1 NAT'L INST. OF STAND & TECH Reference

NR.qm 82-2551

Evaluating Alternative Strategies

for Reducing Residential Fire

Loss - The Fire Loss Model

^ cations, AlllDb D3bflti3

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Washington, DC 20234

August 1 982

Interim Report

yc

lou

. Ud6

o2-2I351

u s DEPARTMENT OF COMMERCE

NATIONAL BUREAU OF STANDARDS

BATIOWAI

Ot 6TANDA*D»

!,!»A BT SEP 7 1982

NBSIR 82-2551

EVALUATING ALTERNATIVE STRATEGIES S - FOR REDUCING RESIDENTIAL FIRE

LOSS - THE FIRE LOSS MODEL

Alan Gomberg and Benjamin Buchbinder

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Fire Research

Washington, DC 20234

and

Fred J. Offensend

Decision Analysis Group

SRI International

333 Ravenswood Avenue

Menlo Park, CA 94025

August 1 982

Interim Report

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
r''<.'

M

' -^1 .-.> '

.? <

\j,y- ’«||»!

i:m:

•M

“JaaoM aaoj nan 3ht^« aaoJ^

y.

• "^'w’

^i9bnic}rl3u$ nimeinajQ bha

'''*M'' -'^BE ' "' —''

3'JH3WW0t^ 10 TM3M1fflA<l3a .^.U^SSiv

yiofeibbiU Bfrnj^»fen3 icTi<iJ0^v

vbtVi*lr»4^^ <'5-

..k.

f?' )i,:oi-:.

%'J^SO$, 3^6 ,nc>*i3f<*#«fiW

'IP

f?SO#’

I#'*'

V'>y»

' >a^QoAj

L'®

..'IWWaV.i

ii!®'* ^-

Si.U

A :m imi- fl: K- ^
TABLE OF CONTENTS

Page

LIST OF FIGURES iv

LIST OF TABLES v

Abstract 1

1. INTRODUCTION 1

2. OVERVIEW OF THE RESIDENTIAL FIRE DECISION ANALYSIS FRAMEWORK 2

2.1 Demographic Model 3 2.2 Alternative Selection and Implementation Model 3 2.3 Fire Loss Model 5 2.4 Cost Model 6 2.5 Value Model 7 2.6 Calculation of Cost Plus Loss 7

3. LOSSES UNDER CURRENT CONDITIONS 7

3.1 Ignition Probability 8 3.2 Probability of Fire Being Reported 8 3.3 Fire Scenario Probabilities 8 3.4 Detector Presence Probability 9 3.5 Fire Size at Start of Suppression Activity (Suppression Size) .... 10

3.6 Extent of Flame Damage Probabilities 11

3.7 Loss Assignments 12

3.8 Calculation of Losses Under Current Conditions 12

4. LOSS REDUCTION UNDER THREE ALTERNATIVE INTERVENTION STRATEGIES 12

4.1 Residential Sprinkler Systems 13

4.2 Smoke Detectors 15

4 . 3 Joint Installation of Sprinklers and Smoke Detectors 17

4.4 Comparison of Relative Benefit of Alternatives . 17

5. SENSITIVITY ANALYSIS 18

5.1 Detector Performance . 18

5.2 Loss Assignments - Unsaveable Human Loss ...... 18

5.3 Sprinkler Effectiveness 19

5.4 Unsaveable Human Loss/Sprinkler Effectiveness 19

5.5 Detector Impact on Unreported Fires . 19

6. CONCLUSIONS 22

7. REFERENCES 23

APPENDIX A. PROBABILITY TREE CALCULATIONS 57

iii
LIST OF FIGURES

Page

Figure 1. Schematic of the Residential Fire Decision Analysis Model .... 24

Figure 2. Components of Demographic Model 25

Figure 3. Components of Alternative Selection and Implementation Model . . 26

Figure 4. Schematic of Fire Loss Model 27

Figure 5. Probability Tree for a Single Scenario 28

Figure 6. Probabilities for Suppression Size Given Extent of Damage .... 29

Figure 7. Unsaveable Fatality Assessment Worksheet: Sprinkler

Alternative 30

Figure 8. Unsaveable Injury Assessment Worksheet: Sprinkler Alternative . 31

Figure 9. Attenuation Assessments - Sprinkler Alternative, New Homes ... 32

Figure 10. Detector Presence Probabilities after Attenuation - Detector

Alternative 33

Figure 11. Unsaveable Fatality Assessment Worksheet: Detector Alternative . 34

Figure 12. Unsaveable Injury Assessment Worksheet: Detector Assessment . . 35

Figure 13. Detector Presence Probabilities after Attenuation - Sprinkler

plus Detector Alternative 36

Figure 14. Unsaveable Fatality Assessment Worksheet: Sprinkler/

Detector Alternative 37

Figure 15. Unsaveable Injury Assessment Worksheet: Sprinkler/Detector

Alternative 38

Figure 16. Model for Calculating Detector Influence on Ignition and

Reporting Probabilities 39

Figure 17. pq = 0.00286 40

Figure 18. Sensitivity to Effect of Detectors on Unreported Fires 41

IV
LIST OF TABLES

Page

Table 1. NFIRS Codes for Fire Scenarios 4 2

Table 2. Description of Fire Scenario Groups 44

Table 3. Detector Presence Probabilities 45

Table 4. Suppression Size Assessments - Sprinkler Alternative 46

Table 5. Nominal Loss Reductions for Three Alternatives 47

Table 6. Sensitivity Analysis of Detector Performance Probabilities - Frequency of Nonfunctional Detector 48

Table 7. Sensitivity Analysis of Unsaveable Assessments 49

Table 8. Suppression Size Assessments - Sprinkler Alternative 50

Table 9. High Suppression Size Assessments - Sprinkler Alternative 51

Table 10. Low Suppression Size Assessments - Sprinkler Alternative 52

Table 11. Sensitivity Analysis of Sprinkler Effectiveness Assessments

(Effect of Sprinklers on Suppression Size Parameter) 53

Table 12. Sensitivity Analysis of Unsaveable Assessments and Sprinkler

Effectiveness Assessments Combined 54

Table 13. Base Case Fire Loss Rates One and Two Family Dwellings 55

Table 14. Sensitivity Analysis of Detector Effect on Unreported Fires .... 56

V
-.'fv-hV.*

"?'!r Mp *

., '•'V*' * .. ^

;>

'tsv-l^

!!*“' „. 'aaiWa«.

r. . ^r<: ^mfrnk ftC Min?® •-

' 1 .«- > • J

. -• • ‘il .•' » — •- . r'-'V * ^V-%-' .'5. V

ti .1* ijsg, '• ".' -V’^- < i

"j^vyrvT *1

At,tc-r»t#lt40'‘ 4 tflfj Al^v y' timn ' ?e

1' &vi::»6fl‘iiai:#'XA - sJ;9«rtifsaaei©^

.iW-*- ' > , J- ’*./](*-

i-v.7'h-> ir. . un f #d^i*«p)Ei«se»Ai-.:t<

. ce ^ .* .; * ^(’SBs&m

Vvi. ir^ iiv'**'

sJ;9^saaei©^

*>1X» ^ £

,

' vv: " ^ _.. . 22 ...,», * S^/tjfc.OswQ

?£«jU'-- 1-I. -l*}, '^. 'F A Y

r-Mtuft 15, ' Oofs^kyfoi^^w u ..„

jj?

• . l%«>t rto- 4945ft^' .7^iib«dN«T;. to
EVALUATING ALTERNATIVE STRATEGIES FOR REDUCING RESIDENTIAL

FIRE LOSS - THE FIRE LOSS MODEL

Alan Gomberg, Benjamin Buchbinder and Fred L. Offensend^

Abstract

This report provides a preliminary documentation of a decision analysis framework for evaluating alternative

residential fire loss reduction strategies. The framework,

when it is completed, will provide a systematic means for

assessing the costs and losses occurring under different

intervention strategies. The current report focuses

entirely on the problem of assessing fire losses, as this

is where most of the uncertainty on system performance

occurs. Subsequent reports will address the cost of the

alternatives, after which the alternatives can be compared

on a comprehensive cost/benefit basis.

Three alternatives are considered in this preliminary

report: smoke detectors, residential sprinkler systems with

standard commercial-type sprinkler heads, and a combination

of both measures. Based on the preliminary input data

developed, the preliminary analysis indicates that both

sprinklers and detectors are effective in reducing life

loss. Detectors appear to be somewhat more effective in

reducing personal losses, however, because of their earlier

warning capability. Sprinklers appear to be significantly

more effective than detectors in reducing property loss

because of their earlier start in initiating suppression.

Work is underway refining the loss model and developing a cost model so that meaningful cost/benefit comparisons of

the alternatives can be conducted.

Key words: Cost/benefit; decision analysis; fire loss; fire

safety; residential; smoke detectors; sprinkler systems.

1. INTRODUCTION

This report describes a decision analysis framework for evaluating residential

fire loss reduction strategies. Motivation for the framework derives from the

magnitude of U.S. residential fire losses and the cost of potential intervention

strategies for reducing those losses. Reported residential fire losses are currently

estimated at 6400 deaths, 61,000 injuries, and $2.4 billion in property loss per

^Fred Offensend works for SRI International, 333 Ravenswood Avenue, Menlo Park,

CA 94025.

1
2

* Pot©ntial loss reduction ineasures are available, but they are expensive.

Before advocating implementation of these measures on a national basis, assurance

must be obtained that they are cost- justifiable . It is the purpose of this report

to develop a decision analysis framework for evaluating the cost-effectiveness of

alternative residential fire loss intervention strategies.

The decision analysis methodology provides a framework for identifying decision

alternatives, establishing outcome measures, and developing quantitative models for

evaluating the consequences of various alternatives, given the information available.

It has been successfully applied to several fire safety problems to date, including

upholstered furniture fire safety [2]

Probabilistic methods are used to address

uncertainty, and explicit value judgements are introduced to facilitate comparison

of alternatives. Although sophisticated mathematical techniques are often employed

in a decision analysis, the methodology is not a "black box" for producing results.

Rather, it is a process for providing decision makers with insights into the conditions and relationships -- technical, institutional, or behavioral — that are important in safety related decision-making.

The residential fire decision analysis framework is still in a state of development.

However, the model is sufficiently far advanced that it can be used to carry out

pilot evaluations of a few selected loss reduction strategies. Thus the purpose of

this report is to provide an interim documentation of the decision analysis framework

as used to perform preliminary benefit calculations on three specific alternatives

in one and two family dwellings:

- residential sprinklers (using conventional commercial type sprinkler heads)

- smoke detectors

- conventional sprinklers and smoke detectors in combination.

We actively solicit comments and criticisms on how the model might be refined and

extended to evaluate more fully the alternatives considered and to include a wider

range of loss reduction alternatives.

2. OVERVIEW OF THE RESIDENTIAL FIRE DECISION ANALYSIS FRAMEWORK

The residential fire decision analysis framework provides a systematic approach

for assessing the consequences of alternative loss reduction strategies. As shown

in figure 1, the framework has five basic components. The demographic model specifies

housing population and human characteristics of the population under consideration.

The alternative selection and implementation model specifies the loss reduction

strategy under consideration and calculates the extent to which the mitigation

measure will be implemented and made to operate as designed. The fire loss model is

used to calculates the annual residential fire losses for the target population

2 Numbers in brackets refer to the literature references listed at the end of this

paper

2
in question, given a loss reduction strategy and its degree of implementation. The

value model introduces explicit value judgments to convert the physical losses

calculated by the fire loss model to economic terms. The cost model calculates the

total economic cost of the loss reduction strategy under consideration. Finally,

the economic value of the losses is added to the economic cost to give the cost plus

loss for the alternative in question. The "best" alternative is defined as the one providing the minimum cost plus loss to society.

The overall decision analysis framework is modular in format. The various submodels are designed so that they can be replaced with more complex models, or expanded, as the need arises. In this initial presentation we have focused primarily

on the fire loss model since it most directly affects the evaluation of the alternatives examined.

Paragraphs 2.1 through 2.6 summarize the basic features of the various submodels .

2.1 Demographic Model

The demographic model specifies the target population of U.S. residences under

consideration. The model can be set to include all U.S. residences or it can be

3 made to delineate a specific type of residence. Figure 2 gives a schematic of the

kinds of factors that can be used to specify the target population. Residences are described by the housing type, size, and geographic location (which in turn provides

an indication of the underlying fire risk) . The occupants are characterized according to population density, socio-economic status, and ownership status. Other

demographic factors can be added as the need arises to focus on particular populations. In particular, we may add some dynamic factors to the demographic model to

describe more realistically anticipated changes in housing and population

characteristics

2.2 Alternative Selection and Implementation Model

The alternative selection and implementation model specifies the loss reduction

strategy to be analyzed and the degree to which it is successfully implemented by

the target population. A schematic of the model is given in figure 3. At present,

the strategies under consideration are detectors, sprinklers, and a combination of

both. Additional strategies for possible subsequent consideration include building

standards, product design standards, fire prevention programs, and enhancement of

fire service capabilities.

3 Figure 2 indicates the different values that each variable can take on. We use the

notation of a circle to indicate that there is uncertainty over which value the

variable will take on. The circle is called a chance node. In subsequent figures

(e.g., figure 3) there are variables such as the research funding level over which

the decision-maker has control. We use the notation of a square to indicate that

the decision-maker choses the value to assign to the variable. The square is

called a decision node.

3
Each loss reduction alternative can be implemented in several ways. The user

(decision-maker) must first decide whether to use the procedure in its current state

of development or whether to do further research or redesign. This decision is

represented by the second factor in figure 3. Uncertainty on the research or redesign outcome is represented by the third factor. For simplicity, we have limited

the two research or redesign outcomes to "success" and "failure", but in later

refinements of the model, we can include a more detailed range of outcomes.

Given a particular research or redesign strategy, the decision-maker must also

decide how to implement it. As indicated by the fourth factor in the figure, the

implementation choices range from mandatory federal policies to free market adoption

of the loss reduction strategy. Intermediate options requiring government effort

might include support programs to help acquire or install the measures, encouragement of local regulation, and tax incentives. In the analysis of specific loss

reduction strategies, these implementation choices would be specified in more detail

to give the exact provisions of the specific procedures.

Merely specifying a target population, loss reduction strategy, and implementation

policy does not guarantee that the reduction strategy will be implemented as intended.

Several factors combine to attenuate the effectiveness of the loss reduction strategy,

as indicated by the remaining factors in figure 3. The first attentuation factor is

the product effectiveness rate. This rate gives the probability that the product

will be in operating condition when delivered to the final user. Reasons for

attentuation may include materials defects, manufacturing problems, or shipping and

storage difficulties. It is clear that this probability depends on the loss reduction

strategy and on the extent of research and design undertaken before product development. It does not depend on the implementation strategy selected. The availability

of the loss reduction technique must be taken into account. Reasons for attenuation

in this area might include inadequacies of a product distribution network, reluctance

on the part of distributors to handle or promote a product, regulatory constraints

that prevent certain persons in the target population from participating in the

program, or lack of trained people to carry out the deployment program. A third

attenuation factor is the degree of user acceptance, defined here as purchasing and

installing the loss reduction procedure under consideration. This factor is important

in determining the overall effectiveness of the loss reduction strategy, because

regardless of how effective the strategy is under ideal laboratory conditions, it

will have little effect on ultimate fire losses if it is not installed by the

target population. A fourth source of attenuation is the misuse or misapplication

that occurs under field conditions. Many of the loss reduction techniques require

frequent testing and maintenance to assure operation as intended. An example of

loss of effectiveness due to field effectiveness attenuation is the clogging of a sprinkler line due to corrosion and lack of maintenance.

4
The effect of the four attenuating factors just described is to reduce the

number of residences in the target population in which the loss reduction strategy

in question is installed and operating correctly. The number of such residences is

then used as a basis for calculating the effectiveness of the loss reduction strategy.

In this initial formulation of the decision analysis model, we assume that the

strategy will either be fully operative or inoperative in a particular residence,

depending on whether attenuation criteria were met. In later refinements of the

model, we can allow for intermediate levels of partial operation.

2.3 Fire Loss Model

The fire loss model calculates the annual fire losses for the target population,

given the particular loss reduction alternative and level of implementation as specified by alternative selection and implementation model. The losses for the

target population are calculated in two parts: losses for that portion of the

target population that does successfully implement the alternative and losses for

that portion that does not successfully implement the alternative. The losses for

the portion of the population that does not successfully implement the alternative

are assumed to occur at the same rate as for current conditions (without the alternative in place) . Adding the losses for the two portions of the population gives

the losses for the entire target population.

The difficulty in assessing fire losses under a new loss reduction strategy is

that there is generally no field experience to determine the effectiveness of the

alternative. Instead, judgments and extrapolations must be made based on the results

of laboratory tests and other field data. To facilitate the assessment of fire

losses under an unproven alternative we introduce the probability tree presented in

figure 4. The tree delineates a number of conditions under which various alternatives

might perform, represented by five factors. By assigning an appropriate number of

values to each factor, it becomes possible to make reasonable assessments on how an alternative might perform.

The pilot residential fire loss model presented in figure 4 introduces five

factors to delineate the conditions under which an alternative might perform. The

model first specifies whether an ignition occurs in a residence in the target

population during the year in question, and given an ignition, whether the fire is

reported to fire authorities. Our present fire loss model considers only reported

fire losses, but in later refinements of the model we plan to include unreported

fires

Given a reported ignition, the second factor specifies the type of fire scenario,

that is, the characteristics (and in some cases the area of origin) of the fire.

For simplicity, the pilot model aggregates 26 different scenarios into 9 scenario

groups. These scenarios and scenario groups are described in Section 3.

5
Given a reported ignition of a particular scenario/ the fire loss model then

specifies whether there is a functional smoke detector in the residence. To facilitate data aggregation, the model allows for three detector presence outcomes;

functional detector in operating condition, installed detector in nonoperable

condition (nonfunctional) , and no detector present.

The fourth model factor specifies the size of fire development at the time

meaningful suppression efforts are begun. Meaningful suppression efforts are defined

to include manual or automated suppression efforts that would have a reasonable

chance of arresting the fire development. Three possible stages of fire development

and start of suppression are considered; fire confined to the object of origin (0)

fire greater than the object but confined to part of the room of origin (<R)

and

fire equal to or greater than the room of origin (^R)

Finally, given a reported ignition of a particular scenario and a specification

of detector presence and size of fire at initiation of suppression, the model allows

for an assessment of the final extent of flame damage. The same three possible

levels of flame damage are considered; 0, <R, and ^R. The three fire stages for the

last two factors were selected to reflect the stages in the fire growth at which the

alternatives might impact. It was not considered relevant to distinguish between

fires of full room involvement and those of still greater involvement.

Each of the variables in figure 4 can take on the different values indicated on the branches in the figure. Combining the different branch values provides a large

number of paths through the tree. Associated with each path through the tree is a level of loss. Probabilities are assigned to the branch values for each factor.

Probability assignments are discussed in detail in subsequent sections. Multiplying

the probabilities for the different branches comprising a given path gives the

probability for that path. Associating the probability for each path with the path

outcome then enables a probability distribution to be constructed showing the range

and likelihood of different loss outcomes. The probability distribution is the

logical implication of the uncertainties on the six input factors to the fire loss

model.

2.4 Cost Model

A cost model must develop the total implementation cost of the loss reduction

measure under consideration. All research, installation, operation, maintenance,

and enforcement costs must be included in the cost ‘calculation. Where costs are incurred in different years, they should be annualized at a nominal discount rate so that the costs can be compared on a consistent basis. In this initial version of

the residential decision analysis framework costs of the alternative are not addressed,

nor is the distribution of costs versus benefits for the population as a whole. In

subsequent work, we will address detailed costs, and develop cost plus loss criteria.

6
2.5 Value Model

The output of the fire loss model gives the number of deaths and injuries and

amount of property damage under the intervention strategy in question. In principle

the decision-maker could compare these different outcomes with the cost of the

alternative to determine whether the loss reduction measure is cost- justifiable

The problem with this approach, however, is that it is difficult to ensure con- sistency in value judgments from one decision problem to another, as there is no widely accepted basis for direct comparison of the "value of life" to the dollar

value of property. In one situation an alternative might be selected that implies a value of saving a human life of $1,000,000. In another situation, the implied value

might only be $10,000. Such inconsistencies could lead to an inequitable allocation

of resources, since in one situation an alternative would be accepted which would be

rejected in another situation.

To ensure consistent value trade-offs from one decision problem to another, we

introduce an explicit set of value trade-offs to convert the various categories of

personal loss to economic terms. The trade-offs give the dollar amount that society

is willing to pay to avoid a fire death or burn injury. Converting the personal

losses to monetary terms then allows the non-monetary losses and economic costs to

be compared on a common scale.

We recognize the difficulty of determining a value for saving human life. It

is doubtful that a consensus for these values will ever be reached among all members

of society. Ignoring the trade-off problem will not eliminate the problem, however,

because value trade-offs, either implicit or explicit, are made every time an expenditure is made (or denied) for public safety.

2.6 Calculation of Cost Plus Loss

The monetary equivalent of the fire losses is added to the economic costs to

give the cost plus loss under the mitigation measure in question. All time streams

of costs and losses are annualized to equivalent annual amounts of costs and losses,

so that they can be compared on a consistent basis. From an economic standpoint

the most attractive alternative is the one with the least total cost plus loss,

since any other alternative would cost society more, whether that cost be an economic

expenditure or dollar equivalent of losses.

3. LOSSES UNDER CURRENT CONDITIONS

We first use the decision analysis framework to calculate total residential

fire losses under current conditions. Setting the models to reflect current conditions provides a known and measureable reference for analyzing the alternatives.

Once the models have been calibrated to reflect current conditions, the individual

parameters can be adjusted to describe the performance of the various loss reduction

strategies

7
This section therefore begins with a presentation of the input data used to

model current conditions and concludes with a summary of the results. Wherever

possible we have used statistical information from the National Fire Incident

Reporting System (NFIRS) and other data sources to establish the input data.

However, if the desired data were not available, or if there are known biases in

the data, then we used expert judgment to develop the input data, since that is the

best information available to us. Whenever possible, judgment is supported by some type of data, often data which are not directly applicable to the model but which

set bounds on, or assist in, making judgments.

3.1

Ignition Probability

The U.S. Fire Administration (USFA) estimates that there were 32,690,000 fires

in the U.S. during 1978 [1]. Of these fires, 26 percent, or 8,499,000 fires, were

estimated to be residential fires. Seventy-three percent, or 6,204,000 of the

residential fires were estimated to occur in one and two family dwellings, the

occupancies of interest in this study. Comparing the number of one and two family

dwellings that had a fire in one year with the total number of such dwellings,

61,000,000 (estimated from Census tabulations), gives a probability of ignition of

6,204,000/61,000,000 = 0.102.

3.2

Probability of Fire Being Reported

Of the 32,690,000 estimated fires in 1978, the USFA reports that 2,690,000

ignitions were reported to the fire service. Thus, the probability of an ignition

being reported to the fire service is 2,690,000/32,690,000 = 0.082. For simplicity,

until better data become available, we assume that this probability applies to one

and two family dwelling fires, as well as to the population of all fires for which

it was calculated. Multiplying the probability of a fire in a one or two family

dwelling by the probability that it is reported gives a probability of 0.008 that

there will be a reported fire in a one or two family dwelling in a given year.

3.3

Fire Scenario Probabilities

A primary difficulty in planning for residential fire safety is that there are

so many different kinds of fires, in terms of such factors as ignition source,

materials ignited and location. Since the effectiveness of loss reduction strategies

depends on the kind of fire, or scenario, the fire loss model explicitly includes

the probabilities of the different fire scenarios. As a starting point we define

26 different fire scenarios which are identified using the NFIRS encoding format

[3] as specified in table 1. The fire scenarios are defined in a hierarchical way.

That is, if a fire can be classified into one of several scenarios, it is assigned

to the lowest numbered category. Thus, for example, a flammable liquid fire

occurring at the exterior of the dwelling is classified as an exterior fire (scenario

no. 1) , and not a flammable liquid fire (scenario no. 5) . The categories are arranged so that the more critical a scenario is, in terms of differences between

8
it and other scenarios in response to the loss reduction measures being considered,

the lower its number is, and the higher it occurs in the hierarchy. Exterior fires,

for example, are scenario no. 1 because they are not significantly affected by any

alternatives, which are concerned only with interior fires. In fact, review of the

scenario components reveals very little overlap between scenarios.

For ease in computation we collapse the 26 scenarios into a smaller number of

groups, where the scenarios within each group have similar characteristics with

respect to response to the loss reduction measures being studied. For the purpose

of the alternatives investigated in this phase of the study, it is possible to

collapse the 26 scenarios into the nine groupings identified in table 2. The table

gives the nine scenario groups, the various scenarios which make up each group, and

the probability for each group.

The probabilities of fire scenario groups as given in table 2 are for reported

fires only, based on NFIRS data. Unless otherwise indicated, the remaining probabilities given in this section are for reported fires only. In later applications

of the decision analysis model, we will develop input probabilities for unreported

fires

3.4 Detector Presence Probability

Three possible states of detector performance are considered by the decision

analysis model; detector present and operable (functional) , detector present and

did not operate (non-functional) , and no detector present (none) . The NFIRS data

base includes these categories, but subdivides the "detector present" category to

detector present in the room and detector present in adjoining space. For simplicity

we have combined these two detectors present categories as well as the category

"detector present, but fire too small or remote to operate" into one category of

"functional" detector present.

In reviewing the NFIRS data, however, it is apparent that a number of fires

were classified as "detector present but did not operate" when the fire was too

small or remote to actuate the detector. To reflect more realistically the fraction

of detectors that actually function, we have reviewed the fire scenario and final

extent of flame damage for the fires nominally classified as "did not operate" and

shifted those fires for which the fire was too small or remote to activate the

detector to the "functional" category. Table 3 describes how each detector state

was defined and gives the final probabilities of detector presence as used in this

report

9
3.5 Fire Size at Start of Suppression Activity (Suppression Size)

The size of fire at the start of suppression activity is an important variable

because it represents a lower bound on possible fire growth limitations. The NFIRS

data base does not give probabilities for this variable because it only gives data

for the fire conditions after the fire was extinguished. Therefore we used expert

judgment to assess the size of the fire at the start of suppression activity.

The size of fire at the start of suppression activity is most readily assessed

as conditional on detector presence and final extent of flame damage. The conditional

assessments can be left dependent on detector performance because the suppression

size variable follows the detector presence variable in the probability tree (figure

5) . However, the assessments conditioned on final extent of flame damage must be

collapsed, because the suppression size variable used in the probability tree

precedes the final extent of flame damage.

As an example of how the probability assignments are coalesced, the probability

of a particular initial suppression size is equal to the probability of initial

suppression given a particular final extent of flame damage, multiplied by the

probability of that extent of flame damage, summed over all final extents. Thus,

the probability of the initial suppression activity occurring when the fire was

confined to the object is:

P(Og) = P(0g/0j) P(Oj) + P(Og/<Rj) P(<Rf) + P(Og/>Rj) P(>Rf)

where

P(x/y) = the probability that x occurs given that y has occurred, and

Og = initial suppression activity occurs when fire is confined to object

Oj = final extent of flame damage is confined to object

<R^ = final extent of flame damage is greater than object but confined to

part of room

>R^ = final extent of flame damage is equal to greater than room

The same basic equation holds for initial suppression sizes <R and >R_, with

s s <R or >R substituted for 0 . Also, the same equation applies for the different

detector presence states; the various assessments must merely be conditioned on the

detector state under consideration. The assessments for suppression size probabilities were made by the project team and are given in figure 6. For scenarios A

or B and for the "no detector" path, for example, the assessments indicate that if

the eventual fire damage is equal to or greater than the room of origin, the probability that suppression started in the object of origin is 0.02. The probability

that suppression started when the fire was beyond the object, but still confined to

part of the room is assessed at 0.04, and the probability that suppression started

at full room or greater involvement is 0.94. For these relatively severe scenarios,

the assessments imply that suppression activities starting before the room is fully

involved will be successful in preventing the fire from growing to full room or

10
greater size in all but 6 percent (0.02 + 0.04) of the cases and for the least

severe scenarios (C, F, G, H) will do the same in all but 3 percent (0.01 + 0.02) of

the cases.

Similar assessments are made for those cases wherein the eventual fire damage

is confined to part of the rocm of origin. For Scenarios A or B the probability

that suppression activity started in the object of origin, given that the final

extent of damage is part of room is assessed at 0.30. The remaining 0.70 is therefore

the probability that suppression started at the same level as the extent of damage,

part of room. If the final extent of damage is the object of origin, the suppression

must, of course, have started in the object of origin.

The assessments made for the no detector paths are used for the non-functional

detector paths as well; similar assessments (in parentheses) were made for the

functional detector paths. Calculated probabilities are given in appendix A.

3.6 Extent of Flame Damage Probabilities

The probabilities of final extent of flame damage can be calculated directly

from the NFIRS data base. These probabilities are conditioned on scenario and

detector presence, but not size of fire at initial suppression. Thus, these probabilities must be adjusted to reflect the dependence on initial suppression size.

Using the definition of conditional probability, the probability of final

extent of flame damage given initial suppression size can be expressed as:

P(F/S) = Jllfi

P(S/F) P(F)

P(S)

where

P (xy) = the probability that x and y occur together, and

F = final extent of flame damage (0, <R, >R)

S = size of fire at initial suppression (0, <R, >R)

The probabilities "P(S/F)" are assessed subjectively by the project team. The

probabilities "P(F)" are taken directly from the NFIRS data base. The probabilities

"P(S)" are the probabilities for initial suppression size as calculated in the

previous section. All probabilities here are conditioned on detector presence and

scenario, but for clarity of notation, we have dropped this dependency.

The desired probabilities of final extent of flame damage, conditioned on scenario, detector presence, and initial suppression size can therefore be calculated

according to the above equation. An example of the probability assignments for

these dependencies is given in appendix A.

11
3.7 Loss Assignments

The NFIRS data base gives property and human losses as a function of scenario,

presence, and final extent of flame damage. For the purpose of calculating

losses under current conditions we can use these statistics to represent the average

losses under all three initial suppression sizes, since current losses can be

directly related to extent of flame damage. In subsequent sections, where we investigate loss reduction strategies that affect human losses independent of extent

of flame damage, we must further subdivide the loss rates. However, we defer those

adjustments until the actual alternatives are considered. An example of loss rates

by scenario, detector presence, and final extent of flame damage is given in appendix

A.

3.8 Calculation of Losses Under Current Conditions

The fire loss model is employed using the parameters just described to calculate

the expected losses under current conditions. The results show that the average

reported one and two family residential fires in this study results in 0.00632

deaths, 0.066283 injuries, and $4479 in property loss. These results correspond

closely with the respective loss rates calculated directly from the NFIRS data base.

The significance of the calculation is not so much that we have calculated the

magnitude of residential fire losses for the first time; they could have been

calculated from the NFIRS statistics directly. The significance is that we have

conditioned assessment of residential fire losses based on a set of variables that

later can be used to model the performance of as yet unproven loss reduction

strategies. In the following sections we will use this base case assessment of the

conditioning factors as a reference point for modeling the performance of new loss

reduction strategies.

4. LOSS REDUCTION UNDER THREE ALTERNATIVE INTERVENTION STRATEGIES

In this section we demonstrate the use of the decision analysis model with a preliminary evaluation of three intervention strategies. We consider loss reduction

strategies involving installation of sprinklers in newly constructed housing,

installation of smoke detectors in all new and existing housing, and a combination

program involving both sprinklers and detectors in newly constructed housing. As

the overall df^cision analysis model is still being developed, this demonstration

analysis focuses only on the loss reduction capabilities of the three alternatives.

12
4.1 Residential Sprinkler Systems

The sprinkler alternative is defined as follows:

° Sprinkler Alternative - A conventional sprinkler system, utilizing standard

commercial type sprinkler heads, installed in all dwelling spaces except

baths, closets, attics, garages and similar spaces. The newly designed

"fast response" residential sprinkler heads are not evaluated in this

alternative but will be the subject of a future evaluation. The sprinkler

option is based on installation in new homes only, under a mandated system.

Detectors are not installed as an adjunct to the system, but are assumed

to be present at the current national rate based on voluntary and locally

mandated detector installation.

It is important to note that the benefits of this alternative apply only

to new hemes as projected for the future.

We first examine the effectiveness of sprinklers given that they are installed

and operating properly. We then analyze the effect of the various attenuating

factors that combine to make sprinklers work less effectively than in the ideal

case.

The effect of sprinklers is to shorten the time to effective suppression and

thus contain fires at a smaller size. In terms of the residential fire decision

analysis model, the effect of sprinklers is to increase the probability that effective suppression will begin when the fire is small (correspondingly decreasing the

probability that suppression will begin when the fire is large) . We can therefore

model the loss reduction potential of sprinklers by adjusting the probability of

fire size at the start of initial effective suppression.

Sprinklers have different degrees of effectiveness in limiting fire spread,

depending on the scenario. For example, sprinklers would be quite effective in

limiting fire spread in living spaces that are protected by sprinklers, but would

have less effect on fires that start in areas that are not protected by sprinklers

(e.g. closets and crawl spaces). Thus, different assessments on time of initial

suppression must be made for different scenarios.

Table 4 shows how the base case probabilities of fire size at time of initial

suppression are changed to reflect the presence of sprinklers. The adjustment rules

were subjectively developed by the CFR/SRI project team based on their combined

experience, and on considerations involving the relative locations of the fires and

nearest sprinkler heads, and the ignition characteristics. As an example of the

adjustment rules, the table shows that for interior living space fires (Scenario G)

and for fires in which the initial object ignited is not large (e.g. wastebasket),

the probability of suppression size >R is reduced by 100 percent under the sprinkler

13
3-ltsirn3.tive . Nin©ty p©rc©nt of th© ”>R” probability is add©d to th© original "<R"

P^ot^^bility and th© oth©r 10 p©rc©nt to th© "0” probability ^ bocaus© a r©lativ©ly

small initial obj©ct is l©ss lik©ly to activat© th© syst©m b©for© oth©r mat©rials

b©com© involv©d.

Whil© prop©rty loss©s ar© dir©ctly r©lat©d to final ©xt©nt of flam© damag©,

human loss©s ar© oft©n not dir©ctly r©lated to final flam© spr©ad b©caus© th© loss©s

may occur at an ©arly stag© in th© fir©. Prop©rty loss©s for this alt©rnativ© ar©

d©v©lop©d by using th© d©cision analysis mod©l to calculat© th© r©vis©d distribution

on ©xt©nt of flam© damag©, giv©n th© n©w suppr©ssion siz© probabiliti©s , and th©n

r©lating th© final ©xt©nt of flam© damag© to ©xpect©d prop©rty losses according to

th© loss experience reported in the NFIRS data base.

In those situations where human loss may b© independent of extent of flam©

damage, and where th© alternative has th© effect of reducing final extent of flam©

damag© only, it is not appropriate to us© th© extent of flame damag© to model human

losses. To address this problem w© divide the population of potential victims into

two groups: those that could possibly be saved by the intervention strategy are "saveable", and are dependent on the final extent of flame damage, and those that

would have no chance of being saved by the alternative are classed as "unsaveable"

A casualty occurring due to clothing ignition is an example of an individual who

would be classified as "unsaveable" under the sprinkler alternative, because the

sprinkler would not be activated until the fire was large enough to cause the

casualty

Separate assessments of the unsaveable rate are made for injuries and deaths

because sprinklers impact differently on different kinds of losses. Similarly,

different assessments are made for different intervention strategies. Once the

unsaveable assessment is made, we set aside the unsaveable population. Human losses

for the saveable population are calculated by multiplying the revised distribution

on extent of flame damage by the base case loss rates for each extent of flame

damage category. The losses calculated for the saveable population are then added

to the losses set aside for the unsaveable population to give the total human losses

under the alternative being considered.

The saveable/unsaveable assessment is made according to the casualty status at

time of ignition and the casualty action at time of injury. Figures 7 and 8 give

the worksheets that were used to calculate the unsaveable fatalities and injuries

respectively for the sprinkler alternative. The figures give nine possible casualty

statuses and eight possible casualty activities. The numbers in the matrix entries

give the number of observed casualties of the designated type in the NFIRS data

base. The shaded entries indicate the casualty types that would be unsaveable under

the sprinkler alternative. Dividing the shaded matrix entries by all of the entries

results in 32.6 and 13.2 percent, respectively, of the current fire deaths and

injuries being judged to be unsaveable under the sprinkler alternative.

14
Our analysis of sprinkler effectiveness must account for the possibility that

sprinklers will not be installed or that they will not perform as intended. As a preliminary assessment of that attenuation, we cohsider the four attenuating factors

shown in figure 9. For example, for mandatory regulation in new homes we assume

that 0.999 of the sprinkler systems will be correctly manufactured at the factory.

We assume that 0.95 of all residences will find sprinklers available for purchase,

and that 0.97 of those residences will have the systems installed. Finally, we

4 assume that 0.92 of the sprinkler systems will be properly maintained and will

operate effectively. The net effect of these four attenuating factors is that 84.7

percent (0.999 x 0.95 x 0.97 x 0.92 = 0.847) of all new homes are assumed to have

operative sprinkler systems under the sprinkler alternative in this analysis. Since

statistics to support these assumptions are not available, the sensitivity of cost

plus loss comparisons of alternatives to these assumptions must be determined before

decisions on alternative selection are made.

Total losses under the sprinkler alternative are calculated in two parts. If

the home does not have an operative sprinkler or if the occupant classification is

"unsaveable" , then the loss rate is set equal to the current loss rate. If there is

a working sprinkler system and if the occupant is classified as "saveable" then we

use the model to calculate the revised distribution of final flame extent given the

suppression activity of the sprinkler. The human losses for this latter group are calculated as a function of final extent of flame damage, using the loss rates

observed under current conditions. The losses for the two population groups are then summed to give total losses under the sprinkler alternative.

The input data for modeling sprinkler losses consists of the base case data

adjusted to reflect the suppression size probabilities for the sprinkler system

(table 4) , the assessments on the size of the saveable population (figures 7 and 8)

and the attenuation factors on effective installation (figure 9) . Using this data

base the model calculates reductions for the sprinkler system alternative of about

34, 17, and 38 percent in deaths, injuries and property losses respectively compared

to current conditions. We will return to these reduction rates in a later section

to examine how the reduction rate changes with changes in the input data.

4 . 2 Smoke Detectors

A second alternative for reducing residential fire losses is to require

installation of smoke detectors in all old and new residences.

^Note that this estimate of reliability is somewhat lower than that usually given

for commercial occupancies, due to the likelihood that residential installations

may be less controlled and inspected less regularly than commercial installations.

15
° Detector Alternative - One or more smoke detectors, as necessary, to

protect sleeping areas, are installed. It is assumed that additional

voluntary multi level installation will result in a distribution that

reflects the current mix of detectors in the population. The alternative

is implemented through regulation requiring minimum detector protection in

all occupied homes (new and existing) . Benefits of this alternative apply

to both the current and future population of homes.

Two adjustments must be made to the base case data base to model the performance

of smoke detectors. We must assess the fraction of homes having operational detectors

under a fully implemented detector policy, and the fraction of the population that

would be saveable under a detector policy.

According to the NFIRS data base, 14.4 percent of all residences with reported

fires had smoke detectors, 12.7 percent functional and 1.7 percent non-functional.

Therefore, 88.19 percent of the detectors present were indicated as functional. As

shown in figure 10, we assume there will be 95 percent availability of detectors if

the mandatory detector policy is implemented. We assume that 95 percent of the

residences without detectors will purchase them and that the functional/non-functional

rate will be the same as under current conditions. The net result is that 80.84

percent of the residences with reported fires are assumed in this analysis to have

functional detectors; 10.81 percent are assumed to have non-functional detectors;

and 8.35 percent are assumed to have no detectors at all. As previously described,

sensitivity analysis of these assumptions will be undertaken in future work due to

the lack of statistics in these areas.

We use the same classification system to assess the unsaveable portion of the

population as was used for sprinkler systems. The assessments are different, however,

because detectors perform differently from sprinkler systems. Figures 11 and 12

give the unsaveable assessments for fatalities and injuries under the detector

alternative. Clothing ignited casualties are unsaveable, as are "response/return"

and some "intimate with ignition" casualties. Those intimate with ignition sleeping

fatalities are considered saveable, as detectors have a chance to wake them up

before the casualty occurs. Casualties located in the room of origin at ignition

and who were "awake at ignition" are considered unsaveable, as they should have been

able to detect the fire and respond to it prior to detector operation. The net

effect of the unsaveable assessments in figures 11 and 12 is that 14.0 and 15.8

percent, respectively, of current deaths and injuries are judged to be unsaveable

under the detector alternative.

The loss model calculates the loss reduction effectiveness of detectors using

the revised probabilities on detector presence and the unsaveable assessments previously described. The results give reductions compared to current conditions of

41, 4 and 16 percent, respectively, for deaths, injuries, and property loss.

16
4.3 Joint Installation of Sprinklers and Smoke Detectors

A third alternative for reducing residential fire losses is to require the

joint installation of sprinklers and detectors, in the manner previously defined

under each separate alternative, in all new residences. The intent of the alternative would be to provide early effective suppression through the sprinkler system

and early warning to the occupants through the detector system. In principle,

detectors could be installed in existing residences as well, but for purposes of

clarity, we consider only new residences.

To model the combination system we must adjust the base case suppression size

probabilities to reflect the presence of the sprinklers, and adjust the detector

presence probabilities to reflect the increased installation of detectors. We must

also model the attenuating factors and the unsaveable portion of population. We use

the same suppression size and attenuation probabilities as for the sprinkler alternative because the sprinkler system will operate the same under the "joint" and

"sprinkler only" alternatives. As shown in figure 13, we assume that detectors will

be more widely available and accepted into new homes. Thus, we derive an 85.9

percent probability of there being a functional detector under the combination

alternative; an 11.5 percent probability for non-functional detectors, and a 2.6

percent chance of there being no detector at all. As shown in figures 14 and 15, we calculate unsaveable rates of 12.3 percent for deaths and 11.4 percent for injuries.

Based on the input data just presented, the decision analysis model calculates

reductions compared to current conditions of 58, 18, and 46 percent, respectively,

for deaths, injuries, and property loss.

4.4 Comparison of Relative Benefit of Alternatives ..

Table 5 summarizes the nominal reduction rates for the three alternatives

considered. The table indicates, based on the previously described preliminary

efforts, that significant reductions in loss may be achievable under any of the

three alternatives. In terms of total loss reduction, the greatest reduction appears

to be under the combination alternative. But if only one protection system is to be

implemented, then these preliminary results indicate that greatest fatality reduction

occurs under the detector alternative, and the greatest injury and property loss

protection occurs under the sprinkler alternative.

If the decision using this information were to be made on the basis of loss

reduction, the choice would be to implement a joint program of sprinklers and detectors. But this alternative is costly and it could take several years for the benefits

of this alternative to be realized in a significant way. Thus the cost effectiveness of each alternative should be examined before selecting an optimal strategy

from the three presented here.

17
As discussed earlier, a detailed cost model which enables the calculation and

comparison of costs and losses over time is currently being developed. In addition,

recent changes in sprinkler technology may require modification of the sprinkler

alternative, to consider the "fast response" sprinkler head. Thus, we postpone

beyond this preliminary report a cost/benefit comparison of the alternative intervention strategies.

5. SENSITIVITY ANALYSIS

Sensitivity analysis is a systematic approach for evaluating the extent to

which our nominal results are affected by changes in input assumptions. The residential fire loss reduction alternatives are reevaluated using plausible alternative

values for individual model parameters. If the ranking of the alternatives changes

with variations in the value of a particular parameter, then additional evidence

about the value of that parameter may need to be collected. The remaining variables

can be fixed at their nominal values for further analysis of the alternatives in

question. Sensitivity analysis helps to guide the model building and data gathering

effort as it enables us to focus our attention on the factors that most critically

affect the evaluation of alternatives.

The following discussion summarizes the sensitivity analysis conducted on our

loss reduction results to date. Other analyses incorporating the costs of the

mitigation measures or other input assumptions will be carried out as the need

arises

5.1 Detector Performance

The nominal value of 11.8 percent nonfunctional detectors in dwellings was varied from a high value of 25 percent to a low value of 5 percent for the detector

and sprinkler/detector alternatives. The smoke detector results are slightly more

sensitive than the sprinkler/detector alternatives to changes in the value of this

parameter, as shown in table 6. However, the relative ranking of the alternatives

in question does not change. Additional evidence about the likelihood of nonfunctional

detectors in dwellings may therefore not be required in the evaluation of these

three alternatives.

5.2 Loss Assignments - Unsaveable Human Loss

Sensitivity analysis was carried out on the probabilities of unsaveable injuries

and deaths for all three alternatives. Low, nominal and high values for these

probabilities are listed in table 7. Although the results for the sprinkler and

sprinkler/detector alternatives are more sensitive to changes in these values

than the results for the detector alternative, the relative ranking of alternatives

does not change.

18
5.3

Sprinkler Effectiveness

Alternative assessments were made of the size of fire development at the start

of effective suppression activity for the sprinkler and sprinkler/detector combination alternative. The original assessments are given in table 8 and the high and

low assessments are given in tables 9 and 10, respectively. Although the results of

this sensitivity analysis, given in table 11, indicate that a choice between alternatives based on loss reduction rates would not be affected by changes in the level

of sprinkler effectiveness, it is possible that some effect could be felt when cost

plus loss is considered. The high assessment is particularly important since it

represents a faster acting sprinkler than the conventional sprinkler on which the

original assessments are based, and may be indicative of the potential for greater

loss reductions under a fast response sprinkler alternative than are currently

projected for conventional sprinklers.

5.4

Unsaveable Human Loss/Sprinkler Effectiveness

Estimates of unsaveable personal loss and sprinkler effectiveness depend on the

speed of operation of a sprinkler head. Therefore, a joint sensitivity analysis on

the values of these parameters was carried out to provide insight into the potential

of faster acting sprinklers. The results of this analysis, summarized in table 12,

indicate that the choice between the alternatives (on a loss reduction basis alone)

would not be influenced by changes in these values, although the overall reductions

under the sprinkler alternative are virtually the same as the reductions under the

detector alternative for the low assessment.

5.5

Detector Impact on Unreported Fires

Fire incident data (NFIRS) for the 1977-79 period indicate that detectors were

present in about 12 percent of the fires reported to the fire service. Estimates of

detector installation in homes nationally for that period indicate that about 35

percent of the homes in the country were provided with one or more smoke detectors

Two possible reasons for this discrepancy have been identified [4]

® The voluntary purchase factor - people who voluntarily purchase and

install detectors may have fewer ignitions for various reasons, primarily

greater fire safety awareness than the general populace, and higher socioeconomic level.

° The unreported fire factor - fires which would develop into reported fires

without detectors present may not do so with detectors present, because

early detection enables occupant extinguishment while the fire is very

small. Unreported fires (and unreported fire fatalities) are those not

reported to the fire department, and which therefore do not appear in the

data base. Other sources, such as death certificate and special studies

are used to develop estimates of the magnitude of unreported fires and

fire fatalities.

19
The potential for early detection to "transform" potential reported ignitions

into unreported fires may have substantial impact on overall losses. However,

because the information available on unreported fires is extremely limited, it is

not possible to make reasonable quantitative estimates of the extent of impact of

this factor on overall fire losses at this time. It is therefore necessary to

estimate possible impacts and test the sensitivity of the results under the Detector

Alternative to these estimates.

The first step in this process is to assign base case losses to unreported

fires, so that new base case losses for all fires (versus losses for reported fires

only, used throughout this report) can be calculated. These losses were calculated

in the following manner.

It is estimated by various sources that 5-10 percent of all fire-related

fatalities occur in unreported fires. For the purposes of this study 7.5 percent

was used. From Highlights of Fire in the United States, Second Edition [1] , the

total fire deaths in one- and two-family dwellings can be approximated by multiplying

total deaths (8400) by the percent in residences (77%) by the percentage of residential

fires in one- and two-family dwellings (73%) , giving a total of 4,722 deaths in

reported and unreported fires. Assuming 7.5 percent are in unreported fires gives a total of about 354 deaths annually. A similar estimating process is used to calculate

the number of unreported fires annually, which equals 30,000,000 x 0.26 x 0.73 or about 5,700,000 unreported fires in one- and two-family dwellings annually. This

represents a loss rate of 0.000062 fatalities per fire, or 62 fatalities per 1,000,000

fires. Injuries can be obtained from the Highlights report in a similar manner,

that is 200,000 injuries not reported multiplied by the percent of injuries in

residences (68%) by percent of residential fires in one- and two-family (73%) for a total of 99,280 injuries in 5,700,000 fires, or a rate of about 17,400 per 1,000,000

unreported fires. Property loss is estimated at $450 per unreported fire. From

these estimates and the base case losses for reported fires, an estimate of loss

rates for all fires can be calculated using the probability assignments for reported

and unreported fires given in Sections 3.1 and 3.2. These loss rates are summarized

in table 13.

The next step is to develop a model to evaluate the possible effects of detection

on ignition and reporting probabilities. The model used is illustrated in figure

16. The path probability of 0.0010 for the reported ignition path is derived from

NFIRS data which indicate that detectors are present in 12 percent of reported

fires. The overall probability of a reported fire, from section 3.2 is 0.102 x 0.082

= 0.00836. The probability of a reported ignition, given a detector present, therefore,

is 0.00836 X 0.12 = 0.0010. This probability is a constant, as it is fixed by

available data. The "buy detector" probability of 0.35 is also a constant, as it

represents detectors installed in 35 percent of all homes. The relationship between

p (the probability of ignition, given detection) and q (the probability that ignition

is reported, given detection) is therefore given by the equation pq = 0.0010 ^ 0.35

(the "buy detector" probability), or pq = 0.00286.

20
This relationship, pq = 0.00286, is graphically represented in figure 17. The

portion of the curve which we are interested in is bounded by the points, from

Sections 3.1 and 3.2 at which the presence of detection does not influence the

probability of ignition (p = 0.102, with a corresponding q = 0.028) and the point

at which the presence of detection does not influence the probability of a fire

being report (q = 0.082, with a corresponding p = 0.035). These points represent

the upper and lower limits of the effect of detection on reported ignition.

From the curve we select three points which approximate 25 percent, 50 percent,

and 75 percent of the effect of detection on fire reporting (with the corresponding

75 percent, 50 percent, and 25 percent on the effect being on ignition. These

points are:

Effect on Reporting P q

25% 0.042 0.068 0.932

50% 0.060 0.048 0.952

75% 0.084 0.034 0.066

Losses under each alternative for both reported and unreported fires are then

calculated under the assumption that detection has no effect on reported ignitions.

This assumption states, in effect, that the three alternatives under consideration

affect reported fires only, and that cost benefit calculations should consider only

reported fires. As with all the sample sensitivity analyses described here, the

calculations are based on loss reductions before attenuation, for purposes of

simplification. These losses, given ignition, are calculated by multiplying losses

per reported fire under each alternative (the original assessments in the sensitivity

analysis tables, such as table 6) by the base case reported ignition probability

(0.082), and, multiplying the loss per unreported fire from table 13 by the base

case unreported ignition probability (0.918) and summing. These losses, and the

corresponding reductions from the base case of all fires are shown in table 14 as "no effect" losses.

The losses under each alternative for 25 percent, 50 percent, and 75 percent

effect of detection on reported fires are then calculated in the same manner with q

representing the reported ignition probability and 1-q the unreported ignition

probability, for the two alternatives involving detectors. The losses under the

sprinkler alternative remain the same, because no effect of sprinklers alone on reported fire probability has been noted. The losses, and corresponding reductions

from the base case for the three selected estimates of possible effect of detection

on fire reporting are also given in table 14.

21
Figure 18 depicts the results of this sensitivity analysis on Total Expected

Value. The apparent sensitivity of potential loss reduction to possible effects of

detectors on unreported fires is substantial. From figure 18, it appears that

detectors may surpass conventional sprinklers in overall loss reduction potential

if slightly over 25 percent of the discrepency between detectors in homes and

detectors in reported fires is due to the unreported fire factor.

Further research, to better understand the ability of early warning detection

to transform potentially large fires into minor unreported incidents is therefore a necessity in order to better understand and quantify the total possible impact of

detectors on fire loss potential.

6. CONCLUSIONS

No definitive conclusions can be drawn from this report, due to its preliminary

nature. Some general observations are possible, however.

Overall Loss Reduction

° All alternatives produce benefits, in terms of fire loss reductions. The

combination of sprinklers and detectors appears to produce the greatest

overall reductions in Expected Value on a per fire basis, followed

by sprinklers alone, and then detectors alone. The sprinkler/detector

alternative produces the best results for all categories of loss (fatalities,

injuries and property loss) , as the strengths of both sprinklers and

detectors are combined in a complementary, although not directly additive

manner.

Fatalities

Detectors alone appear to be slightly (but not significantly) better than

sprinklers alone in reducing fatalities. This may be due in part to the

large number of fatalities which occur in the immediate vicinity of the

fire origin. Detectors may prevent some of these fatalities by alerting

the people involved while the fire is still small and relatively harmless,

while conventional sprinklers often may not operate quickly enough to

prevent death or injury in such situations.

Injuries, Property Loss

° Although the early warning of detectors indicates that reductions in

injury and property losses are possible, sprinklers, by actively con- trolling a fire, appear to produce significantly larger reductions in

these loss categories.

22
Sprinkler^ Detector Potential for Loss Reduction

The potential for loss reduction from detector installation, especially

in the area of life loss, is significant, and should be obtainable in

jurisdictions which mandate detector installations in all homes. However, because the loss reductions calculated here are based primarily on voluntary detector installations, with a likely higher degree of fire

consciousness in the installing population, a simple requirement for

installation of detectors may not produce optimum benefits. Education

and training in detector use and escape practices may be necessary to

achieve optimum loss reductions in the population as a whole.

The significant potential for loss reduction when residential sprinklers

are installed, particularly as new technology produces better systems at

lower cost, warrants further attention. In particular the recently

developed "fast response" sprinkler should be evaluated.

Future Efforts

Further effort is necessary, and is underway, to improve the validity and

credibility of the results presented here, and thus to improve the

reliability of the decision making process. These efforts include the

improvement and validation of data and assessments used for probability

and loss assignments, the development of better information in key areas where results are sensitive to reasonable variations in assessments, the

expansion of the models to allow for improved assessment techniques, the

evaluation of additional alternatives, consideration of demographic and

socio-economic factors, the development of detailed cost information, and

the application of the methodology to other residential occupancies.

7 . REFERENCES

[1] Highlights of Fire in the United States, Second Edition, U.S. Fire Administration,

Federal Emergency Management Agency (April 1981)

[2] Helzer, S.G., Buchbinder, B. , and Offensend, F.L. , Decision Analysis of

Strategies for Reducing Upholstered Furniture Fire Losses, National Bureau of

Standards (U.S.), NBS TN 1101 (June 1979).

[3] NFPA 901, Uniform Coding for Fire Protection, National Fire Protection

Association (1976).

[4] Gomberg , A., Evaluating the Impact of Detectors on Fire Fatalities in One and

Two Family Dwellings, unpublished internal report. National Bureau of Standards

(U.S.) .

23
f— CO CO CO =3 CO

24

1.

Schematic

of

the

Residential

Fire

Decision

Analysis

Model
family

0) p. oC ^ CO

CO

C

U Q)

01 4J P c oP CO O D

C-> 4J

0) P O 4J •H CO

CJOWPo >. P (0c p p

o« o o

a,

(0o a 4-1 •H iH CO ^ P *H

O.

C

U POP J-4 «H 4-» CO &0 O O P P ^ GC U O P

CO *p C P

nH

p P

i-H

N

fH P «H p a. CO

25

Figure

2.

Components

of

Demographic

Model
u

0)

0)uca

0)uu< O *H 3 ^

•o nj O «H P *H

put CO

<0 4J >, C u

flJ -H 8 >H

a) o

i-< P4

1“

4J I M U 4J W 3 U (U *0 (U C

CJ M-l 0) *M >

OuW -H

X

B

U 00 0)

IJ fH 8

<0 (0O « V Cl « *0 4J ^ ^

e6 ec o

S)0 “•H W CO qi*H •a o « (U MO

CO

0) « B >

C8O -H O C

0) 3 Cl P "O <U •H 41 4J M iH

*<

26

Figure 3.

Components

of

Alternative

Selection

and

Implementation

Model
SIZE

OF

FIRE

SCENARIO

DETECTOR

AT

TIME

OF

EXTENT

OF

GROUP

PERFORMANCE

INITIAL

SUPPRESSION

FLAME

DAMAGE

27

Figure

4.

Schematic

of

Fire

Loss

Model
Functional

UQ

W Pd M N W

piS Pd O U H Z

PJ Pd Pd M H Pd

Pd Pi a p- s s u o OT

28

Figure

5.

Probability

Tree

fora

Single

Scenario
cn§“oO PS

•H

O

n)c

<uo « CJ

(U 0 « o

in ^ CO (0 O' to (U -

(1)

N

M -H

o. to OS

g.

cu

«3

waoouo

to

uC

Q)Ow

0) •H

Pd

Co

•H

cn

cn0)

N

M -H

CO

aP

COoK

Pd

0)60

cd

cnCl3OMOU

tdc

0)o

CO

0) o

•H

CO 0)

CO

N

(U •H u CO aap

CO

4-1O

o;

U 60 a

0) s

4J (d X a

Pd

29

Figure

6.

Probabilities

for

Suppression

Size

Given

Extent

of

Damage
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

30

Figure

7.

Unsaveable

Fatality

Assessment

WorUsheet

:

Sprinkler

Alternative
COouo ns o

•H

O

U

CO •' IS

Q)

CJ

(/J

U (1) •H

pZM c

/«-N o m •H CO CO o^ CO 0) • 0) M

•H

a CO O' p. • pwo

(U

4-1 bO c CO (U 6

4J cO O

(d

CO(X

SJoou

cOc

0)u

c/5(1)

(0p.DoMOO PS

•H

.k CO < P

0)CJ

c/5

0) •H

ao

•H

CO

CO

0)aap

CO

(UN

•H

CO

X

Ex3

0)PO

CO P

29

Figure

6.

Probabilities

for

Suppression

Size

Given

Extent

of

Damage
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

09

CO

CO

(/) CO

CM OO

CM CD

mm

in CM CO

CO

in

in in in

CO

COu

(/>

o»3 ® —

~ 2 uo> O >’ \ 3 o> k- “ bo3 U

(/> li. -g u.’ Li- o

t/i 3

3

3

(Q ‘o.

0» o o O **

_09 09 oc u o O. OC

(/) O _09 09 CO

w —

AUnrNI JO iNlilV N0I 13V Auvnsvo

30

Figure

7.

Unsaveable

Fatality

Assessment

Worksheet

:

Sprinkler

Alternative
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

09

CM

c6

CO

00 CM

(= o>

m in o CM to CO

<30 CO III;

<n CO

CM

iimm

24 CM CO iliil 27

CM

GO m 23 - CO

GO 99

35 20 iii

308 48 65 305

LO GO GO <N

GO

CO 58 o> LO

CM

CM

CM

CM

3558 154 313 CO 33

AdOfNi do m\i IV N0I13V Ainvnsvo

31

Figure

8.

Unsaveable

Injury

Assessment

Worksheet:

Sprinkler

Alternative
ooo 0

01 IM

iwu

(Uoc

CO 4Jo.

<uuo< U -H 3 -O

*a CO 0 C-I 01 CO

CO u >. C cj

CU *H B iH

01O

iH Oi O.E

4J 1 (0 u 4-1 (A 3 u 0) •o 0) o 0-1

)-l <4-4 >

Onu •H

j: c U 00 0) M fl E

CO (0 o

01

V

u

cn "o 4J

0)

V 3

ciO 00 o ou

CO<u

CO c

00

•H

l-l CO O 01 •o

(U (0

(0

0)

(0 c >

(0

O *H

Q *H *J »J U CO u c 0) 3 )-i

>-i "O OJ •H 01 U

Pt- PS .H

<!

32

Figure

9.

Attenuation

Assessments -

Sprinkler

Alternative,

New

Homes
33

Figure

10.

Detector

Presence

Probabilities

after

Attenuation

-

Detector

Alternative
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

CO

o>

>

CO

</>

<N

. .

! tf» : mm : m

CD - - 25

49 - LO - 82 33

m CO

CM CO

CO §

: ^ ^ CM

ir> m - - - 1X0

CO CO -

AUnrNI JO JNIl IV N0I13V Auvnsvo

34

Figure

11.

Unsaveable

Fatality

Assessment

Worksheet:

Detector

Alternative
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

09 oe>

59 ^ O'"" <M GO CO

83 164 - o>

CO

*v*** lilii

24 CM CO GO 27

CM

GO o> 23 - CO

GO 99

35 20 56

308 48 65 305

tn GO

memm

138 58 a> LO

CM

CM

CM

CM

3558 313 CO 33

rao

c/> LU oe u u

o> c. a. b4) 3 c u> C c ra si. o Q> <u o> a. OC a>

(/> o> oc o

CO

AdnrNI JO 3NI1 IV N0I13V Ai1VnSV3

(Qc

o

.a

ra

c= ra

35

Figure

12.

Unsaveable

Injury

Assessment

Worksheet

;

Detector

Assessment
Figure

13.

Detector

Presence

Probabilities

after

Attenuation

Sprinkler

plus

Detector

Alternative
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

ignited

<D

tz

rz

(/> CO

iipiii

iiilil ur>

CO m

uo CO

Q) - -

* 25

49 - ir> - 82 33

in 23 CO

GO - CM

LO uo - - - lO

CO GO

tM)

rau

(/>

(U3

(U O LlJ o> o \ c (U >— a.3

bfl c O

CO

Ll_ +-* c LiJ U- c: o

(/> 3

c

3

ra 3.

a> o o jCD <U oe u o Q.

(/} O qa

a> c/5 Q£

AunrNi JO JNii IV N0I13V Ainvnsvo

37

Figure

14.

Unsaveable

Fatality

Assessment

Worksheet:

Sprinkler

/Detector

Alternative
CASUALTY

STATUS

AT

IGNITION

Outside

In

Outside

In

Outside

In

Room

Room

Outside

Room

Room

Room

Room

NonNonClothing

Bldg.

Awake

Awake

Asleep

Asleep

Mobile

Mobile

Intimate

Ignited

II

UT7 CN< CO CO

lilill CO CO

o>

CO 126

24 CM CO GO 27

82 23 - 86 CO

CO

35 CM 56

308 48 65 305

in GO GO

138 58 O) in

CM

CN

CM

CM

3558 313 CO 33

cu 0»

L. £ uJ

a> o \ c

a> t—

ex. 3

bfl s

CO o

v> u. C o

Ui 3

C CQ a. o

a> o o a> oc u o a. oc

U) o

Q> CO 03 CO

oc AdnrNI JO JNIl IV N0I 13V Ailvnsvo

38

Figure

15.

Unsaveable

Injury

Assessment

Worksheet:

Sprinkler

/Detector

Alternative
orHOO

39

I

i

Figure

16.

Model

for

Calculating

Detector

Influence

on

Ignition

and

Reporting

Probabilities
|b| QBitiodau SI NOiiiNSi iVHi AiniavaoMd

40

PROBABILITY

OF

IGNITION,

GIVEN

DETECTION

|p|
m

m

CN

% - 3SV3 3SVa WOUJ (A3 IViOil NOI13na3)l

41

Figure

18.

Sensitivity

to

Effect

of

Detectors

on

Unreported

Fires
Table

1.

NFIRS

Codes

for

Fire

J

COo

•HU

03C

0)uw oouDowco

•HU

•HC

bC c

OC •HO

CO0) <

3-1 O

u

O- lT» O'

0) a^ 00 U I I iH O

o; o> 00

lH PL, tH O' sc <c w E

vO

00

CM iH00

CN

O 0^

O 00 XW

3-i 0m ON

ON 00 1 I •H O00

PL,

M

O SE u E m^ o^

00 I rHON

CV4 SO O sO00I O CM iH o 00 X

0)

3-4 3

COo

CLX

CM

CN

CO3o

•r4CJ •Ha

CO3

CO

CO •H

'CO3

»HO3

00

somm

CO

<i> 3

cx <4

003

•H

Os3

tH

CU33

3-( 'O

CJ

o\

04

sO

CMI Ocs

CO3

CO3O

On

vO

CO

3-1 O§

i-i

CM

<r

CM3

oo3

003

•HOOo

cn

fo

COI o

CO

003

•Hi

CO

003

•r4 'O3

PC

o^CO

CO

COO

CO

003

•HO8

c/5

P54 P

CO

COI o

CO

oo3

•H§

c/53

4J33

•HO3 3O3

CX

C/5 *33

4J 3 3

CJ

M 3O3

CL

CO•33

rH33 U O 3 3 ^ O 4J U O

CO

34O'U

CMSOo<

4JCL3OX3

0000ppi4woo<

00 •S

4-) n)

<u na

COoo

hJ

0)oc

CO

CO O

42

NOTE:

AO=

Area

of

Origin

FMI=

Form

of

Material

Ignited

TMI=

Type

of

Material

Ignited

EQP=

Equipment

Involved

in

Ignition

FHI=

Form

of

Heat

of

Ignition

IF=

Ignition

Factor
16.

Central

Heating

Area

AO

62

uo

'O

0)Dc

•HCOO

0) f—

CO H 11 o 11 111 11

C5> 11o> !1 111 111 111 111 o 1 1 1 m 1 1 1 \ 1

r. 1 o o vD in M pL. cu O' O K w W

o^

Ov

CNJ o\ o> <?^ 00

cn o^ a^ o\ <3^ o> o>

i-H

X - X 00X 00

X 00

CM [d 00

W a^

W ONW CTv o>

CO 1 1 ON

#V 1 1 M

M

H

H ^ #» 1-4 1-H y S S r>. S o CN CO CM E r>* E ctn E a^ E o> o

o^

M

MH iH r-H •* rH 1—1 *'

M s 2 s 1—1 iH O rH O tH O s P O P O P O P o m vo O CM o

CO CO CM o O CM CM

4.)

#. «s

p 1 1 1 1 1 1—1 •*>0“ 1-H 43 1 i 1 1 1 1 O rH CM CM 1 rH o O O rH

<I <2 < P

00 0) P c p o O 3 •H p P P o oo 4*2 a o CX p p O c/5 d) P C/5 4*2 •H a o P* C 4*2 c/5 OO p c/5 • O 1 p cx 4J a P •rt c/5 00 P • c/5 O > P P H c 1 •H 00 •H P C P t-4 p CX 43 M p o • CO •H P U P P P 0) > P 4H 43

***«...

C o •H rH •HU

00 00

p

p C/5 o p c p 00 p 3

•H •H P •H • • • • o

TD H3 • 'rl t-HOUO o p

H3 42

U PX P CO P CO 4*2 d) CU a •H a •H H •H •rH P

PQ PQ c IS < , * f^ 00 o> o rH CM CO

m 1—4 t-4 rH 1—

CM CM CM CM CM CM

43
Table 2. Description of Fire Scenario Groups

SCENARIO GROUP PROBABILITY DESCRIPTION

A .2139 Exterior fires, iqnitinq roof or walls, on open porches, car fires in yard, brush and

trash fires in yard, usually involving

building eventually. (Scenarios 1,2)

B .1169 Rapidly Developing fires, either incendiary/

suspicious fires, or those where flammable

liquids, gases or explosives are involved in

ignition. (Scenarios 3, 5, 6, 7)

C .0492 Children Playing, Apparel fires, where

clothing on a person is the first material

ignited, or where the ignition results from

children playing with the source of ignition

or the material ignited. (Scenario 4)

D .0373 Smoldering Upholstered Furniture fires are upholstered furniture, mattress and

bedding fires ignited by smoking materials.

(Scenarios 9,10,17A)

E .0207 Flaming Upholstered Furniture fires, where

the upholstered furniture, mattress or bedding is ignited by other than smoking

materials. (Scenarios 17,18,17A)

F .1129 Concealed Space fires, primarily wiring

within the walls and ceiling, ducts and

chutes. Chimney fires are included in this

category. (Scenarios 12,13)

G .3057 Interior Living Space fires are those not

categorized above, in the living room,

bedroom, kitchen, dining room and hallways.

Sprinklers and detectors are likely to be

installed so as to protect these areas. (Scenarios 8,11,14,16,19,20,21,22,23)

H .1395 Interior Non-Living Space fires are in

closets, bathrooms, laundry and storage

areas, and garages. Most of these areas would not be expected to have sprinklers or detectors installed in them. (Scenarios 15,

24)

I .0039 Other fires are those that cannot be identified

or categorized. This group is very small.

(All other fire except unknown)

44
Table 3. Detector Presence Probabilities*

State Description Probability

Functional In room of fire origin and operated. .1267

Not in room of fire origin and operated.

Fire too small to require operation.

Non-Functional In room of fire origin and did not .0174

operate. Not in room of fire origin and

did not operate.

None No detectors present. .8559

*Note : These probabilities are conditional on reported fires, i.e., they are

"given a reported fire".

45
Table 4. Suppression Size Assessments - Sprinkler Alternative

Scenario Suppression Change to Probability for

Groups Size Sprinkler Alternative

A 0 No change

< R Add 25% of > R probability to < R

1 R Reduce ^ R probability by 25%

B 0 Add 30% of > R probability to 0

< R Add 50% of > R probability to < R

> R Reduce ^ R probability by 80%

C 0 Add 20% of > R probability to 0

< R Add 80% of > R probability to < R

1 R Reduce ^ R probability by 100%

D 0 Add 80% of > R probability to 0

< R Add 20% of > R probability to < R

> R Reduce ^ R probability by 100%

E 0 Add 60% of > R probability to 0

< R Add 40% of > R probability to < R

1 ^ Reduce ^ R probability by 100%

F,H 0 No change

< R Add 30% of > R probability to < R

i R Reduce ^ R probability by 30%

G,I 0 Add 10% of > R probability to 0

< R Add 90% of > R probability to < R

> R Reduce ^ R probability by 100%

46
Table 5. Nominal Loss Reductions for Three Alternatives

Sprinkler Alternative^

2 Detector Alternative

Possible Reduction* in

Fatality Rate Injury Rate Property Loss

34% 17% 38%

41% 4% 16%

Sprinkler/Detector Alternative 1

58% 18% 46%

^Applies to reported fires in new one and two family dwellings with sprinkler

systems utilizing standard commercial type sprinkler heads only.

2Applies to reported fires in all one and two family dwellings.

*The results presented here indicate current estimates of possible reductions

in fatality, injury and property loss rates under the three alternatives

considered. The estimates also include modeling of the attenuation previously

described in the alternative selection and implementation model. It is

important to regard these results in the context of the specific definitions

and scope of the alternatives considered as well as the implementation strategy

defined for each. Additional work, including more detailed modeling and

further evaluation of assessments, as indicated by sensitivity testing, is

necessary to refine and substantiate these estimates, and to allow for the

evaluation of additional alternatives.

47
Table

6.

Sensitivity

Analysis

of

Detector

Performance

Probabilities

Frequency

of

Nonfunctional

Detector*

c

o

u

3

'O

(1> C3C

c

o

u

3

3

O

a

3

'O

0)

cid

6

ou

I

•HX

6ou

4^

60Uo

CO3U

(U

CO3

PQ

C

<u6

CO

CO

CU(0

CO <

(1) CO

CO o

0)CO

CO PQ

(U

CO

CO u

0)

COCO PQ

4^3

0)6

CO

CO0)

CO

CO <

5^ ^s B>S B-e B^ B^ B^S B^ B-S B^ B^

ON o >3* o^ O v3 rH CO CO

Csj CO CO rH CM vO CM m in

o o m O CM m in m CO Mf

«—

cn 00 m so O CO ON rH 00 ON

00 iH ON Os 00 o rH in o rH fO fO C4 m CO CO CO CM CM CM Ml- in vO m

B^S Bs9 B^ B^ B^S B^ B^ B'S B^ o o m o^ Os in ON rH rH in sO

'sf CO mT CM P^ CM in m

pN. vr m ON 00 rH 00 CO P^ CM CM

CM O CO CO On CM rH p^

iH <r CM rH vO 00 '3* O ON cn PO CM m CO CO CO vO rH CM CM CO m vO m

rH rH 3 3

3 3

0 0

B^ B^ B'S B^ B^ B^ B^ •H *H o o m ON m <r m ON rH m m U U

04 MT CO MT CM vO CM m in o O 3 3

3 3

4-1 44 3 3 O O

3 3 3 3

Hi Hi 3 3

00 U U

ON C4 ON vO m o\ CM CM CO O vO p^ 3 3

CO C4 CO vO CO o o CM 00 CO 3 3

1-H < ^3“ p^ ON o o CO CO CO ro CM m CO CO CO s^ rH CM CM 3 3

m vO m U Hi CH CH

CO CO

Hi Hi O O

4J U

U o

3 3

CO CO CO U 4J

(0 CO CO 3 3

o o o -o -o

(0 ij CO h-i CO i-3 (U 3 3 44 44

•H CO > •H CO > •H CO > 0 O

4J 3 u 4J 3 u 6a 4J 3 4J Ci3 •H *H u •H *H u •H •H u B^ BsS tH 3 rH P 3 rH fH u 3 rH m m

3 3 a 3 3 3 a. 3 3 3 PX 3 CM

4J •r-) o 4-> U T-) 0 U 4J T-) o 4J 3 3 u O 3 3 u O 3 3 u o

64 H P4 H 64 M PH H 64 M PH H f-to >

•H O X

9AX3BuaaqxV 9AX3BujaqxV 9ApqBua9qxV

jax^iufids aoqo959a xoq99q9a/J3T^uxadg

48

*Base

Case:

11.8%

of

detectors

present

are

nonfunctional
Table

7.

Sensitivity

Analysis

of

Unsaveable

Assessments

c

o

•Hu

O

;3

0)

<U

C/5 CCJ e u

o

M 0)

C/5 03 pa

C OJ O CO •H CC3 4J g CJ a o

0 u 0

'O 44 CO

(1) CO

dti PQ

c

0/ ^ e

bO CO •H CO X 0)

CO

CO <

CO <U c e

•H CO W) CO •H 0) u CO O CO

B^ B^ B^ B-5 B-9 B-5 B^ B^ B>9 O 00 un vO iH CO in ON m CO

cn iH <!• CO tH CM so iH in m

o CO iH 00 CM CM O 00 vO 00 o <N vO C7> O CO CO vO CO iH

1-H •nT vD o 00 CM CO o CM

<r CM m CO 'd- CO sO CM CO CM <f m in

5^9 B>5 B-9 B^ B^5 B'S B^S B^ B^ 6^5 B-9 O 1—1 m CO CTn m SO <r CM m

m CS <r <r iH CM r>- CM in in

00 CM On o CM CO CM CO v£> CO VO so

CM iH CO so O 00 tH C3S CO

rH rH iH CM CM m SO m o 00

CM CM m CO CO CO so iH iH CM CO

to SO m

B'S B^ B^ B-9 B>9 B^ B^ B^ B^ B^ B^ O O IT) <T\ LO LD OS iH m m CM <r CO

00

iH CM vO CM m m

ON CM o\ vO m CT» CM CM CO O so

00 CM CO vO CO O O CM 00 CO

tH <1

* <r <r A CTv <f o o CO CO CM m CO CO CO vO tH CM CM

tn

CO

VO

CO

m

(0

CO CO (0 o o o

CO hJ (0 h4 CO

<u 05 05 •H CO to > •H CO to > •H CO > 4J 0) 4J w H 05 4J W H 05 u

•H •H •H •H M •rl •H M

r—

U 05 tH tH M 05 fH tH U 05 iH

«0 P a CO cd P ex Cd cd P a Cd

4J *r-> o H H •rn 0 4J H •r-> o 4J

cO c O cd c O Cd c: u o M Ph H Pm IH p4 H P4 H Pm H

3A'j:aBua3 a'[Y aA-paeuaaaxv aAxaeuaaaxV

aai^u-pads ao^oaaaa joaoaaaa/aai^uxads

CO CO

(U 0>

r-l rH •S -2

cO to (U (U > >

to to

CO CO c c

C3 !=>

XI •• oo &

•H O

5C i-J

49
Table 8. Suppression Size Assessments - Sprinkler Alternative

Scenario

Groups

A

B

C

D

E

F,H

G,I

Suppression

Size

Change to Probability for

Sprinkler Alternative

0

< R

> R

No change

Add 25% of ^ R probability to < R

Reduce ^ probability by 25%

0

< R

> R

Add 30% of ^ R probability to 0

Add 50% of R probability to < R

Reduce ^ R probability by 80%

0

< R

> R

Add 20% of ^ R probability to 0

Add 80% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

Add 80% of ^ R probability to 0

Add 20% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

Add 60% of ^ R probability to 0

Add 40% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

No change

Add 30% of ^ R probability to < R

Reduce ^ R probability by 30%

0

< R

> R

Add 10% of ^ R probability to 0

Add 90% of ^ R probability to < R

Reduce ^ R probability by 100%

50
Table 9

Scenario

Groups

A

B

C

D

E

F,H

G,I

High Suppression Size Assessments - Sprinkler Alternative

Suppression

Size

Change to Probability for

Sprinkler Alternative

0

< R

> R

Add 25% of ^ R probability to 0 Add 25% of ^ R probability to < R

Reduce ^ R probability by 50%

0

< R

> R

Add 25% of ^ R probability to 0 Add 70% of ^ R probability to < R

Reduce ^ R probability by 95%

0

< R

> R

Add 60% of ^ R probability to 0 Add 40% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

Add 90% of ^ R probability to 0

Add 10% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

Add 80% of ^ R probability to 0 Add 20% of ^ R probability to < R

Reduce ^ R probability by 100%

0

< R

> R

Add 40% of ^ R probability to 0 Add 10% of ^ R probability to < R

Reduce ^ R probability by 50%

0

< R

> R

Add 50% of ^ R probability to 0 Add 50% of ^ R probability to < R

Reduce ^ R probability by 100%

51
Table 10. Low Suppression Size Assessments - Sprinkler Alternative

Scenario Suppression Change to Probability for

Groups Size Sprinkler Alternative

A 0 No change

< R Add 10% of > R probabilities to

> R Reduce ^ R probability by 10%

B 0 Add 10% of > R probability to 0

< R Add 50% of > R probability to <

> R Reduce > R probability by 60%

C 0 No change

< R Add 80% of > R probability to <

> R Reduce ^ R probability by 80%

D 0 Add 60% of > R probability to 0

< R Add 20% of > R probability to < > R Reduce > R probability by 80%

E 0 Add 40% of > R probability to 0

< R Add 40% of > R probability to <

> R Reduce > R probability by 80%

F,H 0 No change

< R Add 10% of > R probability to <

> R Reduce > R probability by 10%

G,I 0 Add 10% of > R probability to 0

< R Add 70% of > R probability to <

> R Reduce ^ R probability by 80%

52
Table

11.

Sensitivity

Analysis

of

Sprinkler

Effectiveness

Assessments

(Effect

of

Sprinklers

on

Suppression

Size

Parameter)

c OJ o CO •H cd e o B^ B^ B-5 O 0 o CM ON a OJ cn rH CO CM

T3 <4-1 CO

Cd Oi i:c:^

B^ BnS B^ B^ B^ B^ m <r r*^ in <• in

rH CM <r

o

c

<1) 0

CO

CO

a;

CO

CJN CM <r o in 00 CM CM in <r o m

00 vO vO c?^ vO CO O O 00 in o

CO 1-H o CO 'd' a^ CM 00 <r

>d- CO vO CO CO CO vO CM m CM m vO in

a 0) o CO •H cd U e a B>5 B^ B^2 B^ O o <1* 00 00 3 u OJ CM m

tj 4-1 CO

0) cd PQ

B'S m<!* <r

rH lA

Csi

C'4 00 cn

vO

^5

f—

v£)

U

.c

0)0 iH o\ CO rH

60 CO o CM vO

•H CO in <y^ vO

as 0) CO r>* 1—1

CO

CO <

<r

m

vO

<r

cn

00

cn

o>

COvO

CMo

CO

CMO

vO O CTn in o in 00 CO

o> vO

rH rH CO <r

c Q) 0 CO *H Cd U 0 o B^ B^ B^ B'S O O o O 40 ON D U cu CM <r CO

T3 4-1 CO

CU Cd

PC4 Pd

m

^s

<3* m

CM

B>S B^ B^ B>5 o\ rH m m

vX) CM in 40

iH C

03 0) C B

»H CO 00 CO •H <U U CO O CO

ON CM <J^ vO in 00

00 CM CO vO cn

rH <r

CO CO CM m CO CO m vO

CO

COo

CO CO

0) CJ •H CO >4 > •H CO U (U u w U 0) •H •H •H

rH }-< (U rH t-H Cd 3 o- Cd Cd U •r-) o 4J 4J «t~) Cd c )-< O Cd c M P<4 H P4 M

CM CM CO O sO O o CM 00 CO <r

<3^ <r o o

CO rH CM

40

CM <r

m CO

CO CO o o

kJ CO hJ

Q) > •H CO Po > 4-1 w 4J 0) 4J u tH •H

<u rH rH u (L) rH

p- Cd Cd s CL Cd o • 4J O u

0 Cd (d U o

PLI H M CH H

BA^ijeuaa^xv

aax^^luTjds

3A74Bua9:i-[V

jorioanaQ

aAj^FUjia^XV

JLo:^OB2Ba/j,Bxy[ujjids

vO

CM

OJu^ CM

•H

4-1 0)U

4-1

.. 0) (1) a> 0) (u

CO w w

CO u

0) ^ CO 00

CO *H O

(£) 3d h-3

53

figure

28
Table

12.

Sensitivity

Analysis

of

Unsaveable

Assessments

and

Sprinkler

Effectiveness

Assessments

Combined

c 0) 0 CO •H 3 U B a B^ B^ B^ B^ B^ B^ B^ 8^ O 0 cn cn CM \o rH CO Os m in sO 0 h 0) CN CO CM <r pH CM m

U-l CO

cd

cx: pa

4Jc

0) 3 S o m ««d* vO CO MT CM CM CO CO o O

O CO m m vO Ov O CO m m in vO

iJ CO 00 00 O vO vO O 00 m in 00

0) CO vO CO CO sO CM so CM

CO m so m

CO <

co CO •H cd 4J B a ^s B^ 8^ B^ B^a B^ B^ B^ B^ e^a a o in O vO CM o^ in SO P^ OS CO CO 3 )-> 3 m cn in in pH CM r> CM so so

U.I CO

0) cd cc (£)

c

<u CO

JS s os CO o CM so CM CO CM CO in PN.

CO CM vO o CO CM O 00 CO pH CO 00

•H CO 00 CO Ov CO CM P^ m <r 00 so CM S 3 CM tH CO CO CO so pH so pH CO

CO so

c OJ 0 CO

Cd U s o B^ B^ Bn2 B^ B^ B^ B^ 8^ B^ O o o O in ON m >d* in OS rH in m

3 )4 (U CM CO MT pH CM sO CM in m -3 14-1 COCd pa

u

i-H c

d 0) 00 C 6 CM vO in os CM CM CO O sO p^

•H CO 00 CM CO so CO O o CM 00 CO <r

bO CO #> Os o o •H 0) cn CO CM in CO CO CO \o fH CM CM

CO m so m

O CO <

CO CO CO

CO CO 3

O 0 o

CO hJ CO hJ CO hJ 3 (U 0) •H CO fo > tH CO >^ > *H CO >

U (U u pa u 0) 4J 4J 3 u pa

•H •H u •H •H u •H •H u

U 0) rH U <u iH Ml 0) iH

Cd 3 cu fd Cd 3 a 3 3 3 PM 3

4J •r-) 0 4J AJ T-) 0 U 4J T-) 0 U

Cd 3 p o Cd 3 u O 3 3 Ml O M PM H Pm M PM H Pm M CU H

aA-paeuaaaxV aAf^BUja^xV

j9-[:)iu-j;ads

54

High:

High

Unsaveable

Assessments

(Table

8)

and

High

Sprinkler

Effectiveness

Assessments

(Table

10)

Low:

Low

Unsaveable

Assessments

(Table

8)

and

Low

Sprinkler

Effectiveness

Assessments

(Table

11)
Table 13. Base Case Fire Loss Rates

One and Two Family Dwellings

NOTE

Reported Unreported All

Fires Fires Fires

Fatalities 6302 62 574

Injuries 66283 17400 21408

Property Loss $ 4479 $ 450 $ 780

Total EV $ 8956 $ 829 $ 1495

Fatality and injury rates shown is rate per 1,000,000 fires.

Property loss and total EV shown as rate per fire.

55
The

%

effect

is

the

percentage

of

the

discrepency

between

detectors

fires

and

detectors

in

homes

estimated

to

be

due

to

the

unreported

factor.

The

values

given

in

this

table

are

fatalities

and

injuries

1,000,000

ignitions,

and

property

loss

and

total

EV

per

ignition.

Attenuation

is

not

considered.

w

0) •H

(1) U

C U d) •H •H a

(1) ^-4

C OJ 0 tn

f-( ca •u a o ^2 ^s ^S B^S o o vO m iH os Os rH 00 rH 3 M (U 'O y-i cn m CM rH sO rH CM CO

0) rt Pi ca

u

u 00 sO 00 CM rH OS

5^ OJ so eg rH o rs. sO vO CM

lO IH CO vO CM rH OS m O

o 00 rH U CM <J> <j> rH -CO- </>

c <u o to •H ta <u s u 8>S B-S O 0 so m rH OS 00 CM SO 3 Vj OJ "3 iw to CO CM iH SO CM CM

tu ca Pi ea

ij

S'? o 00 Os SO in SO SO rH O (U so CM iH o CM o O rH

ITl >4.1 CO CO sO CM CM so SO rH

4.1 o iH OS

[d CM <J> rH <ry </>

C tu o to .H ta •u a o B^ ^S y o sO lO os OS <r 00 3 w <u •3 4.1 to 3 3

Pi PQ

CO CM rH •d* rH rH

4J 6^ O 00 Os sO CO rH 00

tn 3 SO CM o OS CM CM

CN 44 CO CO sO CM CM m so CM

44 O iH o rH M CM <n- CM </> <J>

3 3

o to .H 3

4J a u dsg BsS B-S O O so m 1—

os rH rH 00 CM 3 M 3

•3 44 to CO CM rH rH 3 3

Pi to.

u

u

3 00 O' SO rH 00 r>4 rH

44 so CM o Mf SO rH iH

44 CO CO SO CM CO rH CO o rH rH rH

CM CO- <J> CM •co- </> Oz

CO co

CO CO o o

CO CO r4

d) 0) •H (0 > •H CO Ps > 4J 0) u u U d) 4J Ex3 •H •H u •H •H U

fH u d> rH tH U d> pH d 0 a (0 CO 0 a CQ 4J •rn o u 4J TH o HI

nj C u 0 <0 a u O H Ou H H Ph H

aAp^BuaaaxV aAxiBuaaaxV

jax^ux^ds Z030333a

B^ B^ B^

00 CO in

pH CO

m CO

CM OS o

rH in in 00

rH </>

B^ B^S

'd* iH CO

rH CO

rH SO m 00 CM

rH o in OS

iH <0-

B^ 6'S OO 00 so CM

CJs m 00

00 00 m

rH in

ospH </>

B^ B-e B-S CO in sO

so CM

in O

fH 00

CM CMO

in

CM <0-

CO

m

u

oJ

d) •H CO 4J d) 4J •H •H V4 tH U 3

(0 O.

HI TH O

«3 c >4 pH M P4

3AT;:iBua93xv

ao5Daa3a/J3I^u-j;ads

56

Total

EV

$1093

27%

$1048

30%

$

983

34%

$

938

37%
APPENDIX A. PROBABILITY TREE CALCULATIONS

An illustration is provided of probability and loss assignments for one scenario group. The scenario group presented is Group G - interior living space

fires, which is the dominant scenario group in terms of both overall losses and

frequency of occurrence. Four probability trees are presented in Figures Al through

A4 - the Base Case probability tree, the Detector Alternative probability tree, the

Sprinkler Alternative probability tree and the Sprinkler/Detector Alternative

probability tree, respectively.

Note that the loss assignments for a particular path are related only to

Extent of Damage within each scenario group. Differences in total Expected Losses

between alternatives, where Expected Loss is the siim of the loss assignments times

the path probabilities, depends on differences in path probabilities between

alternatives

The remaining thirty-two probability trees (eight additional scenarios for the

base case and three alternatives) are not documented here due to the preliminary

nature of the study and for reasons of space limitation. All calculations, in

computer printout form, are available at the Center for Fire Research offices.

57
000163

.020013 $

385 CO

CvJ o

CM

</>

CTn vO

00

<J>

00

CsJ o

CN

<J>

05

vO00

<J> o

00

coin 00 00 CN 05

CO

O s£> CN 00

<r> <j> <j>

CO r*-* CN O 05 O so vo

CN CO 00 </></> <f>

m 00 00 CN 05 cn o so

CN 00

•CO- -CO* -coco

CNo

CN

<J>

05sD

00

<oo>so

00

•COCO

COo

hJ (Uu U -H

Cd 14-1 •H Vj O <U Q a

C^) fO CN fO CO o CN SO CN

00 00 r-- 00 00 CO sD CO sO

CN CN CN CN in 05 00 c?5

i-H rH CN iH 05 o 00 o o o SO o o 1—

SO CT5 so

t—

O o o o O

sO SO o CN SO CN SO vO

CO

n)

CU CO CO CO sO CO SO CO CO

00 00 in 05 00 05 00 00 <4M 00 CO 05 O 00 o 00 00 05 05 i-H SO 05 SO 05 C75 o o o o o O o o c 01 M a

r-v *—

r->. iH CO

m 00m

m I-H in in CO 05 CO

05 CN 05 CN CN CO 05 CN 05 c o o rH in o o o O o o o iH o o o o o o o o O o

CO

q; 00 00 CO in 00 in 00 00

•H

4J

<u 05 05 CO 05 CO 05 05 •H •H CN CN CO 05 CN 05 CN CN

in in o iH in iH

m in

rH o O I-H

O o o o o O o o o crt 0)&

<4-1 o

0) 4J 00 c m

0)

g

4J td X Q

Cx3 co

•H

CO

CO CU 0) N U -H

CX CO

ex3

COM 0) o a ^ c O 0)

(U CO 4J 0)

CU

u

O Pm O

•HU cx

CO 3 c o

(Uu

CJ o

c/3

58

Figure

Al.

Base

Case

Probability

TreeScenario

Group G
IT) CO CO r*^

m 00 r*^ 00 CM o^ cs o^ 00 CM o> o sD o vD vO fO O VO

CN CO CM 00 00 CM CO

<J> </> <jy </> </> </> </> </>

00

m 00 00

CM Ov o^ 00 CM o> CM o^ a^ O vO vO fO o vO O v£> CM 00 00 CM 00 CM 00 00

</> <J> </> <J> <J> <o- </> <o- </>

wwo a; hJ u

•H

U M-(

CI3 fH

U

f-H QJ o a Q

fO ro CM COm o CM vO CM v£> vD o CM vO CM vO vO

030303u

00 00 00 00 CO v£> CO vO CO CO CO CO VO CO CO •H •rH o CM CM <r CM CM m o^ 00 Ov 00 00 m C7^ 00 o^ 00 00 u 4-1 o 1—1 f—

CM 1—

rH C3^

O 00

O 00 00 CTvO 00

O 00 00

CM o o vD o o 1—O cr> vO Ov Ov vO av vO as as •1—)

u

O 1—

1—

O 1—

tH o O o O

OO o O o O o o c 03 • • • • • • • • • • • • • • • • • M CX

CO f-H 1—1 1—

CO

m 00

m 00 00 CO

m 00

m 00 00

MO f-H in rH ..

mm CO as CO as as CO O' CO as as

1—

Ov CM CM CM CO as CM as CM CM CO CM as CM CM o o r^ O o I—

m rH m m o I-H m 1—

in

m o o o O O o o o i-H O i-H 1—1 o O o 1—1 rH o o o o O o o o O O O o o o o o o o

03

<u <u •H >-i 4J «H •H y-i

03 t-i 4^ 03 CO OU-lo

03 4J C»0 C CO 0) e

4-J CO X a wco

•H

03

03 03

03N

U

CX. C/D ‘CU0

C/D

03

O

O

4->

C

CJ 03

03 03 4J 03

03

U

Q PU

03

O

CJ CO

59

Figure

A2.

Detector

Alternative

Probability

TreeScenario

Group G
w

COO Q) hJ U

•H

m CO

CO CM fO o

CN

</y </>

vO

00

<0-

00

CSJ org

</>

vO

00

</>

CTv \D

00

COCOCO

00

egO

CN

<0-

vO

00

coco

egO

CM

COON

vO

00

COON

n£> 00

coin

00CO

00

CNoCN

COo>vO

00

coco

CNo

o\

NO

00

coC7N NO

00

coco

.-H

U

rH Q) o o- Q

cn

mm eg CO CO 0 CN NO CN ,—

00 00 00 00 CO NO CO NO 0 CN (r-j CN CN UO <3N 00 ON 0 r-H (M CN rH 1—1 ON 0 00 0 CN c c NO 0 0 1—1 SO ON sO 0 tM rM 0 iH 1—1 0 0 0 0

SO NO 0 eg vO CN NO NO

CO

G

G

U

*H *H CO CO CO vO CO NO CO CO U IM 00 00 in ON 00 ON 00 00 0

00 00 ON 0 00 0 00 00 •r-)

U

ON O' »H NO ON NO CTs ON

C

G 0 0 0 0 0 0 0 0 M P

CO

0) cu

•rl J-j CO 1—

r-- 1—iM CO

m 00m 00 00 CO

m 00

m CO 00 •H IM NO I—

m 1—

in in <• CO CTN CO ON ON CO ON CO ON Os

«—ON eg ON •• CN CN CO

C7N CN as CN CN CO ON eg Os CN CN cd

U 0 0 r-* 0 0 1—

in rH

m in 0 1—1 in 1-H inm

M

G 0 0 0 0 , 0 0 0 0 I—

0 1—

1—1 0 0 rH 0 iH 1—

cd P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 p

CMO

(U

M 00 C CO (U s M CO X P ao

•H

CO

CO 0)

(1)

N

U *H P CO 3

COM 0) O G

•W

C

G

G

G CO M G G U P P 0

•HU P

cd

D c 0 G U G 0

CO

60

Figure

A3.

Sprinkler

Alternative

Probability

TreeScenario

Group G
in 00 CO in 00 r-H 00 <N ON CM ON ON 00 CM CT\ cn O O SO so m O so

CM 00 CN CO 00 CM 00

</> CO- <J> <J> <o- </> •CO- <j>

w

w

O CJ ^ u

•H

00 n- in 00 00

CM (3N ON 00 CM ON CM ON ON OJ o so so rn o so O so so 0

CM 00 CO CM 00 CM 00 00 Q </> </> </> •CO- CO* •CO- •COcn m m CM m m O CM SO CM SO so o CM SO CM SO so

00 00 00 CO m SO m so cn cn m SO cn so cn cn c C^J CM <r CM CM m ON 00 On 00 00 m ON 00 ON 00 CO o t-H 1—

CM 1—1 1—

ON O CO O 00 00 ON O 00 O 00 00

CM O o vO o O r-l SO CJN SO ON ON i-H SO C3N SO ON ON O 1-H rH o 1—1 i-H o O o O o o O O o O o o

<D ^ P

0) *H

<4-1 3 P

•r-) 0) C Cl M

cn n- iH r-. 1—1—1 cn m 00 in 00 00 cn m 00 m 00 00

a; •HU

•H

fire

so rH in iH m in •<3- m ON cn C3N C5N -<r cn ON cn C3N C7N rH c^ CM ON CM CM cn ON CM C7N CM CM cn ON CM On CM CM CO u o o o . r-. o r—

m iH m m o rH m iH in in 4J o o o o o o o o o rH O rH 1—

cz> o iH O iH »H cd Cl o o o o o o o o o O o o o o O o O o

61

Figure

A4.

Sprinkler/Detector

Alternative

Probability

Tree

-

Scenario

Group

G
NBS*n4A (REV. 2 «BC) U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBSIR 82-2551

2. Performing Organ. Report No. 3. Publication Date

August 1982

4. TITLE AND SUBTITLE

EVALUATING ALTERNATIVE STRATEGIES FOR REDUCING RESIDENTIAL FIRE

LOSS - THE FIRE LOSS MODEL

5. AUTHOR(S)

Alan Gomberg, Benjamin Buchbinder and Fred L. Offensend

6. PERFORMING ORGANIZATION (If joint or other than NBS, see Instructions) 7. Contract/Grant No. national bureau of standards

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

WASHINGTON, D.C. 20234 Interim Report

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City, State, ZIP)

10.

SUPPLEMENTARY NOTES

Document describes a computer program; SF-185, FIPS Software Summary, is attached,

11. ABSTRACT (A 200-word or less factual summary of most significant Information. If document Includes a significant

bibliography or literature survey, mention (t here) This report provides a preliminary documentation of a decision analysis framework

for evaluating alternative residential fire loss reduction strategies. The framework,

when it is completed, will provide a systematic means for assessing the costs and

losses occurring under different intervention strategies. The current report focuses

entirely on the problem of assessing fire losses, as this is where most of the

uncertainty on system performance occurs. Subsequent reports will address the cost

of the alternatives, after which the alternatives can be compared on a comprehensive

cost/benefit basis.

Three alternatives are considered in this preliminary report; smoke detectors,

residential sprinkler systems with standard commercial-type sprinkler heads, and a combination of both measures. Based on the preliminary input data developed, the

preliminary analysis indicates that both sprinklers and detectors are effective in

reducing life loss. Detectors appear to be somewhat more effective in reducing

personal losses, however, because of their earlier warning capability. Sprinklers

appear to be significantly more effective than detectors in reducing property loss

because of their earlier start in initiating suppression. Work is underway refining

the loss model and developing a cost model so that meaningful cost/ben^fit comparisons

of the alternatives can be conducted.

12. KEY WORDS (S/x to twelve entries; alphabetical order; capitalize only proper names; an'U~s€piTrare -key- wofd-s by semicolons)

lost benefit analysis; decision analysis; fire losses; fire safety; residential

buildings; smoke detectors; sprinkler systems

13. AVAILABILITY 14. NO. OF

PRINTED PAGES

Unlimited

1 1 For Official Distribution, Do Not Release to NTIS

1

1 Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

66

15. Price 20402.

Order From National Technical Information Service (NTIS), Springfield, VA. 22161 $ 9.00

USCOMM*DC 6043-P80