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Property Risk Optimization by PredictiveHazard Evaluation Tool (PROPHET)

Final ReportFunded by the SFPE Educational & Scientific Foundation Chief Donald

J. Burns Memorial Research Grant

Austin Anderson and O.A. Ezekoye

August 27, 2014

1 Introduction

The total cost of fire in the United States on a yearly basis is substantial. According to Hall,economic losses associated with fire in 2010 totaled an estimated $14.8 billion [1]. In additionto this direct cost component from fire damage, there is an additional $19.2 billion thatgoes towards insurance premiums [1]. It is clear that there are tremendous costs associatedwith fire losses annually in the United States, and thus the possibility of reaping substantialbenefits if the fire risk leading to these losses, in addition to those risks regarding human lifesafety, can be identified, quantified, and tracked.

Unfortunately, the industry standards regarding fire risk have historically been rather loose.For property protection, fire insurance companies, for the most part, set their premiums basedon qualitative inspections and code compliance standards, resulting in what are classified as“well-protected” properties suitable to be insured. Ramachandran suggests that the insuranceindustry does not adequately quantify fire risks, opting instead to set premiums withoutsuitable effective statistical backing [2]. From the life safety perspective, new construction islargely driven by standards created by the International Fire Code council and National FireProtection Association.

Building Information Modeling [BIM] is an emergent paradigm in the construction, buildingoperations, and maintenance fields that is facilitating and encouraging more involved use ofcollaborative 3-D modeling and object oriented database approaches that allow stakeholders inprojects more control over every aspect of a building’s life cycle. Prominent in the operationsand maintenance portion of a building’s life is asset management software, which gives ownersthe ability to track contents within their buildings at the room level while also giving themthe ability to add attributes to the contents they are tracking.

The end goal of the PROPHET framework is to improve the level of fire hazard and riskinformation that is available to the building owner, fire service, and any other interestedstakeholders such as insurance companies within the BIM context. This approach will berealized by tying objects tracked in asset management software to available fire modeling andstatistical risk approaches through the addition of new object attributes that store relevantproperties necessary to inform a fire risk and hazard analysis. Additionally, other buildingattributes available through BIM models, such as potential built-in heat sources like outletsand fire suppression systems will be drawn and utilized by the fire risk model.

2 Overall Methodology

Figure 1 summarizes the PROPHET framework. First, the design model for the structure,floor, or room in question is either acquired or built, including built-in heat sources suchas outlets, and relevant fire protection features such as wall ratings and sprinkler locations.Next, an asset model is either acquired or constructed, which details in a general way thevarious objects in the rooms on the floor, and preferably their geometry in the room in either2-D or 3-D.

Various attributes would be attached to the room contents depending on the level of detaildesired in the fire modeling process. Table 1 provides a summary of necessary new attributes,and their uses. For a basic initial model, only the heat release rate curves and ignition heat

1Chief Donald J. Burns Memorial Research Grant Final Report

Acquire or build model of floor with basic geometry,

outlet locations, and fire protection features intact

Reference or populate model with contents information

Append model-specific

properties to contents

Gather reliability data on model heat-sources

Gather burning properties of model

contents

Gather reliability data on fire

protection features

Identify contents that could act as heat

sources

Identify intersections between heat

sources and fuel sources in the model

Construct fire scenarios based on

identified intersections and fire

protection features

Weight fire scenarios using heat source

reliability data

Model-based risk output for floor

Figure 1: PROPHET framework for integrating BIM models with fire risk methodology


Table 1: Summary of attributes to add to contents objects

Attribute Application

Heat Release Rate Curve Necessary input for firemodel

Ignition Heat Flux Necessary input for firemodel

Probability of Ignition Necessary for weighting firescenarios

Probability of alarm/sprinkler operation Necessary for constructingand weighting fire scenarios

Probability of vent opening area Necessary for predicting firegrowth

flux need be known. Practically speaking, the models do not require incredible detail, soitems can be categorized in a fairly general way by their material composition if model sizeis a concern. In addition to these two attributes, probability of ignition is required for allitems that can serve as heat sources. Similarly, probability of operation is required for allactive fire suppression systems such as alarms and sprinklers, and fire rating is required forthe walls of a given compartment. Ventilation characteristics can also be treated statistically.

Fire scenarios would be constructed using a simple rule. Any time a potential heat sourcein the room overlaps with a fuel package, the hazard is identified and a fire scenario createdfeaturing the heat source as an ignition source on the object. Fire scenarios would beconstructed for all identified intersections on the floor and subsequently run to determinepotential levels of damage from fires that might occur on that floor. If sprinklers arepresent, each scenario would include a sprinklers-working and not-working variant. Firealarm activation is modeled by penalizing the arrival time of the fire brigade to the scene.Finally, each fire scenario’s outcome would be weighted by its probability of occurrence, andthe total outcomes can be aggregated to form the floor’s expected loss or damage due to itspresent configuration of hazards.

Ultimately, PROPHET is a framework for using BIM models and asset managementsoftware to construct numerous relevant fire scenarios for buildings that can then be analyzedin many different ways.

3 PROPHET specifics

PROPHET’s framework involves a system for developing relevant fire scenarios in a buildingand then weighting them to obtain a risk profile for the structure. However, there are asizeable number of difficulties in moving through the steps outlined in Figure 1. This sectionwill outline the relevant difficulties involved with various steps in the PROPHET frameworkand their proposed solutions.


3.1 Conversion of BIM data to a fire model

Presently, there is no established conversion process that takes the relevant information in aBIM model and converts it into an input file for fire models such as Fire Dynamics Simulator(FDS) or Consolidated Model of Fire and Smoke Transport (CFAST). A study examiningthe feasibility of such a conversion was performed in 2007 by Dimyadi [3]. He notes that themost reliable format to use for conversion is the Industry Foundation Classes (IFC) datamodel created by the buildingSMART alliance, which likely will remain relatively stable asa file format as it is intended to last for building lifetimes. Dimyadi was able to constructa parser that successfully read and converted geometry and, in a coarse sense, objects in aroom into FDS format from an IFC file. However, he encountered some strong limitations inimplementation due to idiosyncrasies in FDS’s implementation of obstructing objects [3].

Another viable path of conversion relies on the use of the Blender software package. Blenderis an open-source 2D and 3D modeling package that is readily extensible by add-ons, andthere are presently a number of add-ons and scripts that can convert geometry in Blender tothe geometry in a FDS input model. Likewise, Blender has an add-on that supports importingIFC files, so it can, with some difficulty, serve as a feasible bridge between standard BIMsoftware and FDS without requiring the development of a brand new parser from scratch,which is always an option. However, blender is primarily a 3D visualization software, andso while it excels at handling geometric conversion between a BIM model and FDS, otherattributes and properties, such as thermophysical properties of room boundaries like walls,must be externally referenced and parsed from either the BIM model, if relevant propertiesare available from it, or an external database containing the material properties.

The following list summarizes the attributes required in FDS to set up a fire scenario:

• Building Geometries:

– floor, ceiling, and wall geometry

– relevant vents – e.g. doors, windows. These are rendered in FDS as “holes” in thegeometry.

– xyz coordinates of fire protection features such as sprinklers and alarms.

• Floor, ceiling, and wall thermophysical properties

• Fuel packages:

– representative bounding box. this is a sextuple of coordinates describing thecoordinates of the opposite corners of a cube in space. see Figure 2 for a visualdescription.

– heat release rate curve (HRR) of the fuel package

– ignition heat flux

Geometry conversion has been discussed. Fortunately, most BIM software has some form ofAPI or object output list that is readily queried. Thus, it should be possible to query relevantthermophysical properties of boundaries from the models themselves, otherwise it is necessaryto construct an IFC file parser that will locate and extract the relevant properties. Methodsfor acquiring and handling the fuel package properties will be discussed in section 3.2.


Figure 2: Representative bounding box with sextuple of coordinates, x1,y1,z1,x2,y2,z2

3.2 Obtaining fuel package data

Traditionally, gathering fuel package data has been accomplished via on-site surveys that seekto obtain the total combustible mass in a sample of rooms in a given building [4–6]. Thereare typically two approaches, or some combination thereof, used in these sorts of surveys:

• Weighing method – Combustibles in a given compartment are individually weighed andtheir contribution to the room’s fire load noted.

• Inventory method – Pioneered by Culver in 1976, this method involves gathering alist of items in a given room along with their volumetric measurements, then usingmanufacturer data sheets or prior metrics for density to determine the item’s contributionto fuel mass in a room.

Obviously, the inventory method very readily lends itself to BIM implementation, whencoupled to asset management software. Many of the relevant combustible items in a roomwill already be accounted for in the database, with their material sheets readily available.Culver notes that smaller objects’ weights can be estimated by the inspector, and that errorsin these weight measurements due to a lack of weight estimation training or experience forthe typical inspector do not unduly impact the overall fuel mass estimate in the room [4].Zalok, however, recommends utilizing the weighing method for smaller items in a room thatare readily manipulated by one person to gain additional accuracy in estimates [7].

Originally, the impetus for determining this “room level” fuel mass was to use the standardheats of combustion of wood and paper to convert it into an energy load for use in determiningthe necessary fire ratings of walls in compartments [8]. Nowadays, it can be used to estimateappropriate design fire experiments in support of performance-based design [9] and riskassessment.

The PROPHET framework, as noted in the list in section 3.1, requires a finer resolution ofdetail regarding the fuel contents of a room than the summary measures indicated by theseother studies. Because it seeks to construct a fire model for plausible ignition scenarios in agiven building, it is necessary to know the representative volume and location of the contentsin rooms. Thus, PROPHET would still likely require an on-site inspection since buildingmanagers will not typically track room contents in detail outside of primary building assets.


This inspection could be performed by either an individual with a tablet PC with a floor planand sketching capabilities, or by an individual with a camera, privacy obligations permitting.

After objects in a room are listed and their weights determined, it is necessary to developrepresentative heat release rate curves and ignition heat fluxes for them, as noted in Table 1.Typically, these heat release curves are determined at an object level at sizeable experimentalexpense. Since it is likely infeasible due to cost to obtain and perform fire experiments onduplicates of every combustible asset intended to be inside a given building, approximationsare necessary. For PROPHET, a proposed approach is to use the HRR curve of the closestmatching object from available literature [10] to approximate the burning behavior of a givenitem, with corrections made for size and mass differences between the experimental itemsand the object of interest.

Ignition heat flux is likewise difficult to retrieve without experimentation. However, it istypically accepted that fuel packages susceptible to ignition (e.g. paper) ignite at around 15kW/m2, typical packages ignite at roughly 20 kW/m2, and more fire resistant packages willignite at roughly 40 kW/m2.

Regarding automated workflow in BIM applications, it is envisioned that in the near futurethere will be a database of common objects compiled for fire protection modeling applications.This would enable fire scenarios generated by BIM input to readily reference objects fromthis database for more effective automation, rather than relying on a manual process forobject designation.

3.3 Obtaining reliability data

PROPHET requires two sets of reliability data for its implementation:

• Fire suppression system reliability data

• Ignition source reliability data

Fire suppression reliability refers to the probability that a given fire protection feature suchas a heat detector or automatic sprinkler system will operate as intended given a fire occurs.Fortunately, reliability of fire protection systems has been examined quite well, so reasonableestimates can be obtained quite readily from literature [1, 11, 12].

Ignition source reliability refers to the probability of a given energized appliance failing insuch a way that ignition results. This is unfortunately a very difficult value to pin down forindividual categories of materials at the level that it is useful for a risk analysis. There aretwo potential approaches that could work, both with complications:

• Use historical data on equipment involved in fire ignitions to estimate equipment ignitionfrequency

• Perform fault-tree analysis on all relevant ignition sources to obtain an estimate of aprobability of failure leading to ignition

The difficulty in using the first method arises from the necessity of normalizing all historicevents over the universe of possible events. For example, if the probability that a laptop in


America will ignite within the year is desired. The following simple equation would provide areasonable estimate of this frequency:

P (laptop ignition within 1 year) =# of fires due to laptops in America in the past year

Total # of laptops in America in the past year

The National Fire Incident Reporting System (NFIRS) is a national fire database to whichmany fire departments report, and it obtains an estimated 75% of all fires that occur annually[13]. Unfortunately, reporting fire departments do not evenly cover America, introducingadditional complications to the denominator. If NFIRS (the best source of fire event data inAmerica) is to be used, then the equation above needs to be altered in the following manner.

P (laptop ignition within 1 year) =

# of NFIRS reported fires due to laptops in America in the past year

Total # of laptops in areas with an NFIRS reporting fire department in the past year

Unfortunately, while market reports estimating the total number of laptops in America mightexist, it is unlikely that one could narrow it down to the specific regions necessary to renderthe altered equation viable. Additionally, such an estimate is technically valid only for areascovered by an NFIRS reporting fire department. This is not as much of an issue as might beexpected because most urban areas where commercial buildings are constructed are within aprotected zone.

The second method, using event tree analysis, is a more proactive approach, but hasits own issues. Event tree analysis, also called fault tree analysis, seeks to identify andquantify the probability of the events that are necessary to reach an outcome of interest,here ignition by an ignition source [14]. Consider the example above, the probability of alaptop in America igniting a fire within a year. In this case one might identify that the laptopcould potentially ignite due to overheating of one of several components, say the cpu or thebattery. Additionally, assume that the overheating from the cpu cannot happen unless boththe laptop’s cooling fan fails and the card is “overclocked” and running at a higher voltagethan normal. Figure 3 presents an event (fault) tree for this situation. This provides a visualrepresentation of all the events necessary to arrive at the failure. Probabilistically, this figurewould be represented as follows:

P (laptop ignition) = [P (fanfail) ∩ P (overclock)]∪P (GraphicOverheat)∪P (BatOverheat)

Looking at the above equation, the difficulty in utilizing this approach in PROPHET becomesplain. Each class of ignition source requires an arbitrarily large fault tree to be constructed,and the probabilities of the constituent events leading up to the ignition event quantified.

The difficulties discussed above are what would be necessary for a “gold standard” levelof accuracy in the modeling process. Realistically, overcoming them for each object thatcan be ignited in a building is probably too costly to afford unless a database of such objectattributes were being constructed. In lieu of such complexity, approximations must reasonablybe used. For example, it might be possible to argue that the population not covered byNFIRS reporting fire departments would have a negligible contribution to the laptop ignitionand ownership statistics, and thus the denominator could be laptop population in Americawithout strongly biasing the frequency estimation. Likewise, there may already exist faulttree models or statistical papers for more common electrical equipment that can be readilyadopted, as there are for fire protection systems.


Figure 3: Fault tree for laptop ignition

3.4 Identifying fire scenarios

Once a model is constructed that contains all relevant room geometries and fuel pack-ages/ignition sources, it is necessary to identify potential fire scenarios. In a BIM setting,this procedure is done as an extension of clash detection. Objects designated as ignitionsources are compared against objects designated as fuel packages. If an object designatedas a fuel package is within some distance (likely related to the heat transfer length scale forignition) of an ignition source, a fire scenario involving ignition at that source is created, runand analyzed using all relevant attributes outlined above. In this manner, an entire building’sworth of potential fire scenarios are identified and analyzed.

4 Norman Hackerman Building (NHB) office case study

As an example of the implementation of the above process, a case study was constructedexamining a particular office at The University of Texas at Austin’s Norman HackermanBuilding. It is a newer building on campus for which BIM models were available, andpossesses a more contemporary architecture, featuring free-flowing “collaborative” spaces andsizeable atriums within parts of the building. For this paper an interior office, room 5.402,was selected for examination.

4.1 Fire load survey

As a first step, a fire load survey was conducted in office 5.402 of the NHB. The survey methodadopted for this phase was the inventory method of Culver [4] mentioned in section 3.2. As


a reminder, this survey method involves obtaining either catalog information regarding themass of combustible items in a room, or measuring the volume of items and using materialdensities to approximate their combustible mass.

The volumes of items were measured, or if they seemed distinct enough, e.g. a “chef stylecoffeemaker” in Table 2 and Figure 7h, they were located on an internet catalog and theirshipping weight considered their weight. A few items clearly not entirely made of combustiblematerial, e.g. the computer monitor, had their shipping weight multiplied by the estimatorsguess at the percentage of the weight contributed by combustible materials. Additonally, a3-D model of the room was developed in sketchup to better visualize relevant dimensionsand spacing of combustibles in the room. This 3-D model formed the starting point for theBIM-to-FDS conversion proof of concept discussed in section 4.2.3, and mentioned earlier insection 3.1.

Material densities were determined from several different sources [15–18]. Cross-referencingbetween sources indicated such densities were reasonable.

A survey of various sources heats of combustion indicated that material heats of combustioncan vary rather widely. For this evaluation, mid-range values were selected.

Finally, there is debate in the literature regarding how to treat combustible material housedin noncombustible containers, such as metal filing cabinets or blueprint cabinets [4, 7]. Forthis survey, items in non-combustible containers were assumed to not contribute to the fireload. This is obviously a more “liberal” assumption, but was a reasonable trade-off to avoidinvasive examination of what might be sensitive materials.

4.1.1 Room geometry

The geometry of the office was a standard rectangular enclosure, 3m x 7.5m x 3m in size, with3 doorways. 2 of these doorways led to smaller personnel offices, while 1 doorway providedaccess to a corridor connected to the rest of the building. Doorway dimensions were 0.91mwidth, with a 2.1m soffit height. For modeling purposes in this paper, the two doorwaysleading to the smaller offices were assumed closed (and thus not modeled), and the doorwayconnecting to the rest of the building was assumed to be propped open. Figure 4 displaysthe dimensions of the room and doors as modeled.

4.1.2 Office model and content distribution

In order to aid visualization of the distribution of the office contents, as well as in anticipationof future steps, a Sketchup illustration of the office and its contents was created. ExaminingFigure 5, it is apparent that the office space can be thought of as two zones, the front andthe back of the room, divided by the bookshelves in the middle of the room. Figure 6 showsactual areas of the office corresponding to sections of the model to aid in orienting the reader.

4.1.3 Office inventory items

Table 2 summarizes the inventoried results of the fire survey. This section provides adescription and photos of the items described in the inventory. Figure 7 contains photos ofthe items in the inventory, which are listed as follows:


Figure 4: Approximately Isometric view of office 5.402 in the Norman Hackerman building

• Bookblock – The bookshelves that divide the room into two spaces contain a lot ofbinders and paper-based materials. In order to simplify the inventory, every level ofthe bookshelf was classified as all or part of a “Book Block.” In the calculations, thevolume of a Book Block was determined by measuring the volume of the papers/bindersin the top left shelf of the left bookcase in Figure 7a.

• Paper box – Six boxes full of paper were found in the room. Two of the boxes arelocated in the work station zone and four boxes rest in the first zone of the office.Figure 7b displays the paper box.

• Stack of folders – Atop the blueprint cabinets were several different stacks of folders asseen in Figure 7c. In the inventory, several different folder stacks of different dimensionsare identified.

• Paintings – Three paintings were located around the office, one of which is depicted inFigure 7d. The combustible mass of these paintings was approximated as wood.

• Mini-fridge, Microwave & brown-top table – At the front of the room there is a minirefrigerator, a microwave, and a brown-topped table, as depicted in Figure 7e. the massof the microwave and mini-fridge was obtained using an online catalog, in this caseinternet catalog, and their combustible mass was approximated as a percentage of thatmass.

• Metal-frame chair pad & round table – At the front of the room were four metal-framedchairs with thin padding around a round table, as depicted in Figure 7f. The chairpadding was assumed to be polyurethane.

• Whiteboard – A whiteboard was hung along the wall on the left side of the room, asdepicted in Figure 7g. the text on the whiteboard has been blanked out for privacy


(a) Sketchup illustration view of room con-tents, viewed from the front

(b) Sketchup illustration overhead view ofroom contents, oriented from the back

Figure 5: isometric and overhead view of Office Sketchup illustration

purposes.

• Coffeemaker – On the brown-top table there was also a coffeemaker, as depicted inFigure 7h. Its mass was obtained by matching on internet catalog. Its material wasassumed to be polypropylene.

• Desktop computer – On top of the back desk of the office was a desktop computer,depicted in Figure 7i, the mass of which was obtained by looking for similar productson internet catalog.

• Computer chair – Located in the back of the office was a padded rolling chair, shownin Figure 7j. Its padding material was assumed to be polyurethane foam.

• Printer – A printer was placed on the blueprint cabinets located at the back of theoffice, and is pictured in Figure 7k. Its material was assumed to be polypropylene.

• Recycle bin – located at the back of the office was a recycle bin for paper disposal,depicted in Figure 7l. Its material was assumed to be polypropylene.

4.1.4 Survey summary

The fire load survey performed on NHB office 5.402 indicated an estimated total fire loadwithin the room of 20,562 MJ of energy, most of it paper, resulting in a fuel load density ofapproximately 914 MJ/m2.


Table 2: Fire Survey inventory

Name No. of Units UnitCom-bustibleMass(kg)

Material MaterialHeat ofCom-bustion(MJ/kg)

Source

Book block 11.8 61.8 paper 18 volumetricPaper Boxes 5 20.5 paper 18 volumetricDesk Blueprint 2 3.2 paper 18 volumetricStack of folders 1 2 15.1 paper 18 volumetricStack of folders 2 2 10.1 paper 18 volumetricStack of folders 3 5 7.6 paper 18 volumetricPainting 1 1 7.5 wood 18 volumetricPainting 2 1 5.8 wood 18 volumetricPainting 3 1 22.2 wood 18 volumetricRefrigerator 1 15.5 polypropylene 30 Internet-catalogMetal-frame chair pad 8 1.9 polyurethane 30 volumetricWhiteboard 1 3.6 melamine 30 Internet-catalogCoffeemaker 1 9.5 polypropylene 30 Internet-catalogBrown-top table 1 5.1 particle board 18 volumetricDesktop computer 1 1.4 polypropylene 30 Internet-catalogRound table 1 13.2 particle board 18 volumetricBack desk 1 2.2 particle board 18 volumetricRoller chair 1 11.3 polyurethane 30 Internet-catalogPrinter 1 15.4 polypropylene 30 Internet-catalogRecycle bin 1 1.4 polypropylene 30 Internet-catalogsmall trashbin 1 0.5 polypropylene 30 Internet-catalogRoll of paper 1 1.7 paper 18 Internet-catalog

Total fire load (MJ) 20562Fire load density (MJ/m2) 914


(a) Photo of room contents, taken at front ofroom

(b) Photo of room contents, taken at back ofroom

(c) Model of room contents, oriented at frontof room

(d) Model of room contents, oriented at backof room

Figure 6: Comparison of office photos with sketchup illustration

4.2 Fire scenario modeling

Potential fires in the NHB office were evaluated using the FDS fire model, in order to devisethe growth and impact of a reasonable subset of representative potential fires within theroom. CFAST was not utilized in this analysis, as the program contains strict limitations onthe number of fires that can be parsed at a time, and is thus unsuitable for attempting tomodel fire spread or fire growth within a room. CFAST could perhaps prove more useful forbuilding-level evaluations of compartment interactions, once potential fire scenarios withinindividual compartments had been evaluated using FDS.

4.2.1 Heat release rate curves and material properties

FDS minimally requires HRR curves describing the burning of a room’s contents. Readilyfinding HRR curves for the contents of a room can be difficult. Since the development of the


(a) Book block (circled in red) (b) Paper box (c) Stacks of folders

(d) One of three paintings (e) Mini-fridge, microwave,and brown-top table

(f) Metal-frame chairs andround table

(g) Whiteboard (text blankedfor privacy)

(h) Coffeemaker (i) Desktop Computer

(j) Rolling desk chair (k) Printer (l) Recycle bin

Figure 7: Photos of combustible room inventory


cone calorimeter and furniture calorimeter, huge numbers of HRR experiments have beenperformed documenting the HRRs of various configurations of objects. However, the modeleris responsible for the following regarding these HRR curves:

• Locating the paper or study that contains the experimental data for the items ofinterest,

• Evaluating the experimental conditions of the paper or study to ensure that the roomcontents conditions match that paper’s experimental conditions.

The first point above is difficult to handle at times because there is no extensive onlinedatabase of experimental HRR data, so the modeler is dependent on locating relevant papersor experimental data using their own contacts in the fire field in conjunction with a few onlinerepositories. A few organizations that bear contacting for this purpose when help is neededdue to the rate at which they perform tests include:

• National Institute of Standards and Technology (NIST) Building and Fire ResearchLaboratory’s Fire Research Division,

• Underwriters Laboratories,

• Southwest Research Institute’s Fire Technology Group

The second point above comes about because interaction between objects can result inHRRs that are larger than either object’s individual HRR, and additionally the HRR canbe readily affected by factors such as room geometry or fuel orientation [10]. However, inorder to model the potential behavior of a room, the best set of HRR information availablewithout additional experimental testing generally does not readily match the room geometryor conditions. In such a case, an ethical but time-intensive approach would be to perform asensitivity study of the room to feasible variations in HRRs of its objects.

For the purposes of this modeling study, a number of HRR curves were identified forvarious items, as described in Table 3. It should be noted that the HRR for a number ofitems was approximated by weighting the HRR curve of test 201 in [19] by the mass of paperin the item divided by the mass of the paper used in test 201 in [19], which was 240 kg. Thisapproximation is rather weak, but since the original item was a vertically oriented bookshelfloaded with paper, such estimates should be conservative due to the increased growth effectfrom the vertical orientation being carried to smaller items. Additionally, the HRR of theminifridge was assumed to be akin to that of a CRT monitor, and the HRR of the microwavewas assumed to be the same as that of the printer, as all three items feature some level ofmetal construction with plastic features. Finally, only one desk was readily found in HRRliterature, so its HRR curve was used for all three desks in the NHB office.

In addition to the HRR curves, it was necessary to gauge item materials and determine thedensity, thermal conductivity, and specific heat capacity of said materials. Table 4 summarizesthe materials, their properties, and the source from which the properties were obtained. Forthis analysis, all material thermal properties were assumed temperature invariant. This is astrong assumption, but simplifies the model input greatly and does not introduce an amountof uncertainty that would override, say, the uncertainty introduced from the mismatch of


Table 3: List of items for which a HRR curve was obtained, and the sources from which theycame

Item Material Source

back desk HD particle board [20]bookblock paper [19]metal-frame chair polyurethane [21]coffeemaker polypropylene [10]desktop computer polypropylene [10]computer chair polyurethane [22]folder stack paper [19]front desk HD particle board [20]microwave polypropylene [23]minifride polypropylene [23]paintings paper [19]paperboxes paper [19]printer polypropylene [23]recyclebin polypropylene [24]roundtable HD particle board [20]trashbin polypropylene [24]whiteboard melamine coated paper [19]

the experimental HRR curve conditions with the room under analysis or the simplifyingassumption that all gas-phase combustion reactions are methane in lieu of any knowledgeabout the reaction chains and interactions of a room filled with proprietary substances, someflame-retarded, of which one has no real knowledge.

4.2.2 Fire scenarios

A number of potential fire scenarios were examined within the room. Figure 8 displays afew views of the fire model showing items that were considered first ignited for fire scenarios.Each of these scenarios provides a basis for the statistical manipulations in a later analysis.

4.2.3 From Bentley AECOsim to FDS model

The process of developing a solid FDS model even for one compartment like the NHB officeis a time-consuming, detail-oriented process. Figure 9 summarizes the process required toobtain FDS input files for room contents fires from BIM, CAD, or Sketchup files. Additionally,Figure 10 displays the model at various steps in this process.

First, a model of the compartment and its contents is constructed. The reason behindusing sketchup for the initial model is that Google provides a model warehouse functionalitythat allows one to browse a catalog of user-submitted models and import them directly intotheir existing model. Thus, it was easy to locate objects within the model warehouse thatmatched in appearance those found in the room. This functionality is also present within


Table 4: Modeled item materials and thermal properties

Material Density (kg/m3) Thermalconduc-tivity(W/mK)

Specificheatcapacity(kJ/kgK)

Source

5/8” Gypsum board 755 0.188 1.09 [17]1/2” Gypsum board 772 0.16 1.09 [17]Concrete 2000 0.14 0.8 [15]Paper 930 0.18 1.34 [15]High Density Particle Board 1000 0.17 1.3 [15]Melamine 1500 0.5 1.2 [18]Polypropylene 880 0.15 1.9 [16]Polyurethane 1100 0.19 1.76 [16]Plain Steel 7854 60.5 0.43 [15]

Bentley AECOsim, so a true BIM model can be quickly supplemented with room contents,albeit said contents only provide geometric information natively.

Generally, these room contents will also be an approximation of the actual room contents,with the survey driving how well a match can be made. If this analysis is being performedduring the design phase of a building, then an approximation as is often seen in visualizationscan be used, provided it matches with the expected use of the compartment.

Second, this room contents model is exported from AECOsim as a .IFC format object, andimported into Blender. As stated prior, Blender is natively a visualization modeling software,so it only conserves the geometry information from the .IFC file. This geometry is oftenquite overly complex for the resolution that FDS operates at, and simplification is necessary.The approach to simplification adopted for this paper was to replace all combustible objectsin the room with their representative bounding boxes, and then assure their boundariesaligned with the global axes. Figure 11 provides a visual of this operation. Additionally, forsetting up object thermophysical properties, it is necessary to import the material propertiesfor all objects involved in the model from either the BIM itself, in this case AECOsim, oroptionally an external database that can detect the object material and supply the correctthermophysical and burning properties.

Next, plane geometry designating the locations of vents and heat detectors for FDSis created by duplicating the relevant faces of the simplified geometry items. This task isperformed by looking at the orientation of combustible objects in the room, and then selectingthe face most likely to be exposed to fire in a real fire scenario. Usually, this is the top faceof most objects, unless they are nested within bookshelves or have other objects situateddirectly on top of them. Note that each of these vents and heat detectors has associated withit the critical heat flux of ignition of the material and a material ID that designates the firepertaining to that object in the configuration file.

Next, a configuration file is constructed consisting of initialization settings and gas-phasereaction settings (for this analysis a simple methane reaction), as well as the surface IDs


Figure 8: FDS model view with 8 examined initial fire ignition scenarios highlighted. Fromleft to right: printer, pile of paper, computer chair, books on the back and frontshelves, respectively, the round table, a metal-framed chair, and a box of printerpaper at the front of the room.

and fire ramps for each object’s prospective fire. Note that to construct the fire ramp fora given object, it is necessary to take the HRR curve chosen to represent that object, andthen divide it by the surface area of the vent representing the burning surface of the object.This operation is necessary because FDS only accepts heat release rates in the form of heatrelease rate per unit area, corresponding to the area of a vent.

Finally, once everything is in place, the model is exported into an FDS input file, and, ifnecessary, debugged until it is operational. At this juncture, it is simply a matter of swappingwhich item is the ignition item in order to explore several different fire scenarios for a givencompartment.

4.2.4 FDS model automation

Conceptually, it would be ideal if the process of translating a BIM model into an FDS inputfile could be automated. While this analysis did not undertake implementation of such a task,consideration was given to the possibility. Automation of the tasks in Figure 9 should bequite possible, albeit highly involved. The manner in which the process would be automatedwill be outlined here, using an external script at the operator running the process:


Figure 9: Work process required to translate a compartment with contents from a sketchupor BIM model to FDS.


(a) Sketchup Model(b) Model Imported into AECOsim and converted

to .DGN

(c) Model imported into Blender from .IFC (d) Model geometry simplified for FDS export

(e) FDS Smokeview Model

Figure 10: Translation of a Sketchup model through AECOsim BIM software and into anFDS model


Figure 11: Process of simplifying geometry. 1. A chair is imported into Blender 2. It isreplaced with its bounding box 3. It is rotated to align with the global xyz axes 4(optional). If the object’s combustible mass is largely gathered in one spot on theobject (e.g. metal-frame chair), the bounding box is shrunk to encompass onlythe relevant combustible region

• Blender can be manipulated from command line, so the script must first import the.IFC file containing the compartment(s) to be analyzed within Blender.

• The script next must be able to parse between which objects in the room are obstructionsand which are combustibles. This could be done using either a naming convention forobjects in the .IFC involving keywords indicating combustion, or a dictionary mappinga particular “combustible item” parameter outlined in the .IFC to the combustibleobject names.

• Next, the script must simplify the geometry automatically. Referencing Figure 11, allparts of such a task are readily performed except the optional step 4, which is a processeasy for a human to handle visually, but difficult for a computer to handle if the objectmodels are not “watertight.” as many models from the Google Warehouse are not.

• Now, the script must reference either the .IFC property files, or an external databaseto locate the thermophysical properties for all obstructions in the room, and appendthem to said obstructions

• As mentioned in section 4.2.3, vents and heat flux detectors must be placed on allcombustibles in the room. Nominally, this can be accomplished by placing them on thetops of all combustibles in the room, but in cases such as a tightly packed bookshelf, itis necessary to place the vent towards an open face. Determining what constitutes aconfined space, and how to place the vent automatically is one problem that requiresfurther study.

• Next, either the .IFC file or an external database must be referenced again to obtainthe burning properties of the combustibles in the room, and these properties must be


assigned to the relevant vents.

• Finally, the .fds input file can be exported and cleaned up for running. The scriptcould conceivably iterate through all combustibles in the room designating each one asitem first ignited to obtain a comprehensive picture of fire scenarios from the room,or a human could afterwards designate a number of representative scenarios to run, ifcomputational time is limited.

It should be noted that the above list is relevant to the process undertaken by this particularauthor. Since an FDS input file is ostensibly a collection of property definitions and geometricvalues, one should be able to write their own custom parser (with considerable effort!) thatcould handle translating a BIM model to an FDS input file, as Dimyadi did [3]. Additionally,there is a software firm, CYPE, that has produced a CAD-integrated FDS module. Theirapproach appears to be to translate the geometry information into FDS from their CADsoftware, and then have users outline fuel packages in a plan layout of the room to be run inFDS.

4.3 Compartment fire risk analysis

This section summarizes the process of taking the results from the FDS model scenarios andcombining them with statistical ignition data and fuel load surveys in office occupancies toproduce a viable fire risk analysis for the NHB office compartment. Fire risk in a compartmentis driven by the probabilities of certain fire scenarios occurring coupled with the potentialseverity of outcomes from those identified fire scenarios.

4.3.1 Severity and loss

A necessary question in this regard is what one’s definition of “severity” is. This definitionvaries depending on the application of the risk analysis, and can vary rather widely, as discussedin [25]. For this report, it was decided that the peak average upper gas layer temperature inthe compartment was an adequate severity measure that served as a convenient surrogate fordamage to the compartment.

Next, peak upper gas layer temperature was mapped to “room damage” by the followingloss function:

Room loss = min(Tmax − 25

600− 25)

where Tmax is the peak average upper gas layer temperature experienced in the compartmentin degrees Celsius. This loss function states that the amount of loss a room experiences isrelated to proportionately how close it gets to the temperature approximation of flashover,with total room destruction occurring in the event of flashover. Certainly, other models ofloss can readily exist, it is a matter of selecting the loss function that most readily answersthe question posed by the analysis.

For the eight representative fire scenarios run using CFAST, Table 5 outlines the lossmapped to the scenario.


Table 5: Loss function mapping of FDS model outputs for eight representative fire scenarios

Ignition Scenario Peak Average Upper Gas Layer Temperature (◦C) Room loss

Printer 95 0.12Pile of paper 44 0.03Computer chair 340 0.55Books (backshelf) 191 0.29Books (frontshelf) 169 0.25Round table 239 0.37Metal-frame chair 46 0.04Box of printer paper 52 0.05

Table 6: Percentage contribution of various materials to combustible mass of compartment forNHB Office and Canadian Office Survey, as well as ignition propensity adjustment

Material NHB office (%) Zalok [7] (%) NHB adjustment

Paper 88 33 2.67Wood 5 54 0.09Plastic 7 13 0.54

4.3.2 Ignition probability

Ignition probabilities for the eight representative fire scenarios were determined using acombination of fire load survey data from offices and national estimates of items first ignitedin Office occupancies.

Table 6 shows a comparison of the combustible mass in the surveyed NHB office comparedagainst Zalok’s estimates for Canadian office occupancies. The NHB adjustment column wascalculated as follows:

Offset =%NHB

%Zalok

where the % designates the percentage contribution of a given category from the subscriptedsource to the combustible mass of the office.

Zalok’s fire load survey represents the most recent examination of office fire loads in recenttime, and Culver found that occupancies in different regions of the U.S. do not appreciablydiffer with distance [4, 7]. Thus, it is a reasonable approximation to take Zalok’s findings ofmean combustible mass contribution in office occupancies to be akin to the “average” officein the nation.

In addition to this, a study was performed examining Texas office statistics to determine theaverage ignition propensity of various categories of materials, matching Zalok. Table 7 providesthese values. NHB propensity comes from adjusting these “average” ignition probabilities forthe “average” office to more effectively fit the actual fire load distribution seen in the office,by multiplying by the adjustment values calculated in Table 6. As can be seen, due to thelarge amount of paper products in the compartment, it has a much higher chance of initialignition of paper products than, say, textiles.


Table 7: Average frequency of materials first ignited for fires occurring in Texas “businessoffices” whose area of origin is an “office,” as well as the adjusted frequencies formaterials in the NHB, based on departure from the “average” office surveyed in [7].Note that frequency is obtained from TEXFIRS evaluation of Type of material firstignited, for 2002-2012 fires in Texas belonging to property use 599 (business office)and area of origin 27 (office).

Material AverageFrequency(%)

NHBadjustedfrequency(%)

Discretefuel pack-ages (#)

Individualpackagefrequency(%)

Paper 15.03 40.13 41 0.98Wood 16.59 1.49 3 0.50Plastic 16.58 8.95 12 0.75

Table 7 supplied the relative frequencies of material ignition in an office. So, given that afire occurs in the NHB compartment, the assertion is that there is a probability 0.413 thatthe fire will involve initial ignition of a paper product within the compartment.

The next assumption necessary is that the probability of an individual fuel packagebelonging to a particular material cateogry being selected from within a material category.For this analysis, the probability of ignition of individual fuel packages given a materialcategory ignition was assumed to be uniform. For example, in the office model there were 41discrete paper fuel packages, and thus the probability of any given one being ignited, givenpaper was the first material ignited, was 40.13/41 or 0.98 percent chance of selection as theinitial item ignited. This is the discrete fuel packages column in Table 7, along with theindividual package frequency.

Finally, the last assumption made was that the representative scenarios explored wereindicative of the fire growth behavior and overall loss that would be associated with any otherobjects in their vicinity and/or type burning. For example, any book blocks burning on theback face of the bookcase would look roughly like the book block scenario, or any desk burningin the room would look roughly the same in terms of loss. Table 8 thus provides a look atthe representative scenarios, their fuel object membership, and thus the net frequency thateach scenario represents. The last column of Table 8 provides the normalized representativefrequencies, scaled to sum to 1, in order to make a proper discrete probability distribution.

4.3.3 Fire risk results

The discrete probability distribution in 8 provides the frequencies that are combined withthe loss values of the fire scenarios outlined in 5 to provide an overall assessment of thecompartment’s fire risk. The most informative summary of the results of this assessmentis to graph the “survivor” function of the room loss, Figure 12. This function graphs theprobability that damage in the room will meet or exceed a particular value of the designatedloss function, given that a fire occurs in the room. Say that one is interested in the probabilitythat a fire occurring in this compartment would, by the stated loss function, result in 0.26 or


Table 8: representative and normalized frequencies for FDS modeled fire scenarios withinNHB office.

Scenario Object membership Rep. frequency (%) Normalized probability

Printer 7 (plastic) 5.22 0.103Pile of paper 11 (paper) 10.77 0.213Computer chair 1 (plastic) 0.75 0.015Books (backshelf) 8 (paper) 7.83 0.155Books (frontshelf) 16 (paper) 15.66 0.310Round table 3 (wood) 1.49 0.029Metal-frame chair 4 (plastic) 2.98 0.059Box of printer paper 6 (paper) 5.87 0.116

Total 56 50.57 1

Table 9: Expected loss and contributions of scenarios to expected loss, in descending order.

Scenario Normalized Probability Loss Expected Loss

Books (frontshelf) 0.310 0.25 0.0775Books (backshelf) 0.155 0.29 0.045Printer 0.103 0.12 0.012Round table 0.029 0.37 0.011Computer chair 0.015 0.55 0.008Pile of paper 0.213 0.03 0.006Box of printer paper 0.116 0.05 0.006Metal-frame chair 0.059 0.04 0.002

Total 1 - 0.17

higher loss. Looking at Figure 12, one can see that the probability of experiencing a loss asbad or greater than 0.25 is roughly 0.23, or a 23% chance.

Two additional boons to possessing this information are the ability to calculate the expectedloss to the compartment, which is simply the sum of the loss multiplied by the associatedprobability, and the ability to rank order the contributions of the representative objects inthe room to the expected loss, for informative purposes. Table 9 provides the results of thesecalculations.

As can be seen from examining Table 9, the expected loss incurred by a fire in the room,given that it occurs in the office, is about 0.17, not particularly severe. Additionally, thetop contributors to this expected loss are the books on the bookshelves in the middle ofthe compartment. This would indicate that mitigating either their ignition propensity orthe severity of a fire involving their ignition would perhaps be the most worthwhile way toimprove this compartment, provided one wished to do so at all.


0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00Loss

Pro

babi

lity

of X

>=

loss

NHB Office fire loss survivor function

Figure 12: Survivor function for NHB office fire risk assessment. “X” in the graph representsthe loss associated with a random fire occurring in the compartment.

4.4 Expanding upon the fire risk analysis

The analysis performed in the preceding section only covered one compartment and only oneset of fire scenarios corresponding to one ventilation condition and excluding any sort of firedetection or suppression. The above analysis is supplied to help outline the methodologysomewhat practically, and to display what results would look like.

The methodology however, is quite robust to increasing complexity. For example, differentventilation conditions and fire suppression situations can be added quite readily, and only theprobability distributions describing, for example, door opening probability or fire suppressionactivation need be further estimated. The problem, however, is that, just as with the eventtrees discussed earlier, the number of computational scenarios required to resolve the lossgrows rapidly.

For example, imagine an analysis where one took into account 3 different ventilationconditions in the room and a sprinkler operating/not operating condition. It would benecessary to supply a probability for each ventilation condition, a probability for the sprinkleroperating or not operating, and 2*3*8-8=40 more FDS scenarios corresponding to thecombinations of sprinkler and ventilation interaction. Thus, while the probabilities mightbe fairly simple to estimate or obtain, the computational burden can be quite daunting.This situation is summarized in Figure 13 Of course, one may continue to make reasonablesimplifying assumptions as well. For example, one might avoid evaluating the small-fire


sprinklered scenarios, instead approximating the loss from them as zero, provided somethinglike water damage was not part of the loss function.

Figure 13: Increasing number of factors accounted for by an analysis increases both complexityand computational burden rapidly.

This is not to say one should not undertake a proper analysis accounting for fire sup-pression systems and varying ventilation conditions or fuel packages, but simply to warnthat increasing complexity demands additional computational resources to maintain feasibleanalysis timeframes.

5 Conclusions

This report has outlined a framework, PROPHET, designed to provide an informed riskanalysis of a building using using a combination of BIM software, fire models, and statisticalanalysis.

BIM software and files, combined with asset management databases, are increasingly per-vasive within the building construction and management fields. The potential for automatingconversion from a BIM model to FDS would have a sizeable impact on the capacity of industryprofessionals to incorporate fire analysis into their design and management considerations ina more meaningful manner. Additionally, proper (or exhaustive) design of fire scenarios could


enable building managers to incorporate fire risk into their decisions regarding compartmentorganization or asset placement in storage within the building.

Finally, this framework, operating on a building, could prove useful to fire departments aswell, supplying them with a much better idea of the kinds of fires they might face within agiven building, where fuel concentrations are located, and which buildings, based on theseevaluations, might merit more detailed or frequent inspections.

6 Acknowledgements

The researchers thank Bentley Systems and The Society of Fire Protection Engineers forfunding this work through the Chief Donald J. Burns Memorial Research Grant. Additionally,the researchers thank Luis Arias and Stalin Armijos for their support during the fire surveyand in building the compartment model.

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