaneously, occupants in different compartments can have different perception probabilities as their locations can have different fire cues, leading to different probabilities of direct perception and local alarm activation in different compartments. Therefore, the response model produces probabilities for occupants in each compartment separately. However, occupants in the same compartment have the same fire cues and probabilities of direct perception and local alarm activation. As a result, the same probabilities are assumed for occupants in the same compartment. Such process is compatible with the fire growth and smoke movement sub-model providing inputs for the response model, and the evacuation sub-model using data produced by the occupant response model. In the model, parameters corresponding to a cumulative distribution of 90% are selected as the most conservative values, if the cumulative distribution function is available. For example, the selection of 11 min as the most conservative response time of the fire department means 90% of response times of fire departments

The process of occupants perceiving the fire and the fire detection system detecting the fire in each compartment is divided into five states: start of fire, fire cues, smoke detection, heat detection, sprinkler activation, and flashover. Characteristics of the five fire detection states are summarised in Table 1. The timing of these states is based on the development of the temperature of each compartment provided by the fire growth and smoke movement sub-model. Generally, different compartments in the same fire emergency can experience fire detection states with different time frames. State 0 starts from time t0 at which the fire has been initiated but the temperature of a compartment is still T0, the ambient temperature. In this state, fire signals in the compartment are too weak to be detected by either occupants or fire detection and alarm systems in the compartment. State 1 starts from time t1 at which the temperature of a compartment has increased to T1, 5 1C higher than the ambient temperature. In this state, fire signals in the compartment have grown significantly and occupants in the compartment begin to sense the fire. State 2 starts from time t2 at which the temperature of a compartment has increased to T2, 25 1C higher than the ambient temperature. In this state, fire signals in the compartment have grown large enough to activate local alarms and smoke detectors in the compartment. The selection of the temperature rise of 25 1C is based on experimental results showing that more than 90% of ionisation and photoelectric detectors activate to flaming fires at a temperature rise less than 25 1C [28]. State 3 starts from time t3 at which the temperature of a compartment has increased to T3, the temperature rating of heat detectors or sprinklers specified by the user. In this state, fire signals have grown to a size to activate heat detectors and sprinklers in the compartment. State 4 starts from time t4 at which the temperature of a compartment has increased to T4, 600 1C. In this state, flashover occurs in the compartment. It is assumed that any occupants who still remain in the compartment will die due to the severely untenable conditions. Detailed life hazard analysis is performed in another sub-model of CUrisk, where the effects of hot gas temperatures, radiation fluxes and concentrations of toxic gases are considered.

Table 1 Fire detection states in each compartment. No.

State characteristics

Start time

Start temperature

0 1 2 3 4

Start of fire detection Fire cues Smoke detection Heat detection and sprinkler activation Flashover

t0 t1 t2 t3 t4

T0 T1 T2 T3 T4

(Ambient temperature) (T0 þ 5 1C) (T0 þ 25 1C) (Temperature rating of heat detectors or sprinklers specified by the user) (600 1C)

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X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

3.2. Perceptions Occupants can perceive a fire from a number of sources, seven of which are taken into account in the present model: direct perception, local alarms, sprinkler activation, the central alarm, voice alarm, other occupants, and fire department. The probabilities of occupants in each compartment perceiving any one of the seven warning sources are calculated step by step. The probability of evacuation initiation of occupants in compartment i, Pev,i is the union of the probabilities of evacuation initiation of occupants due to perceiving the fire from various sources, which can be expressed as P ev;i ðtÞ ¼ P ev;dp;i ðtÞ [ P ev;la;i ðtÞ [ P ev;sp;i ðtÞ [ P ev;ca;i ðtÞ [ P ev;va;i ðtÞ [ P ev;oo;i ðtÞ [ P ev;f d;i ðtÞ

ð1Þ

where Pev,dp,i, Pev,la,i, Pev,sp,i, Pev,ca,i, Pev,ca,i, Pev,oo,i and Pev,fd,i are the probabilities of evacuation initiation of occupants in compartment i at time t due to their direct perception and receiving warnings from local alarms, sprinklers, the central alarm, voice alarm, other occupants, and fire department. The calculation of these probabilities is described in the following sections. 3.2.1. The direct perception of fire cues (DP) Occupants in a compartment can perceive a fire by directly sensing fire cues. This direct perception can include seeing flames or smoke, smelling smoke, feeling heat, and hearing the noise of the fire. There has been no research on the probability of direct perception available for reference. For simplicity, in this model, the probability density function of the direct perception of occupants in each compartment is assumed to have a symmetrical triangular distribution to produce a cumulative probability which increases from 0 at t1 to 1 at t3 of their own compartment. The reason for this is that the fire cues are too weak to be perceived when the time is earlier than t1 and the temperature is lower than T1, and the cues are strong enough to lead to a certain perception when the time is t3 and the temperature reaches T3. The probability of direct perception of occupants in compartment i at a given time, Pdp,i, can be obtained by integrating the single step probability of direct perception. For a compartment in which temperature cannot reach T3, a virtual time t3 is used by linearly extrapolating the time tmax at which the maximum temperature Tmax the compartment has reached according to the equation t3 ¼

T3 T0 t max T max  T 0

ð2Þ

where tmax and Tmax are taken from the output of the fire growth and smoke movement sub-model. Meanwhile, the probability of direct perception stops accumulating after tmax. Such extrapolation produces the same cumulative probability for the time duration with the same temperature history, whether the compartment reaches T3 or not. 3.2.2. Warnings from fire detection and alarm systems 3.2.2.1. Warning from local alarms (LA). A local alarm is a standalone smoke detecting and alarming device not connected to the central alarm system of the building. It can only alert occupants in the compartment where it is located. The calculation of the probability of local alarms in compartment i activating, Pla,i, is described in Section 3.2.2.4. 3.2.2.2. Warning from sprinkler activation (SP). If a sprinkler in a compartment is activated, occupants in the compartment will be able to perceive or hear the sprinkler activation. Similar to local alarms, the warning effect of the sprinkler activation itself is

limited to the compartment where the sprinkler is located. However, if the activation further activates the central alarm and voice alarm systems of the building, the warning effect will be counted into the warnings from the central and voice alarm systems. The calculation of the probability of sprinklers in compartment i activating, Psp,i, is described in Section 3.2.2.4. 3.2.2.3. Warnings from the central alarm (CA) and voice alarm (VA). If a building has a central alarm system, then once it is activated and sounded, all occupants in the building can hear the alarm and become aware of fire. A central alarm system can be activated by a number of devices including sprinklers, heat detectors and smoke detectors, and by occupants pulling fire alarms. Therefore, the probability of occupants in compartment i receiving warning from the central alarm, Pca,i, is the union of the probabilities of these devices in all compartments activating and the probabilities of occupants in all the other compartments pulling fire alarms, which is described by the following equation P ca;i ðtÞ ¼ ½P pf ;1 ðtÞ [ P pf ;2 ðtÞ [ ⋯ [ P pf ;i  1 ðtÞ [ P pf ;i þ 1 ðtÞ [ ⋯ [ P pf ;n ðtÞ [ P sp;1 ðtÞ [ P sp;2 ðtÞ [ ⋯ [ P sp;i ðtÞ [ ⋯ [ P sp;n ðtÞ [ P sd;1 ðtÞ [ P sd;2 ðtÞ [ ⋯ [ P sd;i ðtÞ [ ⋯ [ P sd;n ðtÞ [ P hd;1 ðtÞ [ P hd;2 ðtÞ [ ⋯ [ P hd;i ðtÞ [ ⋯ [ P hd;n ðtÞRca

ð3Þ

where Ppf,i is the probability of occupants in compartment i pulling fire alarms, Psp,i, Psd,i, and Phd,i are the probabilities of sprinklers, smoke detectors, and heat detectors in compartment i activating, and Rca is the reliability of the central alarm. The probability Ppf,i is related to perceptions and interpretations that have occurred at previous times and will be described in Section 3.4. The calculation of the probabilities of sprinklers and smoke and heat detectors activating is described in Section 3.2.2.4. If a central alarm system has a voice component, a voice message will activate at a delayed time tva after the activation of the central alarm to alert the occupants in the building. Thus the probability of occupants in compartment i receiving warning from the voice alarm can be obtained by multiplying the reliability of the voice alarm Rva and the probability of the central alarm activating at a prior time P va;i ðtÞ ¼ Rva P ca;i ðt t va Þ

ð4Þ

3.2.2.4. The calculation of the probabilities of fire detection and alarm systems activating. Fire detectors, including local alarms, sprinklers, and smoke and heat detectors, are assumed to activate within an activation time range rather than to deterministically activate at their temperature ratings, to reflect the dispersions of the activation times of these detectors. The probability density functions of the activation of these detectors are assumed to have symmetrical triangular distributions with their activation times located at the center of the activation time ranges. The lengths of the time ranges are taken to be 2 min based on the activation time standard deviation of up to 22 s given by a smoke alarm sensitivity study [29]. The cumulative probabilities of these devices over their activation ranges are equal to their reliabilities. The activation times of local alarms and smoke detectors are taken as t2. For heat detectors and sprinklers, an activation delay reflecting response time of the devices after t3 is used as their activation times. The time delay is calculated as RTI/v1/2 in seconds, where RTI is the response time index of heat detectors and sprinklers in (m  s)1/2, and v is the ceiling jet gas velocity in m/s, based on the response time estimation method [30]. If v is taken as 1.5 m/s, the activation delay will be 0.82RTI in seconds. If RTI is available for heat detectors and sprinklers, their activation

X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

delays will be calculated and rounded according to the correlation. Otherwise, the model will use the maximum RTI of fixed temperature and rate compensated heat detectors, 68 (m s)1/2 [31], and the maximum RTI of standard sprinklers, 350 (m s)1/2 [32]. The RTIs will produce activation delays of 55 s and 287 s. In the model they are rounded to 1.0 min and 5.0 min. According to a report of the National Fire Protection Association (NFPA) [33], when present and fires were large enough, smoke alarms overall operated in 85% of the fires, with battery-powered smoke alarms operating in 77% of the fires, and hardwired only smoke alarms operating in 90% of the fires. Since 50% of smoke alarms are battery-powered [33], the reliability of local alarms and smoke detectors are taken to be 75% and 90% in the model. An NFPA report based on the statistical data of the National Fire Incident Reporting System (NFIRS) showed that sprinklers in the fire area with a fire large enough to activate sprinklers operated 91% of the time at an effectiveness of 96% of the time, producing a combined performance of operating effectively of 88% [34]. The model takes 85% as the reliability of sprinklers. The reliabilities of heat detectors, the central alarm and voice alarm are assumed to be 0.9, the same as that of sprinklers without considering effectiveness, as no statistical or experimental data are available for these devices. Table 2 summarises the recommended input data for the performance of fire detection and alarm systems for the cases where no better data are available. These values can be changed through the input files. 3.2.3. Warning from other occupants (OO) When a fire occurs, occupants may be alerted by others who have perceived the fire. The probability of occupants in compartment i receiving warnings from occupants in all the other compartments is P oo;i ðtÞ ¼ P oo;1½i ðtÞ [ P oo;2½i ðtÞ [ ⋯ [ P oo;j½i ðtÞ [ ⋯ [ P oo;n½i ðtÞ;

j ai

ð5Þ

where Poo,j[i] is the probability of occupants in compartment i receiving warning from occupants in compartment j and is determined by P oo;j½i ðtÞ ¼ Roo;ji ðP wo;dp;j ðtÞ [ P wo;la;j ðtÞ [ P wo;oo;j½i ðtÞÞ

ð6Þ

where Roo,ji is the location relationship factor between compartments i and j, and Pwo,dp,j, Pwo,la,j, and Pwo,oo,j[i] are the probabilities of occupants in compartment j warning occupants in the other compartments due to direct perception, local alarm activating, and the warnings of occupants in other compartments excluding compartment i. The union of the probabilities in the bracket reflects the probability of occupants in compartment j warning occupants in other compartments due to perception originated from other compartments excluding compartment i. The calculation of these probabilities is described in Section 3.4. The warning signal transfer between the compartments is important for evacuation initiation, but no detailed research is

45

available for reference. In the present model, the warning signal transfer between compartments on the same floor of a building is assumed to be perfect and that between compartments on different floors of a building is assumed to decay rapidly according to the following equation Roo;ij ¼ 

1   2 1 þabs f loorðiÞ  f loorðjÞ

ð7Þ

where floor(i) means the floor number of compartment i. 3.2.4. Warning from the fire department (FD) When a fire occurs, the fire department can be notified and take actions. After arriving at the fire scene, firefighters can warn occupants in the building. The fire department can be notified automatically by the connection to the central alarm system of the building or called manually by occupants in the building. After the fire department is notified, firefighters will arrive on scene after a response time tfd of the fire department specified by the user. Thus, the probability of occupants in compartment i receiving warning from the fire department will be a union of the probability of the central alarm activating and the probabilities of occupants in the other compartments calling the fire department as follows: P f d;i ðtÞ ¼ P cf ;1½i ðt  t f d Þ [ P cf ;2½i ðt  t f d Þ [ ⋯ [ P cf ;j½i ðt t f d Þ [ ⋯ [ P cf ;n½i ðt t f d Þ [ δca  f d P ca;i ðt  t f d Þ; j a i

where δca  fd is the availability of the central alarm connection to the fire department, which is either 0 or 1, and Pcf, j[i] is the probability of occupants in compartment j calling the fire department due to warnings from occupants in the other compartments excluding compartment i. The calculation of the probability of occupants calling the fire department is described in Section 3.4. According to a report of the National Fire Data Center of the U.S. Fire Administration, nearly 50%, 75%, and 90% of the response times of fire departments were less than 5 min, 8 min, and 11 min, respectively [35]. The model recommends 11 min as the fire department response time if no better data is available. 3.3. Delay of interpretation process After receiving warnings, occupants will spend time to confirm the presence of the fire in the interpretation process. The interpretation delay refers to the necessary time occupants take to decide what is happening and what to do next, which will depend on the warning sources. If a warning source is very reliable, then it may be interpreted and may make occupants take action immediately. Otherwise, occupants may take more time to confirm before taking further actions. Interpretation delays due to different warnings are grouped into three levels: level I includes direct perception of fire cues and warnings from local alarms, sprinklers, the voice alarm, and fire department. This information can be verified locally, resulting in a short delay. Level II includes warnings from other occupants,

Table 2 Recommended input data for performance of fire detection and alarm systems for the cases where no better data are available. Device type

Activation condition

Activation delay (min)

Activation time range, (min)

Reliability

Local alarm Sprinklers Smoke detectors Heat detectors Central alarm

Temperature reaches T2 Temperature reaches T3 Temperature reaches T2 Temperature reaches T3 Detectors connected to central alarm activating or occupants pulling bar Central alarm activating

0 5.0 0 1.0 0.0

2.0 2.0 2.0 2.0 0.0

0.75 0.85 0.90 0.90 0.90

1.0

0.0

0.90

Voice alarm

ð8Þ

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Table 3 Interpretation levels and recommended values for corresponding delays for the cases where no better data are available. Interpretation level

Perception type

Delay time (min)

Level I

Direct perception of fire cues Warning from local alarm Warning from sprinklers Warning from voice alarm Warning from fire department Warning from other occupants Warning from central alarm

3

Level II Level III

P action;i ðtÞ ¼ P action;dp;i ðtÞ [ P action;la;i ðtÞ [ P action;sp;i ðtÞ [ P action;ca;i ðtÞ [ P action;va;i ðtÞ [ P action;oo;i ðtÞ [ P action;f d;i ðtÞ ð11Þ

4 5

which are more difficult to verify, and a longer delay is warranted. Level III includes warning from the central alarm. It is the most difficult to verify and takes the longest delay. The delay for interpretation level III is taken as 5 min based on a fire drill study of four mid-rise residential buildings which showed that about 90% occupants needed 5 min to start evacuation following hearing the central alarm [1], and a fire drill study of a 3-storey multi-purpose university building which showed that more than 4 min was necessary to start evacuation following alarm [36]. For level I and level II interpretation, 3 min and 4 min are recommended. Table 3 shows the interpretation levels and recommended values for related delays for the seven warning sources for the cases where no better data are available. These values can be changed through the input files. 3.4. Action process Once a perception is interpreted as the presence of a fire, the occupants may take actions before beginning evacuation. The occupants' actions considered in the model include pulling the fire alarm to activate the central alarm, warning other occupants, calling the fire department, and finally evacuating. In this model, it is assumed that evacuation will eventually occur after occupants perceive the fire. Without losing generality, a calculation method of actions due to a specific perception is P action;perception;i ðt þ t perception Þ ¼ P action;perception;i ðt þ t perception  ΔtÞ

 þ P action  perception P remain;i ðtÞP aw;i ðtÞI perception P perception;i ðtÞ   P perception;i ðt ΔtÞ

ð9Þ

where Paction,perception,i is the probability of occupants in compartment i taking a specific action due to a specific perception at a previous time step, Paction-perception is the probability of occupants taking a specific action due to a deterministic specific perception, Premain,i is the probability of occupants in compartment i remaining in their original location, Paw,i is the probability of occupants being awake, which is discussed in Section 3.5, and Iperception is the probability of interpretation of a perception. The above equation means that a perception probability increase at time t will result in an action probability increase at a delayed time tþtperception, where tperception is the interpretation delay time related to the perception shown in Table 3. A value of 0.9 is applied as the interpretation probabilities for the central alarm and voice alarm as the warnings from the devices are not from local, while the interpretation probabilities for all other warnings are taken as 1.0. The probability of occupants remaining in their original location is calculated from the probability that the occupants have started the evacuation process at last time step as follows: P remain;i ðtÞ ¼ 1  P ev;i ðt  ΔtÞ

From Eq. (9), the probability of an action can be calculated based on one perception. In the case that an action is caused by a few perceptions, the probability of the action will be the union of all probabilities of the action due to different warning sources. The calculation method can be expressed as

ð10Þ

This equation is applied to each possible action to calculate the probabilities of actions such as pulling the fire alarm, warning other occupants, calling the fire department, and evacuating. The effect of actions due to local warnings like direct perception, local alarms, and sprinklers can be directly calculated according to Eqs. (9) and (10). However, in the process of calculating the effect of actions due to other occupants and fire department’s warnings, care should be taken to avoid repetition. To predict the probability of occupants in compartment i receiving warnings from occupants in compartment j, the probability of occupants in compartment j warning other occupants should be based on the probabilities of occupants in compartment j receiving warnings from occupants in all the other compartments excluding compartment i. P wo;oo;j½i ðtÞ ¼ P wo;oo;j½i ðt  ΔtÞ þ P wo;oo P remain;j ðt  t oo ÞP aw;j ðt  t oo ÞI oo   P oo;j1 ðt  t oo Þ [ P oo;j2 ðt t oo Þ [ ⋯ [ P oo;jk ðt  t oo Þ [ ⋯ [ P oo;jn ðt  t oo Þ  P oo;j1 ðt  t oo  ΔtÞ [ P oo;j2 ðt t oo  ΔtÞ [ ⋯ [ P oo;jk ðt  t oo ΔtÞ  [ ⋯ [ P oo;jn ðt  t oo  ΔtÞ ; k a j; i

ð12Þ

where Poo,jk is the probability of occupants in compartment j receiving warning from occupants in compartment k and is described as   P oo;jk ðt  t OO Þ ¼ Roo;jk P wo;dp;k ðt  t oo Þ [ P wo;la;k ðt  t oo Þ ð13Þ While earlier warnings can be traced by including the probability of occupants in compartment k warning other occupants after they have been warned by other occupants in Eq. (13), the model does not do so to reduce computational time. Similarly, to predict the probability of occupants in compartment i receiving warning from the fire department, the probability of occupants in compartment j calling the fire department due to occupants in compartment i pulling the fire alarm and warning other occupants should be excluded P cf ;j½i ðtÞ ¼ P cf ;dp;j ðtÞ [ P cf ;la;j ðtÞ [ P cf ;sp;j ðtÞ [ P cf ;ca;j½i ðtÞ [ P cf ;oo;j½i ðtÞ

ð14Þ

where Pcf,ca,j[i] is the probability of occupants in compartment j calling the fire department due to receiving the central alarm originated from occupants in all the other compartments other than compartment i, and Pcf,oo,j[i] is the probability of occupants in compartment j calling the fire department due to receiving warnings from occupants in all the other compartments other than compartment i. The two terms can be calculated as follows:  P cf ;ca;j½i ðtÞ ¼ P cf ;ca;j½i ðt  ΔtÞ þ P cf  ca P remain;j ðtÞP aw;j ðtÞI ca P ca;j½i ðt  t ca Þ  P ca;j½i ðt  t ca  ΔtÞ ð15Þ P ca;j½i ðtÞ ¼ ½P pf ;1 ðtÞ [ P pf ;2 ðtÞ [ ⋯ [ P pf ;i  1 ðtÞ [ P pf ;i þ 1 ðtÞ [ ⋯ [ P pf ;j  1 ðtÞ [ P pf ;j þ 1 ðtÞ [ ⋯ [ P pf ;n ðtÞ [ P sp;1 ðtÞ [ P sp;2 ðtÞ [ ⋯ [ P sp;n ðtÞ [ P sd;1 ðtÞ [ P sd;2 ðtÞ [ ⋯ [ P sd;n ðtÞ [ P hd;1 ðtÞ [ P hd;2 ðtÞ [ ⋯ [ P hd;n ðtÞRca

ð16Þ

 P cf ;oo;j½i ðtÞ ¼ P cf ;oo;j½i ðt  ΔtÞ þP cf  oo P remain;j ðtÞP aw;j ðtÞI oo P oo;j½i ðt  t oo Þ  P oo;j½i ðt  t oo ΔtÞ ð17Þ

X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

Table 4 The probabilities of occupants taking actions due to deterministic perceptions. Warning sources

Pull fire alarm

Warn other occupants

Call fire department

Evacuate

Direct perception (State 0-1) Direct perception (State 1-2) Direct perception (State 2-4) Local alarm Sprinkler Central alarm Voice alarm Other occupants Fire department

0.3

0.3

0.3

1.0

0.3

0.3

0.3

1.0

0.6

0.6

0.6

1.0

0.3 N/A N/A N/A N/A N/A

0.3 N/A N/A N/A 0.3 N/A

0.3 0.6 0.3 N/A 0.3 N/A

1.0 1.0 1.0 1.0 1.0 1.0

Table 5 Assumptions in the model. Section Assumption 3 3.1 3.2.1

3.2.2.4

3.2.2.4

3.2.2.4

The probabilities of occupants taking actions due to deterministic perceptions are shown in Table 4. In the table, N/A means that the action has already been taken or needs not be taken. According to a statistical study interviewing 584 participants in 335 fires in the United States, notifying others, calling fire departments and pulling fire alarms were among the first three actions for 30.4%, 36.2%, and 2.0% of occupants in fire emergencies, respectively [37]. The low value of pulling fire alarms may be because alarms need activate once only. Based on these data, 0.3 is taken as the probabilities of occupants pulling the fire alarm, warning other occupants, and calling the fire department. Considering that these statistical data considered all occupants including those far from the compartment of fire origin, a higher value, 0.6, is used when the action is a result of direct perception in states 2 to 4 and sprinkler activating. These values can be changed through the input files.

3.5. Asleep occupants If occupants in compartment i are awake, the probability of the occupants being awake, Paw,i, is 1. Otherwise, the probability has to be calculated as occupants will have to be awakened by alarms or fire cues. The sources to awaken asleep occupants include local alarms, the central alarm, voice alarm, other occupants and fire department’s warnings, high temperature or smoke of the fire, and sprinkler discharging. The sound of alarms is generally strong enough to awaken occupants after sounding duration of taw. However, the intensity of temperature or smoke and the sound of sprinkler discharging may be weaker. Therefore, a longer duration will be necessary to awaken asleep occupants and the probability of the occupants being awake will be lower. Thus, the probability of occupants in compartment i being awake will be the union of these probabilities and is calculated as P aw;i ðtÞ ¼ P la;i ðt  t aw Þ [ P ca;i ðt  t aw Þ [ P va;i ðt  t aw Þ [ P oo;i ðt  t aw Þ [ P f d;i ðt  t aw Þ [ 0:5P dp;i ðt  2t aw Þ [ 0:2P sp;i ðt  2t aw Þ

ð18Þ

According to a summary of investigations of waking behaviour to fire alarms [38], 3 min is recommended as input for the duration occupants need to awake to alarms if no better data are available. However, those under the influence of sleep induced medication or drugs were more difficult to arouse to alarms [38]. As a result, longer duration should be used for population under these influences. No statistical data are available for occupants to awake to direct perception of fires and to sprinkler discharging. In this model, the duration occupants need to awake to the direct perception of a fire and to the sprinkler discharging is assumed to

47

3.2.3

3.4 3.5

Occupants in the same compartment have the same response probabilities. Any occupants remaining in a compartment will die if flashover occurs in the compartment. The probability density function of the direct perception of occupants in each compartment has a symmetrical triangular distribution and produces a cumulative probability which increases from 0 at t1 to 1 at t3 of their own compartment. Fire detectors, including local alarms, sprinklers, and smoke and heat detectors, activate within an activation time range rather than deterministically activate at their temperature ratings. The probability density functions of the activation of fire detectors have symmetrical triangular distributions with their activation times located at the center of the activation time ranges. The reliabilities of heat detectors, the central alarm and voice alarm are 0.9. The warning transfer between the compartments on the same floor of a building is perfect and that on different floors of a building decays rapidly. Evacuation will eventually occur after occupants perceive the fire. The duration occupants need to wake to the direct perception of a fire and to the sprinkler discharging is doubled and the probabilities of occupants waking to the two signals are 0.5 and 0.2.

be doubled and the probabilities of occupants waking to the two signals are assumed to be 0.5 and 0.2. 3.6. The effects of the number of occupants In the above sections, the calculation of action probabilities does not consider the effect of population number. Therefore, it can only be used in the case that a compartment has only one occupant. However, if a compartment is unattended, the action probabilities of occupants in the compartment will have to be replaced with zero. If a compartment has more than one occupant, the action probabilities will have to be replaced with the unions of the action probabilities of all occupants in the compartment. The calculation of the probability of direct perception and the probability of being awake also follows this rule. Assumptions used in the model are listed in Table 5.

4. Case study description The occupant response model was applied to simulate the occupant response of a six-storey, 83-compartment building during fire emergencies. Fig. 1 shows the plan of the first floor of the building. The other storeys have the same plan as the first floor except that a compartment on other floors replaces the lobby hall at the main entrance on the first floor. A representative occupancyspecific design fire scenario of typical fires for the occupancy [39], a medium developing t2 fire, is assumed to occur in the small compartment next to the elevator on the first floor. The door of the fire compartment is open during the entire fire development process and all interior doors are specified to have a 5% opening factor, defined as the ratio of leaking or opening area to door area. Each compartment of the building is filled with four occupants except the small compartments having two occupants. Totally 318 occupants are in the building when the fire compartment is unattended and 320 when the fire compartment has two occupants. The scenarios investigated include combinations of attended/ unattended fire compartment, asleep/awake occupants, local alarms/smoke detectors/no detector, and sprinklers/no sprinkler, as shown in Table 6. The central alarm is present and connected to smoke detectors and sprinklers.

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X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

12.0m

12.0m

7.0m

12.0m

12.0m

12.0m

Fire

7.5m

5.5m

5.0m

3.5m

2.5m 1.5m

7.5m

12.0m

Fig. 1. The plan of the first floor of the building.

Scenario symbol

LA þAFC þ AW LA þAFC þ AS LA þUFC þ AW LA þUFC þ AS AFC þ AW AFC þ AS UFC þ AW UFC þ AS SD þ AFC þ AW SD þ AFC þ AS SD þ UFC þ AW SD þ UFC þ AS SP þ AFC þAW SP þ AFC þAS SP þ UFC þ AW SP þ UFC þ AS LA þSP þ AFC þAW LA þSP þ AFC þAS LA þSP þ UFC þAW LA þSP þ UFC þAS SD þ SP þ AFCþ AW SD þ SP þ AFCþ AS SD þ SP þ UFC þAW SD þ SP þ UFC þAS

Local alarms (LA)/smoke detectors (SD)/no detector

Sprinklers (SP)/no sprinkler

LA LA LA LA No No No No SD SD SD SD No No No No LA LA LA LA SD SD SD SD

No No No No No No No No No No No No SP SP SP SP SP SP SP SP SP SP SP SP

Attended/ unattended fire compartment

Attended Attended Unattended Unattended Attended Attended Unattended Unattended Attended Attended Unattended Unattended Attended Attended Unattended Unattended Attended Attended Unattended Unattended Attended Attended Unattended Unattended

Awake/ asleep occupants

Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep Awake Asleep

200 180

Temperature (°C)

160

Upper Layer, No Sprinkler

140 120

Lower Layer, No Sprinkler

100 80

Upper Layer, with Sprinklers

60 40

Lower Layer, with Sprinklers

20 0

0

600

1200

1800

2400

Time (s) Fig. 2. The temperature development of the corridor on the first floor of the building.

5. Results and discussion Fig. 2 shows the temperature development in the corridor on the first floor of the building. The result is produced by the fire growth and smoke movement sub-model, which assumes that two uniform layers in each compartment and corridor are developed

Probabilities of Evacuation Initiation

1

Table 6 Scenarios.

0.9 0.8 0.7 0.6

LA+AFC+AW

0.5

LA+AFC+AS

0.4

LA+UFC+AW

0.3

LA+UFC+AS

0.2 0.1 0

0

600

1200

1800

2400

Time (s) Fig. 3. The probabilities of evacuation initiation for the scenarios with local alarms only.

during a fire. After 10 min following fire initiation, the lower layer and upper layer temperatures of the corridor on the first floor can reach up to 43 and 99 1C in the scenarios without sprinklers and 39 and 76 1C in the scenarios with sprinklers, due to smoke flowing from the fire compartment. The temperatures of the scenarios with sprinklers are significantly lower than those of the scenarios without sprinklers, demonstrating the efficiency of sprinklers in controlling the fire growth and extending the tenable time in fire emergencies. Figs. 3 to 8 show the probabilities of evacuation initiation for various scenarios. To reflect the difference of the probabilities of occupants in different compartments, these figures show both the minimum and maximum probabilities of evacuation initiation for each scenario. The probabilities of evacuation initiation of occupants in a specific compartment are in the range between the minimum and maximum values and depend on their location. Generally, occupants in the fire compartment have the maximum probabilities of evacuation initiation as they experience the stronger fire cues. If the fire compartment is unoccupied, the probabilities of evacuation initiation of occupants in different compartments show small or no differences in this case study, as all of them rely on the warning from the central alarm. The small differences, if any, are due to the effect of the different numbers of occupants in different compartments. Occupants in the compartment with more occupants will have stronger interactions between them and tend to have larger probabilities of evacuation initiation. In this case study, fire cues in compartments other than the fire compartment are too weak to be perceived or detected. Fig. 3 shows the probabilities of evacuation initiation for the scenarios with local alarms only (LAþ AFCþAW, LAþ AFCþAS, LAþUFCþ AW, and LAþUFCþAS). The scenarios with the attended fire compartment (LAþAFCþAW and LAþAFCþAS) have high probabilities of evacuation initiation while those with unattended fire compartment (LAþ UFCþAW and LAþUFCþ AS) have zero probabilities of evacuation initiation. The reason is that occupants in the fire compartment can perceive fire cues directly and be

X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

1 AFC+AW

0.8

AFC+AS

0.7

UFC+AW

0.6

UFC+AS

0.5 0.4 0.3 0.2 0.1

Probabilities of Evacuation Initiation

Probabilities of Evacuation Initiation

1 0.9

0

49

0.9 0.8 0.7 0.6

600

1200

1800

LA+SP+AFC+AS

0.4

LA+SP+UFC+AW

0.3

LA+SP+UFC+AS

0.2 0.1 0

0

LA+SP+AFC+AW

0.5

2400

0

600

1

1

0.9

0.9

0.8 0.7 0.6

SD+AFC+AW

0.5

SD+AFC+AS

0.4

SD+UFC+AW

0.3

SD+UFC+AS

0.2 0.1 0

600

1200

1800

2400

Fig. 5. The probabilities of evacuation initiation for the scenarios with only smoke detectors connected to the central alarm.

Probabilities of Evacuation Initiation

1 0.9 0.8 0.7 0.6

SP+AFC+AW

0.5

SP+AFC+AS

0.4

SP+UFC+AW

0.3

SP+UFC+AS

0.2 0.1 0

600

1200

2400

0.8 0.7 0.6

SD+SP+AFC+AW

0.5

SD+SP+AFC+AS

0.4

SD+SP+UFC+AW

0.3

SD+SP+UFC+AS

0.2 0.1 0

0

600

1200

1800

2400

Time (s)

Time (s)

0

1800

Fig. 7. The probabilities of evacuation initiation for the scenarios with both local alarms and sprinklers connected to the central alarm.

Probabilities of Evacuation Initiation

Probabilities of Evacuation Initiation

Fig. 4. The probabilities of evacuation initiation for the scenarios with no detector and sprinkler.

0

1200

Time (s)

Time (s)

1800

2400

Time (s) Fig. 6. The probabilities of evacuation initiation for the scenarios with only sprinklers connected to the central alarms.

warned by local alarms activating since fire cues in the fire compartment are strong, and then they can warn occupants in the other compartments. However, due to the small opening factors of the doors of the other compartments, the amount of smoke flowing into the other compartments is not enough to be perceived or to activate local alarms or smoke detectors. Meanwhile, the warnings of local alarms in the corridor of the first floor are limited to the corridor while the smoke in the corridor is strong enough to activate local alarms or smoke detectors. As a result, occupants in the scenarios with the unattended fire compartment (LAþ UFCþAW and LAþUFCþAS) cannot perceive fire cues and will not evacuate. For the scenarios with the attended fire compartment, the differences between the probabilities of evacuation initiation of the scenarios with awake (LAþAFCþAW) and asleep (LAþ AFCþ AS)

Fig. 8. The probabilities of evacuation initiation for the scenarios with both smoke detectors and sprinklers connected to the central alarm.

occupants reflect the awakening delay in addition to the lower probabilities of evacuation initiation of asleep occupants. The delay of the maximum probabilities of evacuation initiation is smaller than that of the minimum probabilities of evacuation initiation due to the fact that the former includes the delay to awaken occupants in the fire compartment only and the latter includes the delays to awaken occupants in both the fire compartment and in the other compartments. The longer response time and lower probabilities of evacuation initiation of asleep occupants occur also in other scenarios as shown in Figs. 4 to 8. This result, a reflection of experiments and observations, is in line with the statistical data which show that although only 19% of the reported home fires occurred at night, these fires cause 50% of the home fire deaths [40]. The differences between the maximum and the minimum probabilities of evacuation initiation for both awake and asleep occupants show the effects of different fire cues and warnings on the probabilities of evacuation initiation. For example, the maximum probabilities of evacuation initiation are a result of direct perception and local alarms activating, while the minimum probabilities of evacuation initiation are due to the central alarm activating, and other occupants and fire department’s warning. Fig. 4 shows the probabilities of evacuation initiation for the scenarios with no detector and sprinkler (AFC þAW, AFC þAS, UFCþ AW, and UFC þAS). A comparison between Figs. 3 and 4 shows that there is no difference between the probabilities of evacuation initiation for the scenarios with the unattended fire compartment (LA þUFCþ AW vs. UFC þAW and LAþ UFCþ AS vs. UFCþ AS) as local alarms activating cannot be perceived by the occupants in the other compartments if the fire compartment is unattended. However, for the scenarios with the attended fire compartment, the addition of local alarms significantly increases the probabilities of evacuation initiation and reduces the response

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X. Zhang et al. / Fire Safety Journal 68 (2014) 41–51

times of asleep occupants (LA þAFC þAS vs. AFC þAS), but slightly changes those of awake occupants (LA þAFC þ AW vs. AFC þAW). These results demonstrate the importance of local alarms in warning local asleep occupants. Fig. 5 shows the probabilities of evacuation initiation for scenarios with only smoke detectors connected to the central alarm (SD þAFC þAW, SD þAFC þ AS, SDþ UFCþAW, and SD þUFC þAS). A comparison between Figs. 5 and 3 shows that the replacement of local alarms with smoke detectors remarkably increases the probabilities of evacuation initiation and reduces the response times of occupants in the other compartments for all the four scenarios, although the probabilities of evacuation initiation and response times of occupants in the fire compartment evacuating do not change. This shows that smoke detectors connected to a central alarm are a preferred option as they alert occupants throughout the building. Fig. 6 shows the probabilities of evacuation initiation for the scenarios with only sprinklers connected to the central alarm (SP þAFC þAW, SPþ AFCþ AS, SPþUFC þAW, and SPþ UFCþAS). A comparison between Figs. 6 and 3 shows that for the scenarios with the attended fire compartment and awake occupants (SP þAFC þAW vs. LA þAFC þAW), the probabilities of evacuation initiation and response times change slightly, due to the strong direct perception of occupants in the fire compartment and their warnings. For the scenario with the attended fire compartment and asleep occupants (SP þAFC þAS), sprinklers lead to longer response times for occupants in the fire compartment due to their longer response time but similar probabilities of evacuation initiation and response times for occupants in the other compartments due to the similar response times of sprinklers and asleep occupants in the fire compartment in the scenario with local alarms and asleep occupants (LA þAFC þAS). However, for the scenarios with the unattended fire compartment (SP þUFCþ AW and SPþUFC þAS), sprinklers connected to the central alarm significantly increase the probabilities of evacuation initiation due to their global action area. A comparison between Figs. 6 and 5 shows much longer response times for the scenario with sprinklers than for the scenario with smoke detectors as a result of the much longer response time of sprinklers. Although this suggests that sprinklers are not as good as smoke detectors in detecting a fire, sprinklers will control or extinguish the fire and extend the time available for evacuation. Fig. 7 shows the probabilities of evacuation initiation for scenarios with both local alarms and sprinklers connected to the central alarm (LA þSP þAFC þAW, LAþ SPþAFC þAS, LA þSP þUFC þAW and LA þSPþ UFCþAS). A comparison between Figs. 7 and 3 shows that the addition of sprinklers to local alarms can increase the probabilities of evacuation initiation for the scenarios with the unattended fire compartment. However, its effect on the probabilities of evacuation initiation for the scenarios with the attended fire compartment is slight as the response of the occupants in the fire compartment dominates the result. A comparison between Figs. 7 and 6 shows that local alarms can affect only the response of the asleep occupants in the fire compartment. This again shows that the function of local alarms is limited to warning local asleep occupants. Fig. 8 shows the probabilities of evacuation initiation for the scenarios with both smoke detectors and sprinklers connected to the central alarm (SD þSP þAFC þAW, SDþ SPþAFC þ AS, SD þSPþ UFCþAW and SD þSP þUFCþ AS). Compared with the scenarios with only sprinklers connected to the central alarm shown in Fig. 6, the scenarios with both smoke detectors and sprinklers show much earlier response times for awake and asleep occupants in the other compartments for scenarios with the attended and unattended fire compartments and asleep occupants in the fire compartment. A comparison between Figs. 8 and 5

shows no improvement in the probabilities of evacuation initiation. However, the benefits of sprinklers in controlling fire spread still make the combination of smoke detectors and sprinklers an attractive option of fire safety systems. From the results discussed above, it is evident that the scenarios without fire detection and alarm systems or with only local alarms produce results with probabilities of evacuation initiation as low as zero after 40 min following fire initiation. Sprinklers connected to the central alarm need more than 15 min to warn occupants to evacuate and hence are not reliable to warn occupants to start evacuate. Smoke detectors connected to the central alarm can warn occupants within 10 min. A combination of smoke detectors and sprinklers connected to the central alarm can warn occupants within the same time limit while control fire spread. The results are in good agreement with the experimental results that sprinklers activate well after smoke alarms, and the suggestion that sprinkler installations should always include smoke alarms to provide earlier warning [41]. The specific results given in this Section are related to the fire and building scenarios used and are only valid for similar scenarios. They may not be generalised to cases which are completely different from the scenarios without assessment, as scenario change may lead to different results.

6. Conclusions The present paper has proposed a probabilistic occupant response model for fire emergencies and then integrated the model into a fire risk analysis model CUrisk. The response model takes input data related to building and fire protection system characteristics, occupant distribution, fire hazardous conditions, and fire department response. Based on the concept of PIA process, i.e., Perception, Interpretation and Action, the response model predicts the probabilities of occupants perceiving fire signals due to direct perception, receiving fire alarms due to the activation of local alarms, sprinklers, the central alarm and voice alarm, being warned by other occupants and the fire department, and taking actions including pulling the fire alarm, warning other occupants, calling the fire department, and evacuating to outside. The response model was applied to predict the probabilities of evacuation initiation of occupants in a number of scenarios with various combinations of fire detection and alarm systems for a mid-rise building. The results that asleep occupants need much longer response time to start evacuating and have lower probabilities of evacuation initiation than awake occupants, are in good agreement with the statistical data which show that 50% of the home fire deaths resulted from the 19% of the reported home fires that occurred at night. Additionally, the scenarios without fire detection and alarm systems, with only local alarms, and with only sprinklers connected to the central alarm have low occupant response probabilities and longer response times. The scenarios with smoke detectors connected to the central alarm, or with the combination of smoke detectors and sprinklers connected to the central alarm can warn occupants within significantly shorter times. Considering the benefits of sprinklers controlling fire spread, the combination of smoke detectors and sprinklers connected to the central alarm is a good choice for fires safety systems. This result agrees with the suggestion that sprinkler installations should always include smoke alarms to provide earlier warning. The results show that the model can produce critical characteristics of occupant response. Further validation and sensitivity analysis will undoubtedly make the model more reliable if necessary data are available as a result of more research in this area.

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Acknowledgements The financial support of NSERC and FPInnovations is gratefully appreciated. References [1] G. Proulx, Evacuation time and movement in apartment buildings, Fire Saf. J. 24 (1995) 229–246. [2] R.R.M. Gershon, L.A. Magda, H.E.M. Riley, M.F. Sherman, The World Trade Center evacuation study: Factors associated with initiation and length of time for evacuation, Fire Mater. 36 (2012) 481–500. [3] G. Proulx, Evacuation from a Single Family House, NRCC-51385, National Research Council Canada, 2009. [4] J.M. Watts Jr., Computer models for evacuation analysis, Fire Saf. J. 12 (1987) 237–245. [5] R. Friedman, An international survey of computer models for fire and smoke, J. Fire Prot. Eng. 4 (3) (1992) 81–92. [6] S. Gwynne, E.R. Galea, M. Owen, P.J. Lawrence, L. Filippidis, A review of the methodologies used in the computer simulation of evacuation from the built environment, Build. Environ. 34 (1999) 741–749. [7] S.M. Olenick, D.J. Carpenter, An updated international survey of computer models for fire and smoke, J. Fire Prot. Eng. 13 (2003) 87–110. [8] E.D. Kuligowski, R.D. Peacock, B.L. Hoskins, A Review of Building Evacuation Models, Second ed., Technical Note 1680, National Institute of Standards and Technology, 2010. [9] K. Takahashi, T. Tanaka, S. Kose, An evacuation model for use in fire safety designing of buildings, in: T. Wakamatsu, Y. Hasemi, A. Sekizawa, P.G. Seeger, P.J. Pagni, C.E. Grant (Eds.), Fire Safety Science –- Proceedings of the Second International Symposium, International Association for Fire Safety Science, 1988, pp. 551–560. [10] S. Okazaki, S. Matsushita, A study of simulation model for pedestrian movement with evacuation and queuing, in: R.A. Smith, J.F. Dickie (Eds.), Proceeding of the International Conference on Engineering for Crowd Safety, London, 1993, pp. 271–280. [11] B.M. Levin, EXITT: a simulation model of occupant decisions and actions in residential fires, in: T. Wakamatsu, Y. Hasemi, A. Sekizawa, P.G. Seeger, P.J. Pagni, C.E. Grant (Eds.), Fire Safety Science – Proceedings of the second International Symposium, International Association for Fire Safety Science, 1988, pp. 561–570. [12] M.J. Spearpoint, Comparative verification exercises on a probabilistic network model for building evacuation, J. Fire Sci. 27 (2009) 408–430. [13] R.F. Fahy, New developments in EXIT89, in: S.L. Bryner (Ed.), 15th Meeting of the UJNR Panel on Fire Research and Safety, vol. 1, National Institute of Science and Technology, 2000, pp. 153–159. [14] TraffGo HT GmbH, PedEd PedGo PedView AENEASed AENEASsim AENEASview User Manual Version 2.5.0, TraffGo HT GmbH, Duisburg, 2009. [15] T. Korhonen, S. Hostikka, Fire Dynamics Simulator with Evacuation: FDS þEvac, Technical Reference and User's Guide (FDS 5.5.0, Evac 2.2.1), VTT Technical Research Centre of Finland, 2010. [16] M. Bensilum, D.A. Purser, GridFlow: an object-oriented building evacuation model combining pre-movement and movement behaviours for performancebased design, in: D.D. Evans (Ed.), Fire Safety Science – Proceedings of the Seventh International Symposium, International Association for Fire Safety Science, 2003, pp. 941–953. [17] P. Thompson, J. Wu, E. Marchant, Simulex 3.0: modelling evacuation in multistorey buildings, in: Y. Hasemi (Ed.), Fire Safety Science – Proceedings of the Fifth International Symposium, International Association for Fire Safety Science, 1997, pp. 725–736. [18] J.P. Yuan, Z. Fang, Y.C. Wang, S.M. Lo, P. Wang, Integrated network approach of evacuation simulation for large complex buildings, Fire Saf. J. 44 (2009) 266–275.

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[19] J.N. Fraser-Mitchell, Modelling human behaviour within the fire risk assessment tool CRISP, Fire Mater. 23 (1999) 349–355. [20] L.S. Poon, EvacSim: a simulation model of occupants with behavioural attributes in emergency evacuation of high-rise buildings, in: T. Kashiwagi (Ed.), Fire Safety Science – Proceedings of the Fourth International Symposium, International Association for Fire Safety Science, 1994, pp. 681–692. [21] R.F. Fahy, G. Proulx, Toward creating a database on delay times to start evacuation and walking speeds for use in evacuation modelling, in: Second International Symposiumon Human Behaviour in Fire, Boston, 2001, pp. 175– 183. [22] G. Proulx, G. Hadjisophocleous, Occupant response model: a sub-model for the NRCC risk-cost assessment model, in: T. Kashiwagi (Ed.), Fire Safety Science – Proceedings of the Fourth International Symposium, International Association for Fire Safety Science, 1994, pp. 841–852. [23] D.W. Raboud, N. Benichou, A. Kashef, G. Proulx, G.V. Hadjisophocleous, FIERA system Occupant Response (OCRM) and Occupant Evaluation (OEVM) Models Theory Report, Research Report No. RR-100, National Research Council, Canada, 2002. [24] G. Hadjisophocleous, Z. Fu, Development and case study of a risk assessment model CUrisk for building fires, in: D.T. Gottuk, B.Y. Lattimer (Eds.), Fire Safety Science – Proceedings of the Eighth International Symposium, International Association for Fire Safety Science, 2005, pp. 877–887. [25] Z. Fu, G. Hadjisophocleous, A two-zone fire growth and smoke movement model for multi-compartment buildings, Fire Saf. J. 34 (2000) 257–285. [26] X. Zhang, G. Hadjisophocleous, An improved two-layer zone model applicable to both pre- and post-flashover fires, Fire Saf. J. 53 (2012) 63–71. [27] X. Zhang, X. Li, G. Hadjisophocleous, A probabilistic occupant evacuation model for fire emergencies using Monte Carlo methods, Fire Saf. J. 58 (2013) 15–24. [28] J.A. Geiman, D.T. Gottuk, Evaluation of smoke detector response estimation methods: optical density, temperature rise, and velocity at alarm, J. Fire Prot. Eng. 16 (2006) 251–268. [29] T. Cleary, Results from a full-scale smoke alarm sensitivity study, in: Suppression and Detection Research and Applications – A Technical Working Conference (SUPDET 2009), Fire Protection Research Foundation, 2009. [30] R.P. Fleming, Principles of automatic sprinkler system performance, in: A.E. Cote (Ed.), Fire Protection Handbook, National Fire Protection Association, 2008, pp. 16-3–16-14 (Section 16, Chapter 1). [31] Approval Standard for Heat Detectors for Automatic Fire Alarm Signaling, Class Number 3210, FM Approvals LLC, 2007. [32] D. Madrzykowski, R.P. Fleming, Residential sprinkler systems, in: A.E. Cote (Ed.), Fire Protection Handbook, National Fire Protection Association, Quincy, Massachusetts, 2008, pp. 16-91–16-107 (Section 16, Chapter 6). [33] M. Ahrens, Smoke Alarms in U.S. Home Fires, National Fire Protection Association, Quincy, Massachusetts, 2011. [34] J.R. Hall, Jr., U.S. Experience with Sprinklers, National Fire Protection Association, Quincy, Massachusetts, 2012. [35] U.S. Fire Administration, National Fire Data Center, Structure fire response times, Top. Fire Res. Ser. 5 (2006) 1–5. [36] S. Gwynne, E.R. Galea, J. Parke, J. Hickson, The collection and analysis of preevacuation times derived from evacuation trials and their application to evacuation modelling, Fire Technol. 39 (2003) 173–195. [37] J.L. Bryan, Smoke as a Determinant of Human Behavior in Fire Situations, NBS-GCR-77-94, U.S. Department of Commerce, National Bureau of Standards, Washington, D.C., 1977. [38] D. Bruck, The who, what, where and why of waking to fire alarms: a review, Fire Saf. J. 36 (2001) 623–639. [39] National Fire Protection Association, NFPA 101s Life Safety Codes, 2009 Edition, National Fire Protection Association. 2008. [40] M. Ahrens, Home Structure Fires, National Fire Protection Association, Quincy, Massachusetts, 2012. [41] R.W. Bukowski, R.D. Peacock, J.D. Averill, T.G. Cleary, N.P. Bryner, W.D. Walton, P.A. Reneke, E.D. Kuligowski, Performance of Home Smoke Alarms Analysis of the Response of Several Available Technologies in Residential Fire Settings, NIST Technical Note 1455-1, February 2008 Revision, 2008.