DSpace at VNU: Integration of Smoke Effect and Blind Evacuation Strategy (SEBES) within fire evacuation simulation

Integration of Smoke Effect and Blind Evacuation StrategySEBES within fire evacuation simulation Manh Hung Nguyena,b,⇑, Tuong Vinh Hoa, Jean-Daniel Zuckera,c a IRD, UMI 209, UMMISCO, IFI/

Integration of Smoke Effect and Blind Evacuation Strategy

(SEBES) within fire evacuation simulation

Manh Hung Nguyena,b,⇑, Tuong Vinh Hoa, Jean-Daniel Zuckera,c

a

IRD, UMI 209, UMMISCO, IFI/MSI, Vietnam National University of Hanoi, Viet Nam

b

Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Viet Nam

c

UPMC Univ Paris 06, UMI 209, UMMISCO, F-75005 Paris, France

a r t i c l e i n f o

Article history:

Received 6 August 2012

Received in revised form 16 February 2013

Accepted 2 April 2013

Available online 5 June 2013

Keywords:

Multi-agents system

Fire evacuation simulation

Evacuation strategy

Blind evacuation

Simulation modelling

a b s t r a c t

Many fire evacuation models have been proposed in recent years to better simulate such as

an emergency situation However most of them do not respect a recommendation of fire evacuation experts regarding the fact that evacuees should follow the boundaries of obsta-cles or wall to find the exits when their visibility is limited by smoke This paper presents

an agent-based evacuation model with Smoke Effect and Blind Evacuation Strategy (SEBES) which respects that recommendation by integrating a model of smoke diffusion and its effect on the evacuee’s visibility, speed, and evacuation strategy The implementation of this model enables us to optimise the evacuation strategies taking into account the level

of visibility The obtained simulation results on a realistic model of the Metro supermarket

of Hanoi confirm the important impact of smoke effect and blind evacuation strategy on the number of casualties

Ó 2013 Elsevier B.V All rights reserved

1 Introduction

Fire is increasingly a cause of casualties in modern life For instance, the Myojo 56 building fire in Tokyo (Japan) on Sep-tember 1st 2001 has killed 44 people and 291 people killed in Mesa Redonda shopping center fire in Lima (Peru) on Decem-ber 29th 2001 There were also 11 people who died in a fire at the detention center of Amsterdam Schiphol Airport (Netherlands) on October 27th 2005 The Moscow (Russian) hospital fire killed 46 people on December 9th 2006 The Santika Club fire in Bangkok (Thailand) killed 66 people on January 1st 2009 The ABC daycare center fire killed 47 people in Her-mosillo (Mexico) on June 5th 2009 The 2010 Dhaka fire was a fire in the city of Dhaka (Bangladesh) on 3rd June 2010 that killed at least 117 people And this list could infinitely grow up

The huge loss in these fires leads to at least two important questions: (1) Were people trained to practice the best strategy

to fire evacuate? and (2) Were the building designed with the best inside configuration regarding to fire evacuate? These two questions show common issues: how can we assess which strategy is best among the fire evacuation strategies? More spe-cifically, given a particular building which strategy is the best one? The real answer does not exist unless we could exper-iment in the real environment! One truthful approach is to rely on simulation environment modelling as close as the real world fire evacuation conditions

Once a fire evacuation simulation model is proposed, it has to comply with at least two modelling points of view First, from the point of view of fire evacuation experts, the model should take into account the smoke diffusion and its effect on the evacuation, the observable range, the evacuation speed, and the toxic poisoning level of evacuees In particular, it should

1569-190X/$ - see front matter Ó 2013 Elsevier B.V All rights reserved.

⇑ Corresponding author at: Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Viet Nam.

E-mail addresses: nmhufng@yahoo.com (M.H Nguyen), ho.tuong.vinh@auf.org (T.V Ho), jean-daniel.zucker@ird.fr (J.-D Zucker).

Contents lists available atSciVerse ScienceDirect

Simulation Modelling Practice and Theory

j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / s i m p a t

tool which could help fire evacuation training experts to visually demonstrate what evacuation strategy is better in a given environment Our contribution is thus three-folds:

 First, a proposal of a new agent-based model for fire evacuation simulation is given This model respects the recommen-dation of experts in fire evacuation by taking into account their recommenrecommen-dation that evacuees should follow the bound-aries of obstacles or wall to find the exits when their visibility is limited due to the smoke

 Second, an implementation of the proposed model based on an agent based integrated GIS support platform (GAMA[1]) supporting the development of an useful tool for two groups of users:

– The first group is experts in fire evacuation They could use this tool as a visual demonstration to illustrate what strat-egy is the best for evacuees to evacuate by applying all considered strategies into the model and run it, then compare the output parameters to see which is the best among them This could lead their evacuation training courses to be more intuitive and convince For instances, in the case study of the Metro supermarket of Hanoi, we compare three strategies of fire evacuation: following the evacuation signs, following the crowd, and following the own’s path when evacuees could observe still, and following the boundaries of obstacles and/or wall when their visibility is limited The simulation results show that following the evacuation signs is the best strategy in that situation

– The second group is building architects, constructors, interior designers, etc They could use this tool to choose the best internal configuration of a given building regarding the effect in fire evacuation by applying their different designs into this model and run it, then compare the output parameters to see which is the best configuration

This paper is organised as follows: Section2presents some related works in the field of crowd evacuation modelling and simulation Section3presents our agent-based model including a Smoke Effect and Blind Evacuation Strategy (SEBES) mod-ule for fire evacuation simulation Section4presents the application of our model to a real case study, including two types of scenario: scenarios comparing three bind evacuation strategies, and scenarios comparing three other evacuation strategies in normal condition Finally Section5presents a discussion of the simulation results and some conclusions as well as a discus-sion about future research

2 Related works

Recently, there has been an increasing number of models proposed for fire evacuation modelling in buildings.Table 1

summaries a partial collection of recent proposed agent models for fire evacuation We consider models at two levels:

 At the level of modelling, we consider the modelling of agent types involving a fire evacuation: the evacuees (eV – col-umn), the group or crowd of evacuees (g/c – colcol-umn), the fire (fi – colcol-umn), the alarm or voice system (a/v – colcol-umn), and the smoke (sm – column)

 At the level of optimisation, we consider the optimisation on the building design and the evacuation plan design (de – column), the optimisation on evacuation strategies in normal condition (visible evacuation strategy – v.e column), and that in limited visibility condition (blind evacuation strategy – b.e column)

More detail, let us analyse at the level of modelling Evacuee and fire are two objects modelled in most of the listed mod-els There is only a small number of models modelling the smoke[6,7,12,39] In these smoke models, the authors took into account the fact that smoke affects the visibility and speed of evacuee Furthermore, no model does respect a recommenda-tion of fire evacuarecommenda-tion experts on the fact that evacuees should follow the boundaries of obstacles or wall to find the exits when their visibility is limited by smoke

At the level of optimisation, there are many models built to choose the best floor designs or evacuation plan for a given building[3,5,6,10,29,30,35,40] There are also some models optimised evacuation strategies in normal (visible) condition

a better model of existing over of all aspects but to focus on smoke modelling and taking into account expert recommendations;

Our model will model many kinds of agent: evacuee, fire, alarm, smoke, etc in which the behaviour of evacuee is mod-elled based on a recommendation of fire evacuation experts on the fact that evacuees should follow the boundaries of obsta-cles or wall to find the exits when their visibility is limited by smoke This enables us to optimise on many aspects: optimise the evacuation plans, optimise the evacuation strategies in both conditions: visible and invisible

3 SEBES: an agent-based simulation model

This section presents our agent-based model including a Smoke Effect and Blind Evacuation Strategy (SEBES) module for fire evacuation simulation: Section3.1presents the general architecture of the model; Section3.2presents the modelling of evacuee agents; Section3.3presents the modelling of fire agents; Section3.4presents the modelling of smoke agents; Sec-tion3.5presents the modelling of fire alarm agents; and Section3.6presents the modelling of sign and plan agents 3.1 Architecture of the model

The architecture of our simulator could be seen at three levels as depicted inFig 1 At the platform level, the model is developed on the simulation platform GAMA[1] GAMA provides a simulation development environment for building spa-tially explicit agent-based simulations It enables: (i) to use arbitrarily complex GIS data as environments for the agents; (ii)

to run simulations composed of vast numbers of agents; (iii) to conduct automated controlled experiments on various sce-narios, with a systematic, guided or ‘‘intelligent’’ exploration of the space of parameters of models; and (iv) to let users inter-act with the agents in the course of the simulations

The second level is the simulator which relies on a multiagent system This is the core of our approach which includes the following types of agent:

 Evacuee agent: representing an evacuee This agent could see the fire/smoke, hear the alarm, and evacuate to one of the emergency exits by avoiding the obstacles and other evacuees

 Alarm agent: representing a fire alarm This agent could detect fire/smoke in its detection range and ring in a ringing dura-tion of time

 Fire agent: representing fire The fire agent could propagate within the building space

Table 1

Summary of recent proposed models (eV = evacuee, g/c = group or crowd, fi = fire, a/v = alarm or voice, sm = smoke, de = design, v.e = visible evacuation, b.e = blind evacuation).

Alavizadeh et al [2] U U U

Garca-Cabrera et al [4] U U U

Filippoupolitis et al [7,8] U U U

Helbing et al [11] U U U

Korhonen and Hostikka [16] U U U

Kuligowski et al [17,18] U U

Okaya and Takahashi [19,20] U U U

Patvichaichod et al [22,23] U U U

Qiu and Hu [24] U U

Ruppel et al [27,28] U U

Shen and Chien [32] U U

Shendarkar et al [33] U U U

Tingyong et al [36] U U

 Smoke agent: representing smoke The smoke agent is created from fire agents It could propagate inside the building space and therefore increase the smoke intensity at a give position by time

 Sign and plan agent: representing of evacuation signs and plan This is a non-movable agent This provides the information about the direction to emergency exits

The modelling of these agents will be presented in the next sections

The third level is the visualisation level This level supports displaying the realistic status of the simulation as well as the values of the output parameters

The details of the classes of the model are depicted inFig 2: all agent classes inherit from the species agent which is the highest in the hierarchy of agent in the GAMA language At the level of agent skills, the Evacuee agents are able to move, so they have a Moving skill Other agents have Situated skill

Fig 1 The three levels architecture of the model SEBES.

3.2 Evacuee agents

This agent represents an evacuee, he has the following attributes:

 Observable range: the space around an evacuee that can be observed and perceived by the evacuee An evacuee agent could only observe evacuation signs and/or other evacuees within this range

 Toxic level: the level of toxicity poisoning an evacuee This is initially as zero, and then increased due to the effect of smoke An agent is considered as to be died if his toxic level reaches 100%

 Fire exposure level: this represents the sensitive level in the fire of the evacuee The higher this value is, the more the evac-uee agent is affected by fire/smoke

 Speed: speed of an evacuee in evacuation This speed is changed according to the effect of visibility, and the toxicity level

 Passed position list: The time stamped list of positions that an evacuee agent has had during evacuation

The evacuee agent’s behaviours are presented inFig 3a: in a normal condition, the evacuee agent normally moves inside the building He starts to evacuate if and only if either he sees a fire/smoke or he hears the fire alarm’s ringing His evacuation movement is finished when he gets out of the building During his evacuation, he moves following the evacuation movement principle His observable range is reduced and the toxic level is increased by time due to the intensity of fire/smoke These principles are presented in Sections3.2.1 and 3.2.3

3.2.1 Evacuee movement principle

The fire evacuation experts of Hanoi Fire Evacuation Association have suggested us to respect the fire evacuation guide-lines when the evacuee meets obstacles: the evacuee should move along the border of the obstacle until the door (target) or there no more obstacle in front of the evacuee We take into account this principle of evacuee movement

We use priority direction approach for modelling of agent’s movement In this approach, agent chooses the direction hav-ing the highest priority to move Other directions will then be prioritised relatively to the one havhav-ing the highest priority There are two movement strategies: 4 directions or 8 directions (as depicted inFig 4) We use the 8 directions strategy in all simulations The more the direction is near the highest priority direction, the more the direction has high priority At each step, the agent considers the highest priority direction to move If it is not possible, the agent will consider the next lower priority direction, and so on A candidate direction is not considered if only if: either it is on an obstacle, or it leads to a posi-tion which is in the recent passed posiposi-tions list of the agent

In order to avoid the infinite loop of agent movement in the case having obstacles on the agent direction, we use a recent passed positions list which contains the n last positions of agent Agent thus considers the next position to move which is not

in its recent passed positions list We do not save all the passed positions of agent because the dynamic environments: some kind of obstacles, such as fire, can dynamically change There may be a fire at the position x at the moment t1, but may be no

more fire at x at the moment t2> t1 So we limit the size of the list to give agent a possibility to return to the positions which

it passed in long time

An agent determines its own recent movement tendency by considering m last positions (m < n, n is the size of recent passed positions list) Therefore, the priority direction is the arc from the m-latest position to the current position of agent.Fig 5

illustrates the movement principle of an agent when there is an obstacle on its evacuation way At the time t = t0, the agent has not meet the obstacle yet, so it continues to move to its target Next step, t = t0+ 1, the agent meets the obstacle, it finds its recent movement tendency which is still direct to target because the two latest positions are on the same line (m = 2) But the 1st, 2nd and 3rd direction are impossible (in the obstacle, case of 8 directions), so the 4th and 5th direction are possible Assume that the agent turns right At the time t = t0+ 2, the priority direction is the arc from the position at t = t0to the one at

t = t0+ 2 In this direction, the first four priorities are not possible, the 5th is possible (the blue arc), and so on At the time

t = t0+ 4, there is no more obstacles in front of the agent, so its priority direction is the direct line to its target

3.2.2 Blind movement principle

In a blind situation, an evacuee uses the same recent movement tendency principle to move, except that he does not know exactly where is the target Therefore, his movement is based on following rules (Fig 6):

 Blind movement rule 1: if there is not any obstacles or walls near him, the evacuee moves ahead (straight, right-straight, or left-straight)

 Blind movement rule 2: if there is some obstacles or walls in front of him, the evacuee changes its movement direction as follows:

– if the current direction is perpendiculars to the surface of an obstacle/wall, the new direction could be either right or left (Fig 6, case of ‘‘90 touch’’),

– if the current direction is not perpendicular to the surface of an obstacle/wall, the new direction will be the nearest direction to the current one which enables the evacuee to follow the obstacle/wall (Fig 6, case of ‘‘normal touch’’)

 Blind movement rule 3: if an evacuee is tracking an obstacle/wall, he continues to track until the end of the obstacle/wall

 Blind movement rule 4: at the end of an obstacle, the evacuee continues to move along the current movement direction

 Blind movement rule 5: at the ‘‘end’’ of a wall, the evacuee continues to follow the next face of the wall until he reaches an exit (Fig 6, case of ‘‘getting out of wall’’)

Note that in the blind movement rule 5, there could be an exception when the wall is a closed block in the building, the evacuee thus could repeat his movement around the wall forever Therefore, he could not get out of the building This

Fig 4 The two movement strategies based on 4 or 8 directions.

problem could be solved by using the proposed recent passed positions list technique: if the evacuee is aware that he is repeat-ing the evacuation path around a wall, he will change to the blind movement rule 4: at the end of the closed block wall, he continues to move as the current direction by considering the closed block wall as an obstacle

3.2.3 Toxic level evolution

If an evacuee agent is in the smoke, he is poisoned by toxic fumes The higher the smoke’s intensity at the agent’s position

is, the more it is poisoned Assume that pt

i and ptþ1

i are the toxicity poisoned of evacuee i at the time t and t + 1, we have:

ptþ1

iþ b  ðItðpÞ  hÞ < 0%

pt

iþ b  ðItðpÞ  hÞ if 0% 6 pt

iþ b  ðItðpÞ  hÞ 6 100%

iþ b  ðItðpÞ  hÞ

8

>

where b is the influence factor of smoke; It(p) is the intensity of smoke at the evacuee’s position p (of the agent i) at the time t; h is a safety smoke intensity threshold

Following Europe guideline (CFPA-E No 19:2009[41]), every inspiration has 16% of oxygen concentration An evacuee shows serious symptoms if the oxygen concentration is lower than 14% When the oxygen concentration decreases to 14%, the smoke in the air must be 100  14  100/16 = 12.5%.1Therefore the safety smoke intensity threshold is chosen in this model is h = 12.5% (Table 2) It means that if the smoke intensity is over 12.5%, the evacuee starts to be poisoned

Note that, following formula(1), the toxic level of an evacuee will be decreased if the evacuee enters in a zone having the smoke intensity lower than h And inversely, the toxic level will be increased if the evacuee enters in a zone having the smoke intensity higher than h The more the smoke intensity is high, the faster the toxic level is increased The evacuee will be sup-ported to be died if his or her toxic level is equal to 100%

Table 2 Simulation parameters (based on [41,43,44] ).

Number of simulations for each scenario 100 Number of evacuee agents 1000 Length of recent passed positions list 20 Influence factor of smoke (b) 0.01 Safety smoke intensity threshold (h) 12.5%

Fig 6 The blind movement principle.

1

In a normal condition: there are 16 l of oxygen in 100 l of normal air Assume that now there are x litres of smoke in 100 l or polluted air, it means that there are (100  x) litre(s) of normal air So the oxygen in these (100  x) litre(s) of normal air are 16⁄(100  x)/100 It takes less than 14% means that 16⁄(100  x)/

3.2.4 Evacuee observable range reduce principle

Like the evacuee’s power, the evacuee’s observable range is decreased if the evacuee is in the smoke The higher the inten-sity of the smoke at the position of the evacuee is, the less the evacuee is able to observe around it (this is well modelled in the model of Kang[42]) The relationship between the smoke’s intensity and the observable range of evacuee is depicted

i and rt

i are the observable range of evacuee i at the beginning (without any smoke) and at the time

t In order to keep the model as simple as possible, we use a function which states that the observable range is inversely proportional to the smoke intensity:

rt

i ¼ ð1  ItðpÞÞ  r0

where It(p) is the intensity of smoke at the evacuee’s position p (of the agent i) at the time t (normalised in the interval of [0, 1])

3.3 Fire agents

This agent represents fires The smoke agent is created from fire agents Fire agent could propagate

 Duration: its time to live Normally, the duration of a smoke is longer than that of a fire

 Propagation speed: the speed of propagation of the fire or the smoke This speed changes by time and by the quantity of fire agents in the building

 Affected zone: the space around a fire which can affect evacuees inside it

 Smoke creation speed: The smoke could not create other smoke, but fire could do it This attribute of fire defines the speed

to create smoke of a fire

The fire agent’s behaviours are presented inFig 3b: From its start, a fire burns until its ‘‘time to live’’ is equal to zero During its burning, a fire continues to create smoke with its smoke creation speed And the fire could also propagate by cre-ating other fire near by its position with its propagation speed

3.4 Smoke agents

This agent represents smoke The smoke agent is created from fire agents: (i) smoke, once being born from fire, is rela-tively independent from fire and (ii) smoke could move in some unpredictable directions: During moving around inside the building, smoke changes the smoke intensity at a given position by time We therefore model smoke as an agent

 Direction: The direction to propagate The direction is determined based on the following principle: the smoke moves from the position with higher smoke intensity to the position with lower smoke intensity

 Propagation speed: the speed of propagation of the smoke The speed of smoke is determined based on the following prin-ciple: the more the difference of smoke intensity at the two positions, the higher the speed of smoke

The smoke agent’s behaviours are presented inFig 3c: From its creation, a smoke updates its speed and direction at every simulation step After updating these two attributes, the smoke moves to the next position If the position is already outside

of the building, it dies Otherwise, it continues to update its attributes

3.5 Alarm agents

This agent represents a fire alarm with following attributes: Main properties:

 Ringing duration: the duration of ringing when fire/smoke was detected

 Detection range: this agent rings if there is fire or smoke appearing in this zone

Fig 7 Relation between smoke intensity and evacuee’s observable range and speed.

The alarm agent’s behaviours are presented inFig 3d: In a normal condition, the alarm agent does not ring It starts to ring if and only if either it detects the fire/smoke inside its detection range This ringing leads evacuee agents to evacuate It stops ringing when the ringing duration is over passed

3.6 Sign and plan agents

They represent the evacuation signs and plan They are non-movable agents They provide the information about the direction to emergency exits

4 Case study: simulation with the Metro supermarket of Hanoi

In this section, we apply the proposed model to simulate the fire evacuation in the supermarket Metro of Hanoi Our objective is to use simulation experiments to study what is the best evacuation strategy in the supermarket environment This section is organised as following: Section4.1presents the setting up of environment for simulations; Section4.2 vali-dates the model of smoke and blind evacuation; Section4.3validates the fire expert’s recommendation; Section4.4 opti-mises the evacuation strategies in normal condition

4.1 Simulation setup

This section presents the setting up of environment for simulations: Section4.1.1presents the setting up of the evacu-ation plans; Section4.1.2sets up simulation parameters; Section4.1.3presents analysis and evaluation criteria

4.1.1 The evacuation plan of Metro supermarket of Hanoi

The environment of simulation is a representation of GIS data as shown inFig 8 The Metro supermarket of Hanoi is sit-uated on one floor, with eight emergency exits: three on the front, two on the left, and three on the right In the simulation, the emergency exits are represented by red rectangles People can directly get to the left and right emergency exits from inside While in order to go to the front emergency exits, people have to pass two more gate layers: first, the cashiers layer with 12 main outputs, each is divided by two to have 24 cashiers in total; second, the security layer with four doors, and then the two main exits to the parking Another exit is the entrance which can use as an emergency exit

The inside configuration of Metro2supermarket can be decomposed into three main zones First, the left zone is the one for clothes and electronic materials There are two rows of shelves One is tall so people cannot look over to see the signs, therefore the evacuation signs are putted on the shelves One other is short so people can look over shelves to see the evacuation signs: there is only one common evacuation sign above them The evacuation signs in this zone indicate the direction to the two left emergency exits

Fig 8 The evacuation plan of the Metro supermarket of Hanoi.

2

In order to make the results comparable, we use the same values for input parameters of all simulations: steps of simu-lations; number of people; initial distribution These values are estimated based on the Europe guideline on Fire safety engi-neering concerning evacuation from buildings (CFPA-E No 19:2009[41]), the Human factors: Life safety strategies Occupant evacuation, behaviour and conditions (PD7974-6:2004[43]), and the Fire and Smoke: Understanding the Hazards of The Com-mittee on Fire Toxicology, Board on Environmental Studies and Toxicology, National Research Council[44] These parameters are shown inTable 2

4.1.3 Analysis and evaluation criteria

For each evacuation strategy, we run the simulations many times (100 times at least) with the same value of initial parameters: the number of people (N = 1000), and the speed of people At the output, we need to calculate the following parameters:

 Percentage of survivals The simulated environment is one floor building, so a person is considered as escaped if s/he passed one of emergency exits

 Percentage of death A person is considered as dead when his or her toxic level reaches 100%

 Average time for a person to be escaped It is the average time duration from the moment when s/he starts to evacuate until s/he escapes

 Average rate of toxic level of survivals

In comparing these parameters among strategies for each experiment, we will see which strategy of occupants is better in this realistic environment of the Metro supermarket of Hanoi A strategy is considered as better if three following observa-tions are true: (1) the % of survivals is higher (% of death is lower); (2) the average time to escape is shorter; and (3) the average rate of toxic poisoned of survivals is lower

4.2 Validation of smoke and blind evacuation model

The development of smoke during fire is presented in some snapshots of simulation interfaces (Fig 9) Following the time

of fire, the smoke increases the propagation space and intensity These are correspond to the results of the modelling of smoke in Section3.4

In order to indicate the effect of smoke on the movement of evacuees, at the visual level, we tracked the evacuation paths

of some evacuees in the condition of limited visibility due to smoke (Fig 10a) These evacuees are modelled with the evac-uation strategy recommended by fire evacevac-uation experts: tracking the walls or obstacles The results in this case show that in majority time of movement, evacuee really tracks the walls and/or obstacles