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Virtual drill for indoor fire evacuations considering occupant physical collisions
Automation in Construction 109 (2020) 102999
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Automation in Construction journal homepage: www.elsevier.com/locate/autcon
Virtual drill for indoor fire evacuations considering occupant physical collisions
T
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Zhen Xu , Wei Wei, Wei Jin, Qiao-rui Xue Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire evacuation Virtual drill Occupant physical collision Non-player characters Building information modeling
Occupant physical collisions (OPCs) influence indoor fire evacuations. To enable occupants to respond to such collisions safely, a virtual drill method for indoor fire evacuations considering OPCs is proposed. First, a modeling solution using the building information modeling technology and a smoke visualization algorithm based on fire computational fluid dynamics simulations are designed, respectively, to create a reasonable and realistic indoor fire scene. Subsequently, an algorithm for the evacuation animation of non-player characters (NPCs) combining evacuation simulations and skeletal animations is designed to provide a dynamic scene of multioccupant evacuations. Finally, a physical collision model between a trainee and NPCs is established based on a physics engine and the key parameters of this model are determined through real collision experiments of human subjects, so that the OPCs can be validly simulated. A case study of a virtual evacuation in a dormitory building demonstrates that the safest evacuation path differs significantly when OPCs are considered. The outcome of this study enables a trainee to experience OPCs in virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
1. Introduction Safe evacuation is an important issue for occupants when an indoor fire occurs. A virtual drill for indoor fire evacuations can allow occupants to experience realistic fire scenes and help them improve the ability of safe evacuations [1,2]. For example, Silva et al. [3] developed a virtual fire evacuation drill tool for healthcare professionals, by which the fire evacuation drills in hospitals and healthcare buildings were performed. The drill results indicated that the awareness and skill of trainees on the fire safety evacuation in complex indoor environments were improved. Currently, numerous research studies on a virtual drill for indoor fire evacuations have been conducted [4–13]. For instance, Cha et al. [11] proposed a real-time visualization method of smokes and flames to construct a dynamic fire scene for virtual evacuation drills. Xu et al. [10] established an integrated assessment model of smoke hazards to assess the safety of different virtual evacuation paths, which could allow trainees to learn to identify the safest path. In addition, some social influences on virtual fire evacuation drills were also investigated [5,12]. However, occupant physical collisions (OPCs) are rarely considered in the existing research on virtual fire evacuation drills. Currently, the
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collision avoidance behaviors of occupants have been mainly investigated [14]. Nevertheless, OPCs frequently occur during actual evacuations of indoor fires, which can affect the normal evacuation behaviors of occupants and decrease their evacuation efficiencies [14–16]. Therefore, as one of the key factors for indoor fire evacuations, OPCs need to be considered in virtual fire evacuation drills. To implement a virtual drill for indoor fire evacuations considering OPCs, the following three challenges need to be resolved: (1) Construction of a valid indoor fire scene. A valid indoor fire scene needs the support of fire computational fluid dynamics (CFD) simulations. Consistent building models are required to be created in a virtual scene and its CFD simulation, respectively, to ensure exact mapping between the simulation and scene. Concurrently, the dynamic processes of fire spreading need to be accurately replicated in the virtual scene using the results of the CFD simulation. Therefore, an integrated solution is required for constructing a valid fire scene. (2) Exhibition of the evacuation process of the non-player characters (NPCs)
Corresponding author. E-mail address: [email protected] (Z. Xu).
https://doi.org/10.1016/j.autcon.2019.102999 Received 6 August 2019; Received in revised form 20 September 2019; Accepted 17 October 2019 0926-5805/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Framework of this study.
animations to demonstrate an evacuation process with the motions of realistic characters on a VR platform. However, the method to combine evacuation simulations and skeletal animations for a reasonable and realistic evacuation of NPCs needs to be investigated. With respect to Challenge (3), physics engines provide a tool to simulate the OPCs between the trainees and NPCs. Physics engines are computer programs that specifically calculate the complex motions of objects with a real-time efficiency [29,30]; thus, they are used for collision simulations in virtual evacuation drills [31]. However, the existing research on physics engines focuses on the collisions between the trainees and components in buildings, rather than on the OPCs. Therefore, a particular physical model of the OPCs needs to be developed based on a physics engine. Currently, most of the well-known physics engines (e.g., Havok, Bullet, and PhysX) [32] are open-source codes, which facilitates the development of an OPC model. In addition, collision experiments are required to be performed to determine the critical parameters of a developed OPC model. A collision experiment is a conventional approach for vehicles to determine the key parameters of their collision models [33]. Similarly, collision experiments with human subjects can also determine the key parameters of an OPC model. Nevertheless, such tests are barely found in the existing literature, thus they need to be performed in this study. To address the above three challenges, a virtual drill method for indoor fire evacuations considering OPCs is proposed. First, a BIMbased modeling solution and a smoke visualization algorithm based on fire CFD simulations are designed, respectively, to create a valid virtual indoor fire scene. Subsequently, an algorithm based on the evacuation animation of the NPCs combining evacuation simulations and skeletal animations is designed to provide a dynamic scene of multi-occupant evacuations. Finally, a physical collision model between a trainee and the NPCs is established based on a physics engine and the key parameters of this model are determined by a real collision experiment of human subjects, so that the OPCs can be validly simulated. A case study of a virtual evacuation in a dormitory building indicates that the safest evacuation path alters significantly when the OPCs are considered. The outcome of this study enables a trainee to fully experience OPCs in the virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
NPCs constitute a multi-occupant evacuation environment for the trainees. Consequently, the evacuation process of the NPCs needs to be exhibited in the virtual drill. Note that such an evacuation process should be replicated by evacuation simulations to create a valid drill environment. In addition, a more realistic evacuation process of the NPCs is demanded for a better drill experience. Therefore, the method to exhibit the evacuation process of the NPCs deserves further studies. (3) Establishing an OPC model between the trainees and NPCs. To simulate the OPCs between the trainees and NPCs, physical models of the bodies, collision mechanic rules, and corresponding implementations are required. In addition, a solution to determine the key parameters of the model is necessary for an accurate simulation of the OPCs. To resolve Challenge (1), the building information modeling (BIM) technology and fire CFD simulations can be adopted. The BIM technology can rapidly generate component-level three-dimensional (3D) building models shared by fire simulations and virtual scenes [17,18]. These save repeated modeling works and maintain a correspondence between the simulations and scenes. For instance, Xu et al. [19] proposed a method for the conversion of a BIM model to a CFD model for fire simulations, whereas Silva et al. [3] imported a BIM model into a virtual reality (VR) platform for evacuation drills. The results of fire CFD simulations can also be used to reconstruct the dynamic process of fire spreading. For instance, Xu et al. [10] proposed a smoke visualization method based on volume rendering and CFD results. Therefore, a solution integrating BIM and fire CFD simulations can construct a valid indoor fire scene. To resolve Challenge (2), a solution combining evacuation simulations and skeletal animations can be used to present the evacuation process of the NPCs. Evacuation simulations can provide valid spatiotemporal evacuation paths for the NPCs. Currently, the methods [20–23] for evacuation simulations are well developed, and numerous computer codes [24,25] can validly simulate the evacuation process of occupants during indoor fires, e.g., the fire dynamics simulator (FDS) developed by the National Institute of Standards and Technology (NIST) of the United States [24]. By employing these codes, spatiotemporal evacuation paths can be obtained. In comparison, as a widely used technology for animating characters in the VR field, skeletal animations [26,27] can be adopted to represent the evacuation process of the NPCs. For instance, Tang and Ren [28] employed skeletal
2. Framework The framework of this study is shown in Fig. 1, which includes three 2
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Fig. 2. Scheme for visualizing smoke spreading by using the grids of particle systems.
Table 1 Decoded writing rules of the *.prt5 file for the evacuation paths. Time
Locations of occupants
T1 T2 … Tm
Current location of 1# occupant Current location of 1# occupant … Current location of 1# occupant
Current location of 2# occupant Current location of 2# occupant … Current location of 2# occupant
… … … …
Current location of n# occupant Current location of n# occupant … Current location of n# occupant
between the NPCs and trainees are also established. To determine the key parameters of the collision rules, a collision experiment on human subjects is performed. Accordingly, the OPCs between the NPCs and trainees can be validly simulated in the evacuation drills. Integrating the above three parts, a virtual drill for indoor fire evacuations considering OPCs can be implemented.
3. Method 3.1. Fire scene construction (1) BIM-based modeling solution As a widely used BIM program, Revit [34] is adopted to generate detailed 3D information models of buildings in this study. A wellknown graphics engine, Unity [35], is employed for scene construction. Unity can directly import only a model in an FBX format as a VR model, whereas Revit can also export the BIM model in the FBX format. Consequently, BIM models can be converted into VR models by using the FBX format. An internationally well-accepted fire simulation program developed by NIST, U.S., FDS [24], is adopted for fire simulations. To convert a BIM model in Revit to a CFD model in FDS, the geometric and material models of the building need to be considered because they determine the spatial constraints of a fire spreading and burning characteristics of the building components, respectively. For the geometric model, a preprocessing program of FDS, PyroSim [36], is employed because it can convert the FBX models exported by Revit into a 3D geometric model in FDS. Although such a geometric model converted by PyroSim retains the geometries and IDs of the building components, there is a lack of material information. Material information of the building components can be extracted from the BIM model by using the application programming interface (API) of Revit and be supplemented to the FDS model according to the IDs of the components. The technical details can be referred from the work of Xu et al. [19]. In addition to the above conversion solution based on Revit API, industry foundation classes (IFC), a platform neutral and widely used file format specification of BIM [37], also can be used for the conversion from BIM to FDS models [38–42]. Dimyadi et al. [42] proposed two IFC-based modeling approaches for FDS, which provide technical details for implementing the above conversion.
Fig. 3. Algorithm for extracting the evacuation path.
parts: (1) fire scene construction; (2) NPC evacuation; (3) OPC model. In the first part, a BIM model of a building is first created and converted to CFD and VR models, which ensures an exact correspondence of the two models. Subsequently, the process of a fire spreading is simulated to provide valid results for fire scene constructions. Finally, an algorithm for smoke visualization is designed to present a fire scene based on the simulation results and VR model. Accordingly, a wellfounded and realistic fire scene will be constructed. In the second part, first, an evacuation simulation is performed to obtain detailed evacuation paths for the NPCs. Subsequently, a decoding algorithm is designed to transform the binary evacuation paths into readable data for producing an evacuation animation. Finally, the skeletal animation technology is adopted to implement the evacuation animation of the NPCs in a virtual scene based on the decoded evacuation paths. In the third part, multi-rigid-body models of the trainees and NPCs are first created based on a physics engine. Then, the collision rules
(2) Smoke visualization In this study, to create a valid fire scene, the evolution of smoke in the virtual environment is visualized based on fire CFD simulations and particle system grids. In the fire CFD simulation, the space of the simulation is divided 3
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(a) elevation
(b) side elevation
(c) plan
(d) perspective drawing
Fig. 4. Multi-rigid-body models of a trainee.
Based on the above method, the particle systems in these grids are activated in order by the function, Smoke.StartDelay, to display the spreading process of a smoke.
Table 2 Geometrical parameters of the multi-rigid-body models of a trainee. Parts of body
Shape
Geometrical parameters
Head Main body Arms Legs
Sphere Box Capsule Capsule
Radius: 0.2 m Length: 0.4 m; Width: 0.3 m; Height: 0.8 m Height (including hands): 0.8 m; Radius: 0.1 m Height: 0.8 m; Radius: 0.1 m
3.2. Evacuation animation of NPCs (1) Evacuation path decoding To obtain reasonable evacuation paths of the NPCs, FDS is employed to perform an indoor fire evacuation simulation. This is because FDS can completely consider the fire effects (e.g., smoke hazards and low visibility) in the evacuation of occupants, and therefore, appropriate evacuation paths can be simulated. However, the data of the evacuation paths obtained by FDS cannot be directly read. To save storage space, FDS outputs the evacuation path in a binary file using the specific format of *.prt5. Such a file must be decoded so that the spatiotemporal paths can be extracted for the evacuation animation of the NPCs. Although FDS provides a decoding program based on MATLAB for *.prt5 files, it is only designed for the results of a sprinkler system and fails to decode the evacuation files. In this study, following much testing, the *.prt5 file of the evacuation path is successfully decoded, as shown in Table 1. The algorithm for extracting the evacuation path based on the above decoded writing rules is shown in Fig. 3. First, the MATLAB program provided by FDS is adopted to convert the *.prt5 file to the codes of American standard code for information interchange (ASCII). Subsequently, the key information of the evacuation, including the number of occupants (denoted as Num), evacuation time (denoted as T), and time
into discrete grids with specified thermo-physical properties, whereas the duration of the simulation is automatically divided into numerous time steps. Consequently, the soot densities in each grid at each time step are available [10]. In the VR scene, particles which are small and simple images or meshes are displayed and moved in large numbers by a particle system [43] to present the effects of a smoke and fire. Following the grids in the CFD simulation, the grids of the particle systems are created in the VR scene to visualize the spreading of a smoke [8]. In addition, the time step of updating a particle system is defined to be equal to that of the CFD simulation. Consequently, the particle systems in the VR scene have an exact spatiotemporal mapping to the grids in the CFD simulation. When the soot density of a gird is higher than zero at some time step, the particle system in this grid will be activated to present the effect of a smoke. For all the time steps, the particle system is continually activated for displaying the spreading process of the smoke, as shown in Fig. 2. In Unity, Prefab Smoke derived from Class ParticleSystems is used to create the particle systems of a smoke, and the function of Smoke.Transform can copy the particle systems to different grids [43]. 4
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1m
Trainee
NPC
Track
Distance sign Horizon
Camera
Fig. 7. Scheme of the collision experiment.
stored in a separated file for the evacuation animations. (2) Evacuation animation The implementation of the evacuation animation of the NPCs includes three key technical steps: a) 3D character modeling: creation of realistic 3D characters for the NPCs; b) Path animation: NPCs are moved following the paths; and c) Motion control: the motions are matched with the evacuation speeds. a) 3D character modeling In this study, the 3D characters are created by a skeleton and skinned mesh. The skeleton [26] is useful for the motion control, whereas the skinned mesh [44] can create realistic characters (e.g., workers, officers, and students). The skeleton and skinned mesh of a 3D character can be modeled by 3ds Max [45] and then they are exported to Unity using the FBX format for the following animations. Fig. 5. Local swing of an arm.
b) Path animation A Unity plug-in named iTween [43] can be employed to move the 3D characters of the NPCs according to the given evacuation path. iTween can automatically interpolate frames between adjacent evacuation locations, which results in a smooth animation. To implement the path animation, the evacuation path and 3D model of the NPCs are mapped by iTween, and the function of iTween.MoveTo() is called to perform the path animation of the NPCs. c) Motion control
(a) standing state
The motions (e.g., walking or running) of the NPCs should match the evacuation speeds. To switch the motions automatically, a blend tree [43] is adopted. In this tree, there are two states (i.e., walking and running), and a blend value (0–1) is used to switch the state. Generally, the mean walking speed of a normal adult is less than 1.5 m/s [46]; thus, the ratio of 1.5 m/s and the maximum speed in the evacuation simulation can be defined as the threshold. The blend value can define the ratio of the current speed and this maximum speed. Accordingly, if the blend value is smaller than the threshold, the motion state of the NPC is walking; otherwise, the motion state will be switched to running. The above blend tree can be defined by using Class Animator in Unity. Note that the motion control and path animation are individually performed in Unity so that an NPC can show compatible motions with its current speed while it moves following the given evacuation path.
(b) running state
Fig. 6. Body model for the NPCs.
step (denoted as T_s), can be read from the ASCII codes. Finally, the spatiotemporal path of each occupant is extracted according to the flow shown in Fig. 3. Specifically, the location of each occupant at each time step (i.e., evacuation path) can be read by the nested loops of the occupant IDs and time steps. The evacuation path of each occupant can be
3.3. OPC model (1) Body models for the trainees and NPCs
5
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(a) Recording area and distance signs
(b) Running
(c) Collision
(d) Backward movement of a trainee
Fig. 8. Typical collision process in the experiment (The left subject simulates a trainee, whereas the right subject simulates an NPC).
are summarized in Table 2. To allow the local movements of a body, Character Joint in PhysX [48] is adopted to connect the arms and legs. Taking arms for example, a rotation axis is first defined at the joint between the arm and main body. Then the corresponding allowable rotation range is added to the attributes of Character Joint, so that the arm can swing around the shoulder, as shown in Fig. 5.
Table 3 Calculated key parameters. Group
No.
mt
mn
vt1
vn1
vt2
s
CR
μ
A
1 2 3 4 1 2 3 4 1 2 3 4
70 70 70 70 55 55 55 55 60 60 60 60
68 68 68 68 72 72 72 72 72 72 72 72
1.75 1.19 1.25 1.63 2.00 2.00 1.94 1.62 2.10 1.25 1.25 1.94
−2.75 −1.57 −2.50 −2.94 −2.13 −2.38 −2.19 −1.93 −2.41 −2.00 −1.62 −2.19
−1.25 −1.02 −1.38 −1.69 −1.06 −1.06 −1.10 −0.92 −1.25 −1.12 −0.94 −0.81
1.66 1.22 1.10 2.07 1.22 1.22 1.31 1.11 1.22 1.13 0.75 0.75
0.35 0.63 0.42 0.47 0.31 0.23 0.30 0.26 0.36 0.34 0.40 0.22
0.05 0.04 0.09 0.07 0.05 0.05 0.05 0.04 0.07 0.06 0.06 0.04
B
C
b) NPCs Although the NPCs participate in the calculation of the OPCs, their evacuations, which are determined by the results of the evacuation simulation, are not affected by the OPCs. Consequently, there is no need to adopt complex models for the NPCs. In addition, the number of NPCs is large, which will lead to a decline in the interactive performance in a virtual evacuation if high-fidelity models are adopted. Therefore, in this study, simple capsules are employed as the boundary of the collision for the NPCs, as shown in Fig. 6. The size of the capsule is determined by the maximum bounding space of the skeleton at different movement states, as demonstrated in Fig. 6. Capsule Collider in Unity is adopted for the NPCs to calculate the OPCs with trainees, whereas Character Controller is also employed for the NPCs to keep fixed evacuation paths. Thus, the NPCs can participate in the calculation of the OPCs, but they do not use the results of the OPCs.
a) Trainees A high-fidelity model of the body is necessary to provide a better OPC experience for the trainees. However, in virtual evacuations, a high-fidelity body model may decrease the efficiency of the OPC calculation. Therefore, the balance between the fidelity and efficiency is a problem. In addition, the arms and legs of the trainees also have local swing movements when they move during evacuations. Such local movements need to be considered in the body models of the trainees because they may cause local collisions, and therefore, affect the evacuations. In this study, simple shapes are assembled to form a multi-rigidbody model for a trainee. This is because simple rigid bodies have a high computing efficiency for collisions. Specifically, a sphere and box are used to model the head and main body of a trainee, respectively, and four capsules are used for the arms and legs. For the OPC simulation of a trainee, the assembled rigid-body model has a detailed boundary, as shown in Fig. 4. Based on the existing literature [47], the sizes of these rigid bodies
(2) Collision rule The OPCs between the NPCs and trainees follow the rules of inelastic collisions. Assume mt and mn are the masses of a trainee and an NPC, respectively. The velocities of a trainee before and after the collision are denoted as vt1 and vt2, respectively, so that vt2 can be calculated by the following equation:
6
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(a) A group of running NPCs
(b) Collision with a trainee
(c) Physical models
(d) Backward trace
Fig. 9. Collision simulation between the NPCs and a trainee.
When a collision occurs, the trainee is subjected to an instantaneous force along the opposite moving direction, which may cause a backward movement. Such a backward movement is difficult to be simulated because it includes complex behaviors of the arms and legs. Consequently, an equivalent drag, which includes the friction and complex behavioral drag of the trainee, are introduced to simplify the simulation of the backward movement. Thus, the backward movement of a trainee is considered as a simplified process in which an object with an initial velocity slides under the equivalent drag until it becomes static. The backward distance (i.e., s) can be calculated by the following equation:
s=
vt22 2gμ
(2)
where μ is an equivalent friction coefficient, which can be used to calculate the influence on the movement caused by the equivalent drag. To implement the above rules in PhysX, the collisions between the NPCs and a trainee are first identified by the function, OnCollisionEnter (). Then the reverse velocity calculated by Eq. (1) can be subjected to the trainee by a function of setVelocity(). In addition, if the equivalent friction coefficient is defined in the attribution table of a rigid body, the backward distance in Eq. (2) will be calculated automatically by PhysX.
Fig. 10. BIM of the case study.
3.4. Collision experiments for parameter determination The equivalent friction coefficient, μ, and coefficient of restitution, CR, are key parameters in the above collision rules. In this study, the collision experiments of human subjects are performed to determine these two parameters. In the experiment, two subjects will collide face to face while they are running with different speeds. The entire process of the collisions will be recorded by video cameras. By the image measurement of the videos, the movement variables (e.g., velocity and distance) of the subjects can be obtained, and therefore, μ and CR will be calculated by these obtained variables.
Fig. 11. FDS model and the scenario of the fire evacuation simulation.
vt 2 =
CR mn (vn1 − vt1) + mt vt1 + mn vn1 mt + mn
(1)
(1) Principle
where CR is the coefficient of restitution.
In this experiment, the two subjects are assumed as a trainee and an 7
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(a) Escape in the room (25 s)
(b) Escape out of the rooms (50 s)
(3) Crowd in the corridor and exits (75 s)
(d) The end (125 s)
Fig. 12. Evacuation process output by the simulations.
the possible collision area for the subsequent image measurements. Based on the masses of the subjects, the experiment is classified into four groups, and four collisions with different velocities are performed in each group so that total 12 collisions are recorded by the video camera. The calculations for the distances and velocities of the subjects include two steps. First, a scale ratio k between the real distance and image distance is determined. In this experiment, the real distance of two adjacent signs is 1 m, whereas the corresponding image distance of these two signs is denoted as ds, which can be obtained by image measurements. Note that Adobe Premiere [49] is employed for the image measurements in the videos. Therefore, the ratio, k, can be calculated as expressed in Eq. (5).
k= Fig. 13. Drill traces of the three paths.
(mt + mn ) vt 2 − mt vt1 − mn vn1 mn (vn1 − vt1 )
(3)
The backward distance (i.e., s) of the trainee can be obtained by image measurements. Consequently, the equivalent friction coefficient, μ, can be calculated as the following equation.
μ=
vt22 2g s
(5)
Subsequently, the movement velocity of the subjects will also be calculated. The video is composed of numerous frames, and the time interval between two adjacent frames is Δt. Generally, Δt is an extremely small number (e.g., 1/100 or smaller) that depends on the scanning frequency of the video camera. If a subject is at location A in some frame and moves to location B in the next frame, the distance from A to B in the video will be measured (denoted as dAB). Consequently, the real velocity in this frame can be calculated by Eq. (6).
NPC, respectively. The masses of the trainee and NPC are denoted as mt and mn, respectively. The velocities of the trainee before and after the collision are vt1 and vt2, whereas that of the NPC before the collision is vn1. According to Eq. (1), CR can be calculated as shown in Eq. (3).
CR =
1 ds
v=
k∙dAB Δt
(6)
(3) Results (4) A typical collision process between a trainee and an NPC is shown in Fig. 8. From Fig. 8, the backward movement of the trainee caused by the collision is obviously observed, which indicates that the OPC between the NPC and trainee has a significant influence on the evacuations. The movement variables of the two subjects in the experiment are measured by the recorded video, and therefore, the key parameters can
(2) Experiment To measure the movement variables (e.g., velocity and distance), a scheme of the collision experiment is designed as presented in Fig. 7. Two subjects run along the track and will collide face to face in the recording range of the video camera. Distance signs are located within 8
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(a) Location A (Paths 1, 2, and 3)
(b) Location B (Path 1)
(c) Location C (Path 1)
(d) Location D (Path 1)
Fig. 14. Evacuation scenes at the key locations in the virtual drills.
evacuation simulations, BIM is converted into an FDS model by the proposed modeling solution, as shown in Fig. 11. In the fire evacuation simulation, the combustibles are two 2.0 m × 1.5 m mattresses composed of sponge and a knitted fabric (see Fig. 11), which undergo polyurethane reactions [50]. In this case, each room has two occupants, and all the occupants on the fifth story are assumed to be in the rooms when the building catches a fire. There are two exits (i.e., two stairs) on this floor; thus, all the occupants need to escape through these two exits. In the virtual evacuation drill, the trainee is located in a neighboring room of the ignition room (see Fig. 11). For the trainee, there are total three paths to exit. During the virtual drill, the NPC movements follow the results of the evacuation simulation, whereas the trainee is selfcontrolled. The NPCs possibly collide with the trainee, and therefore, in this case study, the influence on the evacuation of the trainee caused by such collisions (i.e., OPCs) will be mainly investigated.
be calculated by Eqs. (3) and (4), which are listed in Table 3. According to Table 3, the mean value and variance of the restitution coefficient, CR, are 0.36 and 0.08, respectively, whereas those of the equivalent friction coefficient, μ, are 0.05 and 0.01, respectively. In this study, the mean values of these two parameters are adopted. (4) Simulation implementation Based on the proposed OPC model and determined key parameters, a collision between a group of NPCs and a trainee is simulated, as shown in Fig. 9. In this simulation, the group of NPCs and a trainee run toward each other for collision. Because the designed evacuation animation methods of the NPCs are adopted, the NPCs have detailed 3D characters with appropriate running motions, which make the entire collision process extremely realistic (Fig. 9(a) and (b)). In addition, it can be observed from Fig. 9(c) that a high-fidelity physical model is adopted for the trainee to simulate the complex collision with the NPCs. In addition, the key parameters (i.e., CR and μ) are determined by the collision experiment, which ensures the rationality of the collision simulation. The trainee has an obvious backward distance after the collision (see Fig. 9(d)). Because the collision between the trainee and NPCs is at an angle, the backward distance is also at an angle, which indicates that the proposed collision rules are reasonable.
(2) Fire evacuation simulation The entire fire evacuation process is simulated based on the CFD model constructed by the proposed modeling solution, as presented in Fig. 12. The results of the smoke spreading and evacuation paths are output for the subsequent fire scene and animation of the NPCs. (3) Virtual evacuation drill considering OPCs
4. Case study
The smoke scene and animation of the NPCs are implemented using the proposed methods based on the above simulation results. Consequently, a reasonable and realistic evacuation scene is created for the virtual drills. Furthermore, the proposed OPC model is built for the trainee and NPCs. As shown in Fig. 11, there are three evacuation paths from the original location to the exits. To determine the safest path, the trainee
(1) Introduction of case study A six-story dormitory is selected as a case study, and its BIM is shown in Fig. 10. The total height and built-up area of this building are 21.60 m and 9600 m2, respectively. The fifth floor of the case study is selected to simulate the fire evacuation of the occupants. For the fire 9
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( e ) L o c a t io n E (P at h 2 )
(f) Location F (Path 2)
(g) Location G (Path 2)
(h) Location H (Path 3)
(i) Location I (Path 3)
(j) Location J (Path 3) Fig. 14. (continued)
(4) Safest path
performs three different drills on these three paths, respectively. The evacuation traces for these three paths are shown in Fig. 13. The corresponding scenes at the key locations (see Fig. 13) of the above three paths are shown in Fig. 14. For path 1, it can be observed that there are circuitous traces near location B. This is because the trainee is forced to move in reverse by the head-on collisions with the group of NPCs, as shown in Fig. 14(b). Contrastingly, the traces of the trainee near locations C and D are extremely smooth because there is no collision with the NPCs (see Fig. 14(c) and (d)). Similarly, the trace of path 2 from location F to G is wavy, which is also caused by the OPCs, as shown in Figs. 14(f) and (g). The trace of path 3 indicates that the trainee moves long distances for turning near locations H and I. As shown in Figs. 14(h) and (i), numerous NPCs are crowding at locations H and I when the trainee attempts to turn, and the OPCs make the turns of the trainee difficult. In summary, the traces of the above three paths indicate that the OPCs have a significant influence on the evacuation of the trainee.
In this study, to assess the safety of the different evacuation paths, the integrated hazard dose (IHD) [10] is adopted. The IHD can assess the overall hazard due to toxic gases and heats of indoor fires using a value from 0 to 1. A high IHD implies a severe hazard. When the IHD is equal to 1.0, the hazard is fatal. In a virtual evacuation, the IHD along different paths (i.e., IHDpath) can be calculated as the following equation: path path IHD path = max (FED6‐ Gas , FEDheat )
(7)
where FED6‐Gaspath and FEDheatpath mean the hazard indexes (i.e., fractional effective dose (FED) [10]) caused by toxic gases and heats, respectively. FEDheatpath is calculated by the radiant flux and temperature, whereas FED6‐Gaspath is calculated by the atmospheric concentrations of 6 gases (i.e. CO, CO2, HCN, O2, HCl and HBr) that commonly appear in smoke. The required calculation parameters can be obtained from the result of the fire CFD simulation, and the calculation details can be 10
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The comparison between Figs. 15 and 16 reveals that the average evacuation time and IHDpath values of the three paths without the NPCs are shorter and smaller, respectively, than those with the NPCs. This indicates that the OPCs significantly decrease the efficiency and safety of the evacuations. Furthermore, it can be observed that according to the IHDpath values in Fig. 16, path 1 is the safest path, which is different from the case with the NPCs. This indicates the OPCs can alter the safest evacuation path. Therefore, the decision-making for the safest evacuation path is quite different when the OPCs are considered. 5. Conclusions In this study, a virtual drill method for indoor fire evacuations considering OPCs is proposed, and a case study of virtual evacuation drills conducted in a six-story dormitory building is investigated. Some conclusions can be drawn as follows: (1) A reasonable and realistic scene of multi-occupant fire evacuations can be constructed by using the designed BIM-based modeling solution, smoke visualization algorithm and NPC animation algorithm. (2) The OPCs in the evacuation drills can be validly simulated by using the established physical model and the corresponding key parameters determined by a real collision experiment of human subjects. (3) The comparison of virtual drills with and without the NPCs indicates that the OPCs significantly decrease the efficiency and safety of the evacuations. Furthermore, the OPCs can alter the safest evacuation path, which influences the decision of choosing the safest path. (4) The proposed method enables trainees to fully experience OPCs in virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
Fig. 15. Integrated hazards and evacuation times of the three paths considering the OPCs.
Note that the designed solution to constructing the scene of multioccupant fire evacuations in this study is based on the widely-used FDS platform. Although the technical details are different if other fire CFD simulation platforms are adopted, the scene construction framework in this study still works. In the future, the proposed virtual drill method will be applied on the web or mobile devices, so that people can conveniently use such method to improve their ability of fire evacuation considering OPCs. Meanwhile, the evacuation paths in the drills will be also collected to analyze the evacuation features and thus teach occupants to evacuate safely based on these features.
Fig. 16. Integrated hazards and evacuation times of the three paths without the OPCs.
referred from the work of Xu et al. [10]. The IHDpath in the evacuation process of the above three paths is calculated, as shown in Fig. 15. It can be observed that the evacuation times of path 1 and path 3 are much longer than that of path 2. This is because head-on collisions with the NPCs are frequent on these two paths (see the corresponding traces and scenes in Figs. 13 and 14). By contrast, the OPCs on path 2 are fewer than those on the other paths; thus, the evacuation time of path 2 is the shortest. Furthermore, owing to such a long exposure time to smoke hazard, the IHDpath values of path 1 and path 3 are also much larger than that of path 2. Therefore, in this multi-occupant evacuation scenario considering the OPCs, path 2 is the safest path.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors are grateful for the financial support received from the National Key Research & Development Program of China (No. 2018YFC0809900), the National Natural Science Foundation of China (No. 51978049), Beijing Municipal Science and Technology Project (No. Z161100001116104), and the Fundamental Research Funds for the Central Universities (No. FRF-BD-18-007A).
(5) Comparison with a non-NPC scenario To further assess the influence of the OPCs on the safest path, a virtual evacuation drill is performed in absence of the NPCs. Note that the fire scenario is the same as that of the drill with the NPCs. In this non-NPC scenario, virtual evacuation drills of the three paths are performed, and the corresponding IHDpath values are calculated, as shown in Fig. 16.
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Virtual drill for indoor fire evacuations considering occupant physical collisions
T
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Zhen Xu , Wei Wei, Wei Jin, Qiao-rui Xue Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire evacuation Virtual drill Occupant physical collision Non-player characters Building information modeling
Occupant physical collisions (OPCs) influence indoor fire evacuations. To enable occupants to respond to such collisions safely, a virtual drill method for indoor fire evacuations considering OPCs is proposed. First, a modeling solution using the building information modeling technology and a smoke visualization algorithm based on fire computational fluid dynamics simulations are designed, respectively, to create a reasonable and realistic indoor fire scene. Subsequently, an algorithm for the evacuation animation of non-player characters (NPCs) combining evacuation simulations and skeletal animations is designed to provide a dynamic scene of multioccupant evacuations. Finally, a physical collision model between a trainee and NPCs is established based on a physics engine and the key parameters of this model are determined through real collision experiments of human subjects, so that the OPCs can be validly simulated. A case study of a virtual evacuation in a dormitory building demonstrates that the safest evacuation path differs significantly when OPCs are considered. The outcome of this study enables a trainee to experience OPCs in virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
1. Introduction Safe evacuation is an important issue for occupants when an indoor fire occurs. A virtual drill for indoor fire evacuations can allow occupants to experience realistic fire scenes and help them improve the ability of safe evacuations [1,2]. For example, Silva et al. [3] developed a virtual fire evacuation drill tool for healthcare professionals, by which the fire evacuation drills in hospitals and healthcare buildings were performed. The drill results indicated that the awareness and skill of trainees on the fire safety evacuation in complex indoor environments were improved. Currently, numerous research studies on a virtual drill for indoor fire evacuations have been conducted [4–13]. For instance, Cha et al. [11] proposed a real-time visualization method of smokes and flames to construct a dynamic fire scene for virtual evacuation drills. Xu et al. [10] established an integrated assessment model of smoke hazards to assess the safety of different virtual evacuation paths, which could allow trainees to learn to identify the safest path. In addition, some social influences on virtual fire evacuation drills were also investigated [5,12]. However, occupant physical collisions (OPCs) are rarely considered in the existing research on virtual fire evacuation drills. Currently, the
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collision avoidance behaviors of occupants have been mainly investigated [14]. Nevertheless, OPCs frequently occur during actual evacuations of indoor fires, which can affect the normal evacuation behaviors of occupants and decrease their evacuation efficiencies [14–16]. Therefore, as one of the key factors for indoor fire evacuations, OPCs need to be considered in virtual fire evacuation drills. To implement a virtual drill for indoor fire evacuations considering OPCs, the following three challenges need to be resolved: (1) Construction of a valid indoor fire scene. A valid indoor fire scene needs the support of fire computational fluid dynamics (CFD) simulations. Consistent building models are required to be created in a virtual scene and its CFD simulation, respectively, to ensure exact mapping between the simulation and scene. Concurrently, the dynamic processes of fire spreading need to be accurately replicated in the virtual scene using the results of the CFD simulation. Therefore, an integrated solution is required for constructing a valid fire scene. (2) Exhibition of the evacuation process of the non-player characters (NPCs)
Corresponding author. E-mail address: [email protected] (Z. Xu).
https://doi.org/10.1016/j.autcon.2019.102999 Received 6 August 2019; Received in revised form 20 September 2019; Accepted 17 October 2019 0926-5805/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Framework of this study.
animations to demonstrate an evacuation process with the motions of realistic characters on a VR platform. However, the method to combine evacuation simulations and skeletal animations for a reasonable and realistic evacuation of NPCs needs to be investigated. With respect to Challenge (3), physics engines provide a tool to simulate the OPCs between the trainees and NPCs. Physics engines are computer programs that specifically calculate the complex motions of objects with a real-time efficiency [29,30]; thus, they are used for collision simulations in virtual evacuation drills [31]. However, the existing research on physics engines focuses on the collisions between the trainees and components in buildings, rather than on the OPCs. Therefore, a particular physical model of the OPCs needs to be developed based on a physics engine. Currently, most of the well-known physics engines (e.g., Havok, Bullet, and PhysX) [32] are open-source codes, which facilitates the development of an OPC model. In addition, collision experiments are required to be performed to determine the critical parameters of a developed OPC model. A collision experiment is a conventional approach for vehicles to determine the key parameters of their collision models [33]. Similarly, collision experiments with human subjects can also determine the key parameters of an OPC model. Nevertheless, such tests are barely found in the existing literature, thus they need to be performed in this study. To address the above three challenges, a virtual drill method for indoor fire evacuations considering OPCs is proposed. First, a BIMbased modeling solution and a smoke visualization algorithm based on fire CFD simulations are designed, respectively, to create a valid virtual indoor fire scene. Subsequently, an algorithm based on the evacuation animation of the NPCs combining evacuation simulations and skeletal animations is designed to provide a dynamic scene of multi-occupant evacuations. Finally, a physical collision model between a trainee and the NPCs is established based on a physics engine and the key parameters of this model are determined by a real collision experiment of human subjects, so that the OPCs can be validly simulated. A case study of a virtual evacuation in a dormitory building indicates that the safest evacuation path alters significantly when the OPCs are considered. The outcome of this study enables a trainee to fully experience OPCs in the virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
NPCs constitute a multi-occupant evacuation environment for the trainees. Consequently, the evacuation process of the NPCs needs to be exhibited in the virtual drill. Note that such an evacuation process should be replicated by evacuation simulations to create a valid drill environment. In addition, a more realistic evacuation process of the NPCs is demanded for a better drill experience. Therefore, the method to exhibit the evacuation process of the NPCs deserves further studies. (3) Establishing an OPC model between the trainees and NPCs. To simulate the OPCs between the trainees and NPCs, physical models of the bodies, collision mechanic rules, and corresponding implementations are required. In addition, a solution to determine the key parameters of the model is necessary for an accurate simulation of the OPCs. To resolve Challenge (1), the building information modeling (BIM) technology and fire CFD simulations can be adopted. The BIM technology can rapidly generate component-level three-dimensional (3D) building models shared by fire simulations and virtual scenes [17,18]. These save repeated modeling works and maintain a correspondence between the simulations and scenes. For instance, Xu et al. [19] proposed a method for the conversion of a BIM model to a CFD model for fire simulations, whereas Silva et al. [3] imported a BIM model into a virtual reality (VR) platform for evacuation drills. The results of fire CFD simulations can also be used to reconstruct the dynamic process of fire spreading. For instance, Xu et al. [10] proposed a smoke visualization method based on volume rendering and CFD results. Therefore, a solution integrating BIM and fire CFD simulations can construct a valid indoor fire scene. To resolve Challenge (2), a solution combining evacuation simulations and skeletal animations can be used to present the evacuation process of the NPCs. Evacuation simulations can provide valid spatiotemporal evacuation paths for the NPCs. Currently, the methods [20–23] for evacuation simulations are well developed, and numerous computer codes [24,25] can validly simulate the evacuation process of occupants during indoor fires, e.g., the fire dynamics simulator (FDS) developed by the National Institute of Standards and Technology (NIST) of the United States [24]. By employing these codes, spatiotemporal evacuation paths can be obtained. In comparison, as a widely used technology for animating characters in the VR field, skeletal animations [26,27] can be adopted to represent the evacuation process of the NPCs. For instance, Tang and Ren [28] employed skeletal
2. Framework The framework of this study is shown in Fig. 1, which includes three 2
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Fig. 2. Scheme for visualizing smoke spreading by using the grids of particle systems.
Table 1 Decoded writing rules of the *.prt5 file for the evacuation paths. Time
Locations of occupants
T1 T2 … Tm
Current location of 1# occupant Current location of 1# occupant … Current location of 1# occupant
Current location of 2# occupant Current location of 2# occupant … Current location of 2# occupant
… … … …
Current location of n# occupant Current location of n# occupant … Current location of n# occupant
between the NPCs and trainees are also established. To determine the key parameters of the collision rules, a collision experiment on human subjects is performed. Accordingly, the OPCs between the NPCs and trainees can be validly simulated in the evacuation drills. Integrating the above three parts, a virtual drill for indoor fire evacuations considering OPCs can be implemented.
3. Method 3.1. Fire scene construction (1) BIM-based modeling solution As a widely used BIM program, Revit [34] is adopted to generate detailed 3D information models of buildings in this study. A wellknown graphics engine, Unity [35], is employed for scene construction. Unity can directly import only a model in an FBX format as a VR model, whereas Revit can also export the BIM model in the FBX format. Consequently, BIM models can be converted into VR models by using the FBX format. An internationally well-accepted fire simulation program developed by NIST, U.S., FDS [24], is adopted for fire simulations. To convert a BIM model in Revit to a CFD model in FDS, the geometric and material models of the building need to be considered because they determine the spatial constraints of a fire spreading and burning characteristics of the building components, respectively. For the geometric model, a preprocessing program of FDS, PyroSim [36], is employed because it can convert the FBX models exported by Revit into a 3D geometric model in FDS. Although such a geometric model converted by PyroSim retains the geometries and IDs of the building components, there is a lack of material information. Material information of the building components can be extracted from the BIM model by using the application programming interface (API) of Revit and be supplemented to the FDS model according to the IDs of the components. The technical details can be referred from the work of Xu et al. [19]. In addition to the above conversion solution based on Revit API, industry foundation classes (IFC), a platform neutral and widely used file format specification of BIM [37], also can be used for the conversion from BIM to FDS models [38–42]. Dimyadi et al. [42] proposed two IFC-based modeling approaches for FDS, which provide technical details for implementing the above conversion.
Fig. 3. Algorithm for extracting the evacuation path.
parts: (1) fire scene construction; (2) NPC evacuation; (3) OPC model. In the first part, a BIM model of a building is first created and converted to CFD and VR models, which ensures an exact correspondence of the two models. Subsequently, the process of a fire spreading is simulated to provide valid results for fire scene constructions. Finally, an algorithm for smoke visualization is designed to present a fire scene based on the simulation results and VR model. Accordingly, a wellfounded and realistic fire scene will be constructed. In the second part, first, an evacuation simulation is performed to obtain detailed evacuation paths for the NPCs. Subsequently, a decoding algorithm is designed to transform the binary evacuation paths into readable data for producing an evacuation animation. Finally, the skeletal animation technology is adopted to implement the evacuation animation of the NPCs in a virtual scene based on the decoded evacuation paths. In the third part, multi-rigid-body models of the trainees and NPCs are first created based on a physics engine. Then, the collision rules
(2) Smoke visualization In this study, to create a valid fire scene, the evolution of smoke in the virtual environment is visualized based on fire CFD simulations and particle system grids. In the fire CFD simulation, the space of the simulation is divided 3
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(a) elevation
(b) side elevation
(c) plan
(d) perspective drawing
Fig. 4. Multi-rigid-body models of a trainee.
Based on the above method, the particle systems in these grids are activated in order by the function, Smoke.StartDelay, to display the spreading process of a smoke.
Table 2 Geometrical parameters of the multi-rigid-body models of a trainee. Parts of body
Shape
Geometrical parameters
Head Main body Arms Legs
Sphere Box Capsule Capsule
Radius: 0.2 m Length: 0.4 m; Width: 0.3 m; Height: 0.8 m Height (including hands): 0.8 m; Radius: 0.1 m Height: 0.8 m; Radius: 0.1 m
3.2. Evacuation animation of NPCs (1) Evacuation path decoding To obtain reasonable evacuation paths of the NPCs, FDS is employed to perform an indoor fire evacuation simulation. This is because FDS can completely consider the fire effects (e.g., smoke hazards and low visibility) in the evacuation of occupants, and therefore, appropriate evacuation paths can be simulated. However, the data of the evacuation paths obtained by FDS cannot be directly read. To save storage space, FDS outputs the evacuation path in a binary file using the specific format of *.prt5. Such a file must be decoded so that the spatiotemporal paths can be extracted for the evacuation animation of the NPCs. Although FDS provides a decoding program based on MATLAB for *.prt5 files, it is only designed for the results of a sprinkler system and fails to decode the evacuation files. In this study, following much testing, the *.prt5 file of the evacuation path is successfully decoded, as shown in Table 1. The algorithm for extracting the evacuation path based on the above decoded writing rules is shown in Fig. 3. First, the MATLAB program provided by FDS is adopted to convert the *.prt5 file to the codes of American standard code for information interchange (ASCII). Subsequently, the key information of the evacuation, including the number of occupants (denoted as Num), evacuation time (denoted as T), and time
into discrete grids with specified thermo-physical properties, whereas the duration of the simulation is automatically divided into numerous time steps. Consequently, the soot densities in each grid at each time step are available [10]. In the VR scene, particles which are small and simple images or meshes are displayed and moved in large numbers by a particle system [43] to present the effects of a smoke and fire. Following the grids in the CFD simulation, the grids of the particle systems are created in the VR scene to visualize the spreading of a smoke [8]. In addition, the time step of updating a particle system is defined to be equal to that of the CFD simulation. Consequently, the particle systems in the VR scene have an exact spatiotemporal mapping to the grids in the CFD simulation. When the soot density of a gird is higher than zero at some time step, the particle system in this grid will be activated to present the effect of a smoke. For all the time steps, the particle system is continually activated for displaying the spreading process of the smoke, as shown in Fig. 2. In Unity, Prefab Smoke derived from Class ParticleSystems is used to create the particle systems of a smoke, and the function of Smoke.Transform can copy the particle systems to different grids [43]. 4
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1m
Trainee
NPC
Track
Distance sign Horizon
Camera
Fig. 7. Scheme of the collision experiment.
stored in a separated file for the evacuation animations. (2) Evacuation animation The implementation of the evacuation animation of the NPCs includes three key technical steps: a) 3D character modeling: creation of realistic 3D characters for the NPCs; b) Path animation: NPCs are moved following the paths; and c) Motion control: the motions are matched with the evacuation speeds. a) 3D character modeling In this study, the 3D characters are created by a skeleton and skinned mesh. The skeleton [26] is useful for the motion control, whereas the skinned mesh [44] can create realistic characters (e.g., workers, officers, and students). The skeleton and skinned mesh of a 3D character can be modeled by 3ds Max [45] and then they are exported to Unity using the FBX format for the following animations. Fig. 5. Local swing of an arm.
b) Path animation A Unity plug-in named iTween [43] can be employed to move the 3D characters of the NPCs according to the given evacuation path. iTween can automatically interpolate frames between adjacent evacuation locations, which results in a smooth animation. To implement the path animation, the evacuation path and 3D model of the NPCs are mapped by iTween, and the function of iTween.MoveTo() is called to perform the path animation of the NPCs. c) Motion control
(a) standing state
The motions (e.g., walking or running) of the NPCs should match the evacuation speeds. To switch the motions automatically, a blend tree [43] is adopted. In this tree, there are two states (i.e., walking and running), and a blend value (0–1) is used to switch the state. Generally, the mean walking speed of a normal adult is less than 1.5 m/s [46]; thus, the ratio of 1.5 m/s and the maximum speed in the evacuation simulation can be defined as the threshold. The blend value can define the ratio of the current speed and this maximum speed. Accordingly, if the blend value is smaller than the threshold, the motion state of the NPC is walking; otherwise, the motion state will be switched to running. The above blend tree can be defined by using Class Animator in Unity. Note that the motion control and path animation are individually performed in Unity so that an NPC can show compatible motions with its current speed while it moves following the given evacuation path.
(b) running state
Fig. 6. Body model for the NPCs.
step (denoted as T_s), can be read from the ASCII codes. Finally, the spatiotemporal path of each occupant is extracted according to the flow shown in Fig. 3. Specifically, the location of each occupant at each time step (i.e., evacuation path) can be read by the nested loops of the occupant IDs and time steps. The evacuation path of each occupant can be
3.3. OPC model (1) Body models for the trainees and NPCs
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(a) Recording area and distance signs
(b) Running
(c) Collision
(d) Backward movement of a trainee
Fig. 8. Typical collision process in the experiment (The left subject simulates a trainee, whereas the right subject simulates an NPC).
are summarized in Table 2. To allow the local movements of a body, Character Joint in PhysX [48] is adopted to connect the arms and legs. Taking arms for example, a rotation axis is first defined at the joint between the arm and main body. Then the corresponding allowable rotation range is added to the attributes of Character Joint, so that the arm can swing around the shoulder, as shown in Fig. 5.
Table 3 Calculated key parameters. Group
No.
mt
mn
vt1
vn1
vt2
s
CR
μ
A
1 2 3 4 1 2 3 4 1 2 3 4
70 70 70 70 55 55 55 55 60 60 60 60
68 68 68 68 72 72 72 72 72 72 72 72
1.75 1.19 1.25 1.63 2.00 2.00 1.94 1.62 2.10 1.25 1.25 1.94
−2.75 −1.57 −2.50 −2.94 −2.13 −2.38 −2.19 −1.93 −2.41 −2.00 −1.62 −2.19
−1.25 −1.02 −1.38 −1.69 −1.06 −1.06 −1.10 −0.92 −1.25 −1.12 −0.94 −0.81
1.66 1.22 1.10 2.07 1.22 1.22 1.31 1.11 1.22 1.13 0.75 0.75
0.35 0.63 0.42 0.47 0.31 0.23 0.30 0.26 0.36 0.34 0.40 0.22
0.05 0.04 0.09 0.07 0.05 0.05 0.05 0.04 0.07 0.06 0.06 0.04
B
C
b) NPCs Although the NPCs participate in the calculation of the OPCs, their evacuations, which are determined by the results of the evacuation simulation, are not affected by the OPCs. Consequently, there is no need to adopt complex models for the NPCs. In addition, the number of NPCs is large, which will lead to a decline in the interactive performance in a virtual evacuation if high-fidelity models are adopted. Therefore, in this study, simple capsules are employed as the boundary of the collision for the NPCs, as shown in Fig. 6. The size of the capsule is determined by the maximum bounding space of the skeleton at different movement states, as demonstrated in Fig. 6. Capsule Collider in Unity is adopted for the NPCs to calculate the OPCs with trainees, whereas Character Controller is also employed for the NPCs to keep fixed evacuation paths. Thus, the NPCs can participate in the calculation of the OPCs, but they do not use the results of the OPCs.
a) Trainees A high-fidelity model of the body is necessary to provide a better OPC experience for the trainees. However, in virtual evacuations, a high-fidelity body model may decrease the efficiency of the OPC calculation. Therefore, the balance between the fidelity and efficiency is a problem. In addition, the arms and legs of the trainees also have local swing movements when they move during evacuations. Such local movements need to be considered in the body models of the trainees because they may cause local collisions, and therefore, affect the evacuations. In this study, simple shapes are assembled to form a multi-rigidbody model for a trainee. This is because simple rigid bodies have a high computing efficiency for collisions. Specifically, a sphere and box are used to model the head and main body of a trainee, respectively, and four capsules are used for the arms and legs. For the OPC simulation of a trainee, the assembled rigid-body model has a detailed boundary, as shown in Fig. 4. Based on the existing literature [47], the sizes of these rigid bodies
(2) Collision rule The OPCs between the NPCs and trainees follow the rules of inelastic collisions. Assume mt and mn are the masses of a trainee and an NPC, respectively. The velocities of a trainee before and after the collision are denoted as vt1 and vt2, respectively, so that vt2 can be calculated by the following equation:
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(a) A group of running NPCs
(b) Collision with a trainee
(c) Physical models
(d) Backward trace
Fig. 9. Collision simulation between the NPCs and a trainee.
When a collision occurs, the trainee is subjected to an instantaneous force along the opposite moving direction, which may cause a backward movement. Such a backward movement is difficult to be simulated because it includes complex behaviors of the arms and legs. Consequently, an equivalent drag, which includes the friction and complex behavioral drag of the trainee, are introduced to simplify the simulation of the backward movement. Thus, the backward movement of a trainee is considered as a simplified process in which an object with an initial velocity slides under the equivalent drag until it becomes static. The backward distance (i.e., s) can be calculated by the following equation:
s=
vt22 2gμ
(2)
where μ is an equivalent friction coefficient, which can be used to calculate the influence on the movement caused by the equivalent drag. To implement the above rules in PhysX, the collisions between the NPCs and a trainee are first identified by the function, OnCollisionEnter (). Then the reverse velocity calculated by Eq. (1) can be subjected to the trainee by a function of setVelocity(). In addition, if the equivalent friction coefficient is defined in the attribution table of a rigid body, the backward distance in Eq. (2) will be calculated automatically by PhysX.
Fig. 10. BIM of the case study.
3.4. Collision experiments for parameter determination The equivalent friction coefficient, μ, and coefficient of restitution, CR, are key parameters in the above collision rules. In this study, the collision experiments of human subjects are performed to determine these two parameters. In the experiment, two subjects will collide face to face while they are running with different speeds. The entire process of the collisions will be recorded by video cameras. By the image measurement of the videos, the movement variables (e.g., velocity and distance) of the subjects can be obtained, and therefore, μ and CR will be calculated by these obtained variables.
Fig. 11. FDS model and the scenario of the fire evacuation simulation.
vt 2 =
CR mn (vn1 − vt1) + mt vt1 + mn vn1 mt + mn
(1)
(1) Principle
where CR is the coefficient of restitution.
In this experiment, the two subjects are assumed as a trainee and an 7
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(a) Escape in the room (25 s)
(b) Escape out of the rooms (50 s)
(3) Crowd in the corridor and exits (75 s)
(d) The end (125 s)
Fig. 12. Evacuation process output by the simulations.
the possible collision area for the subsequent image measurements. Based on the masses of the subjects, the experiment is classified into four groups, and four collisions with different velocities are performed in each group so that total 12 collisions are recorded by the video camera. The calculations for the distances and velocities of the subjects include two steps. First, a scale ratio k between the real distance and image distance is determined. In this experiment, the real distance of two adjacent signs is 1 m, whereas the corresponding image distance of these two signs is denoted as ds, which can be obtained by image measurements. Note that Adobe Premiere [49] is employed for the image measurements in the videos. Therefore, the ratio, k, can be calculated as expressed in Eq. (5).
k= Fig. 13. Drill traces of the three paths.
(mt + mn ) vt 2 − mt vt1 − mn vn1 mn (vn1 − vt1 )
(3)
The backward distance (i.e., s) of the trainee can be obtained by image measurements. Consequently, the equivalent friction coefficient, μ, can be calculated as the following equation.
μ=
vt22 2g s
(5)
Subsequently, the movement velocity of the subjects will also be calculated. The video is composed of numerous frames, and the time interval between two adjacent frames is Δt. Generally, Δt is an extremely small number (e.g., 1/100 or smaller) that depends on the scanning frequency of the video camera. If a subject is at location A in some frame and moves to location B in the next frame, the distance from A to B in the video will be measured (denoted as dAB). Consequently, the real velocity in this frame can be calculated by Eq. (6).
NPC, respectively. The masses of the trainee and NPC are denoted as mt and mn, respectively. The velocities of the trainee before and after the collision are vt1 and vt2, whereas that of the NPC before the collision is vn1. According to Eq. (1), CR can be calculated as shown in Eq. (3).
CR =
1 ds
v=
k∙dAB Δt
(6)
(3) Results (4) A typical collision process between a trainee and an NPC is shown in Fig. 8. From Fig. 8, the backward movement of the trainee caused by the collision is obviously observed, which indicates that the OPC between the NPC and trainee has a significant influence on the evacuations. The movement variables of the two subjects in the experiment are measured by the recorded video, and therefore, the key parameters can
(2) Experiment To measure the movement variables (e.g., velocity and distance), a scheme of the collision experiment is designed as presented in Fig. 7. Two subjects run along the track and will collide face to face in the recording range of the video camera. Distance signs are located within 8
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(a) Location A (Paths 1, 2, and 3)
(b) Location B (Path 1)
(c) Location C (Path 1)
(d) Location D (Path 1)
Fig. 14. Evacuation scenes at the key locations in the virtual drills.
evacuation simulations, BIM is converted into an FDS model by the proposed modeling solution, as shown in Fig. 11. In the fire evacuation simulation, the combustibles are two 2.0 m × 1.5 m mattresses composed of sponge and a knitted fabric (see Fig. 11), which undergo polyurethane reactions [50]. In this case, each room has two occupants, and all the occupants on the fifth story are assumed to be in the rooms when the building catches a fire. There are two exits (i.e., two stairs) on this floor; thus, all the occupants need to escape through these two exits. In the virtual evacuation drill, the trainee is located in a neighboring room of the ignition room (see Fig. 11). For the trainee, there are total three paths to exit. During the virtual drill, the NPC movements follow the results of the evacuation simulation, whereas the trainee is selfcontrolled. The NPCs possibly collide with the trainee, and therefore, in this case study, the influence on the evacuation of the trainee caused by such collisions (i.e., OPCs) will be mainly investigated.
be calculated by Eqs. (3) and (4), which are listed in Table 3. According to Table 3, the mean value and variance of the restitution coefficient, CR, are 0.36 and 0.08, respectively, whereas those of the equivalent friction coefficient, μ, are 0.05 and 0.01, respectively. In this study, the mean values of these two parameters are adopted. (4) Simulation implementation Based on the proposed OPC model and determined key parameters, a collision between a group of NPCs and a trainee is simulated, as shown in Fig. 9. In this simulation, the group of NPCs and a trainee run toward each other for collision. Because the designed evacuation animation methods of the NPCs are adopted, the NPCs have detailed 3D characters with appropriate running motions, which make the entire collision process extremely realistic (Fig. 9(a) and (b)). In addition, it can be observed from Fig. 9(c) that a high-fidelity physical model is adopted for the trainee to simulate the complex collision with the NPCs. In addition, the key parameters (i.e., CR and μ) are determined by the collision experiment, which ensures the rationality of the collision simulation. The trainee has an obvious backward distance after the collision (see Fig. 9(d)). Because the collision between the trainee and NPCs is at an angle, the backward distance is also at an angle, which indicates that the proposed collision rules are reasonable.
(2) Fire evacuation simulation The entire fire evacuation process is simulated based on the CFD model constructed by the proposed modeling solution, as presented in Fig. 12. The results of the smoke spreading and evacuation paths are output for the subsequent fire scene and animation of the NPCs. (3) Virtual evacuation drill considering OPCs
4. Case study
The smoke scene and animation of the NPCs are implemented using the proposed methods based on the above simulation results. Consequently, a reasonable and realistic evacuation scene is created for the virtual drills. Furthermore, the proposed OPC model is built for the trainee and NPCs. As shown in Fig. 11, there are three evacuation paths from the original location to the exits. To determine the safest path, the trainee
(1) Introduction of case study A six-story dormitory is selected as a case study, and its BIM is shown in Fig. 10. The total height and built-up area of this building are 21.60 m and 9600 m2, respectively. The fifth floor of the case study is selected to simulate the fire evacuation of the occupants. For the fire 9
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( e ) L o c a t io n E (P at h 2 )
(f) Location F (Path 2)
(g) Location G (Path 2)
(h) Location H (Path 3)
(i) Location I (Path 3)
(j) Location J (Path 3) Fig. 14. (continued)
(4) Safest path
performs three different drills on these three paths, respectively. The evacuation traces for these three paths are shown in Fig. 13. The corresponding scenes at the key locations (see Fig. 13) of the above three paths are shown in Fig. 14. For path 1, it can be observed that there are circuitous traces near location B. This is because the trainee is forced to move in reverse by the head-on collisions with the group of NPCs, as shown in Fig. 14(b). Contrastingly, the traces of the trainee near locations C and D are extremely smooth because there is no collision with the NPCs (see Fig. 14(c) and (d)). Similarly, the trace of path 2 from location F to G is wavy, which is also caused by the OPCs, as shown in Figs. 14(f) and (g). The trace of path 3 indicates that the trainee moves long distances for turning near locations H and I. As shown in Figs. 14(h) and (i), numerous NPCs are crowding at locations H and I when the trainee attempts to turn, and the OPCs make the turns of the trainee difficult. In summary, the traces of the above three paths indicate that the OPCs have a significant influence on the evacuation of the trainee.
In this study, to assess the safety of the different evacuation paths, the integrated hazard dose (IHD) [10] is adopted. The IHD can assess the overall hazard due to toxic gases and heats of indoor fires using a value from 0 to 1. A high IHD implies a severe hazard. When the IHD is equal to 1.0, the hazard is fatal. In a virtual evacuation, the IHD along different paths (i.e., IHDpath) can be calculated as the following equation: path path IHD path = max (FED6‐ Gas , FEDheat )
(7)
where FED6‐Gaspath and FEDheatpath mean the hazard indexes (i.e., fractional effective dose (FED) [10]) caused by toxic gases and heats, respectively. FEDheatpath is calculated by the radiant flux and temperature, whereas FED6‐Gaspath is calculated by the atmospheric concentrations of 6 gases (i.e. CO, CO2, HCN, O2, HCl and HBr) that commonly appear in smoke. The required calculation parameters can be obtained from the result of the fire CFD simulation, and the calculation details can be 10
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The comparison between Figs. 15 and 16 reveals that the average evacuation time and IHDpath values of the three paths without the NPCs are shorter and smaller, respectively, than those with the NPCs. This indicates that the OPCs significantly decrease the efficiency and safety of the evacuations. Furthermore, it can be observed that according to the IHDpath values in Fig. 16, path 1 is the safest path, which is different from the case with the NPCs. This indicates the OPCs can alter the safest evacuation path. Therefore, the decision-making for the safest evacuation path is quite different when the OPCs are considered. 5. Conclusions In this study, a virtual drill method for indoor fire evacuations considering OPCs is proposed, and a case study of virtual evacuation drills conducted in a six-story dormitory building is investigated. Some conclusions can be drawn as follows: (1) A reasonable and realistic scene of multi-occupant fire evacuations can be constructed by using the designed BIM-based modeling solution, smoke visualization algorithm and NPC animation algorithm. (2) The OPCs in the evacuation drills can be validly simulated by using the established physical model and the corresponding key parameters determined by a real collision experiment of human subjects. (3) The comparison of virtual drills with and without the NPCs indicates that the OPCs significantly decrease the efficiency and safety of the evacuations. Furthermore, the OPCs can alter the safest evacuation path, which influences the decision of choosing the safest path. (4) The proposed method enables trainees to fully experience OPCs in virtual multi-occupant evacuation drills and assists them to make safe evacuation decisions.
Fig. 15. Integrated hazards and evacuation times of the three paths considering the OPCs.
Note that the designed solution to constructing the scene of multioccupant fire evacuations in this study is based on the widely-used FDS platform. Although the technical details are different if other fire CFD simulation platforms are adopted, the scene construction framework in this study still works. In the future, the proposed virtual drill method will be applied on the web or mobile devices, so that people can conveniently use such method to improve their ability of fire evacuation considering OPCs. Meanwhile, the evacuation paths in the drills will be also collected to analyze the evacuation features and thus teach occupants to evacuate safely based on these features.
Fig. 16. Integrated hazards and evacuation times of the three paths without the OPCs.
referred from the work of Xu et al. [10]. The IHDpath in the evacuation process of the above three paths is calculated, as shown in Fig. 15. It can be observed that the evacuation times of path 1 and path 3 are much longer than that of path 2. This is because head-on collisions with the NPCs are frequent on these two paths (see the corresponding traces and scenes in Figs. 13 and 14). By contrast, the OPCs on path 2 are fewer than those on the other paths; thus, the evacuation time of path 2 is the shortest. Furthermore, owing to such a long exposure time to smoke hazard, the IHDpath values of path 1 and path 3 are also much larger than that of path 2. Therefore, in this multi-occupant evacuation scenario considering the OPCs, path 2 is the safest path.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors are grateful for the financial support received from the National Key Research & Development Program of China (No. 2018YFC0809900), the National Natural Science Foundation of China (No. 51978049), Beijing Municipal Science and Technology Project (No. Z161100001116104), and the Fundamental Research Funds for the Central Universities (No. FRF-BD-18-007A).
(5) Comparison with a non-NPC scenario To further assess the influence of the OPCs on the safest path, a virtual evacuation drill is performed in absence of the NPCs. Note that the fire scenario is the same as that of the drill with the NPCs. In this non-NPC scenario, virtual evacuation drills of the three paths are performed, and the corresponding IHDpath values are calculated, as shown in Fig. 16.
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