An analysis of fire sizes, fire growth rates and times between events using data from fire investigations

ARTICLE IN PRESS

Fire Safety Journal 39 (2004) 481–524

An analysis of fire sizes, fire growth rates and times between events using data from fire investigations P.G. Holborna,*, P.F. Nolana, J. Goltb a

Explosions and Fire Research Unit, Chemical Engineering Research Centre, London South Bank University, 103 Borough Road, London SE1 0AA, UK b Fire Safety Department, London Fire Brigade, Hampton House, 20 Albert Embankment, London SE1 7SD, UK Received 6 March 2003; received in revised form 8 April 2004; accepted 13 May 2004

Abstract London Fire Brigade’s real fire library is a database of information collected from real fire incidents by specialist teams of experienced fire investigators operating in the Greater London Area. A sample of this data collected over a five-year period has been used to characterise the distributions of fire sizes, fire growth rates and times between events that occur in building fires in a form suitable for use with probabilistic risk assessment. The effect of occupancy type, ignition source, first material ignited, and first aid fire-fighting by the occupants on the form of these distributions was then examined. Incidents that produced very large losses, rapid growth rates and extended time delays were also analysed to try to determine the reasons why such extremes occur in real fires. r 2004 Elsevier Ltd. All rights reserved. Keywords: Fire investigation; Real fire data; Fire size; Fire growth rate; Log-normal distribution; Incident database

1. Introduction The cost of uncontrolled fires in buildings in terms of both human lives and damage to property is a high one. Each year direct losses due to fires are estimated to *Corresponding author. Tel.: +44-0-20-7815-7980. E-mail address: [email protected] (P.G. Holborn). 0379-7112/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.firesaf.2004.05.002

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Nomenclature A1 A2 EðxÞ EðaÞ N Q q00 x x50 x95 t t1 t2 tdb Dt12 t95 z a a95 m ma s sa

area of the fire when it was first discovered (m2) area of the fire when the fire brigade arrived (m2) expected value of x (m2) expected value of a (kW s2) number of fires heat release rate of the fire (kW) average rate of heat release per unit area of the fire (kW/m2) fire size (m2) the median value of x (m2) 95th percentile of the log-normal distribution of x (m2) time (s) time after ignition when the fire was first discovered (s) time after ignition when the fire brigade arrived (s) time for fire to double in area (min) time interval between discovery of the fire and arrival of the fire brigade (min) 95th percentile of specified time distribution (min) natural log of x, ln(x) fire growth parameter (kW/s2) 95th percentile of the log-normal distribution of a (kW/s2) mean value of ln(x) mean value of ln(a) standard deviation of ln(x) standard deviation of ln(a)

account for between 0.1% and 0.4% of a country’s GDP [1,2]. To try to reduce such losses it is important to be able to both understand and quantify the behaviour and consequences of building fires in practice. The shift in emphasis from a prescriptive to a performance based approach to fire safety engineering that has taken place in recent years also means that a wide variety of data from real building fires is required in order to be able to adequately quantify and assess the risk of fire (e.g. distributions characterising fire damage, fire growth rates, times between events, etc.). Fire investigation has the ability to provide such detailed information. London is a city with a population of approximately seven million people. It shares many similarities with other large cities around the world including the problem of uncontrolled building fires. In London, teams of specialist fire investigators are employed by London Fire Brigade working in shifts to provide 24 h coverage of the 32 administrative areas (boroughs) of the Greater London Authority together with the Corporation of the City of London (a total area covering 1578 km2). Their duties involve identification of the most probable causes and sources of ignition of fires and the consideration of all issues involving fire

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development and the performance of fire protection. This is achieved through the detailed examination of the fire scene, interviewing of witnesses and research into the history of the individual buildings concerned. Not all incidents would merit the attendance of a fire investigation unit (FIU), since many fires are relatively minor and routine and do not warrant detailed examination by specialist fire investigators. Thus, the investigators only attend fires that meet certain criteria. A FIU automatically attends: *

*

Four pump fires and above, i.e. all fires where four or more fire engines are sent to the scene of the fire. ‘‘Persons reported fires’’, i.e. fires where people are reported to be inside the burning building when the call to the brigade is made.

The incident commander at the fire scene can also request the attendance of a FIU at a fire based upon: human considerations (e.g. fatalities, serious injuries, rescues, evacuations); operational considerations (e.g. fires where the cause or source of ignition is suspicious or would otherwise be recorded as unknown); fire safety considerations (e.g. buildings with automatic fire detection devices or fire suppression equipment); fires of special interest and notifiable fires (e.g. new materials and construction techniques). Taken together such incidents represent around 25% of all the primary fires attended by London Fire Brigade each year. These investigated fires effectively represent the most ‘‘significant’’ incidents that have occurred. Since 1996, the data collected by London Fire Brigade investigators at the scene of each fire incident they attend has been entered into a database known as the real fire library (RFL) [3,4]. The range of information collected into the Library includes basic incident statistics (type of property, location, cause of fire and source of ignition), details of the fire scene, fire development, fire detection and protection and building egress. On average information from investigated fires is collected into the library at a rate of around 4000 incidents per annum. In this paper a sample of data collected in the RFL in the five-year period between 1996 and 2000 has been analysed to try to characterise the distributions of fire-losses (area of damage) and fire growth rates that occurred in both residential dwellings and other types of buildings. The factors examined include: * * * *

occupancy type ignition source effect of first aid fire-fighting by the occupants first material ignited.

The distributions of times between the ignition, discovery, call to brigade and brigade arrival events have also been considered. In addition, extreme cases (i.e. fires that produced very large losses, rapid growth rates and extended time delays) were also examined to try to identify the reasons why such extremes occur in real fires.

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2. Background 2.1. Fire size The distribution of fire losses in buildings has been investigated extensively by Ramachandran [5–7] and Shpilberg [8]. These studies observed that fire loss has a skewed (non-normal) distribution—most fires are small while only a few grow large. However, while these large losses occur at the tail of the probability distribution they account for a high percentage of the total financial value claimed [2]: ‘‘Large fires constitute only a small percentage, about 5 to 10 percent, of the total number of fires but contribute more than 50 per cent to the total loss in all fires.’’ Similarly, Fontana et al. [9] surveyed (financial) fire loss statistics based on the insurance claims for 40,000 building fires (including even very small losses) in Switzerland for a period between 1986 and 1995, providing tabulated distributions for the number of fires that occurred in each fire loss class for different occupancies. They report the total losses corresponding to each fire loss class finding that the 2% of the fires, which caused the most damage (i.e. over 100,000 Swiss Francs) were responsible for 75% of the total amount of losses. Although the Pareto distribution has been considered for modelling fire insurance claims it is the log-normal distribution that has been most widely recommended to characterise fire loss distributions [2]. Ramachandran [10,11], for example, has used log-normal fire loss distribution to show the value of sprinklers in reducing fire loss and in the application of extreme value theory to estimate the average (and total) loss in all fires, from the data on large losses alone. Once the distribution of fire loss has been characterised it can be used with Monte Carlo simulation techniques [12] to help perform a Probabilistic Risk Assessment of fire risk in a building [13]. It has also been shown that the fire damage area increases with both the floor area of the compartment in which the fire originates and the total floor area of the building (i.e. the ground floor area multiplied by the number of floors) in accordance with a power law relationship [14–16]. Such correlations have been used to compare the variation in fire damage area with building/compartment area in sprinklered versus non-sprinklered buildings. 2.2. Fire growth rates The heat release rate is one of the most important factors in the analysis of a fire [17]. Examination of data from fire tests and real fires [18] suggests that the growth in heat release rate during the early stages of a fire may often be reasonably approximated by a time squared (t2 ) growth curve of the form Q ¼ at2 ;

ð1Þ

where Q is the heat release rate of the fire (kW), t the time after ignition (in s), and a the fire growth parameter (kW/s2). Specific values of a have been selected to characterise slow, medium, fast and ultra-fast fire growth rates [19,20].

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However, as Morgan [21,22] points out for a given occupancy there will actually be a distribution of possible fire growth curves: In reality, of course, any actual sample of fires occurring in the same nominal occupancy will never be describable by a single growth curve. There will be a distribution of growth curves depending on such factors as variations in fuel layout and location of the initial ignitiony. In practice, however, the probability distribution of growth curves is rarely known for an occupancy of interest to the designer. Ramachandran [23] has deduced such probability distributions for (exponential) fire growth curves using UK fire statistics. Unfortunately the limited data available meant that only a restricted range of occupancy types could be analysed. Morgan notes that ysimilar studies appear highly desirable for the major occupancies such as retail and offices. Working for the UK Home Office, Wright and Archer [24] analysed 1990s UK fire data and derived relationships between the ‘‘age’’ of the fire (time from ignition until fire brigade attendance at the fire) and the level of fire loss incurred (average area of damage in m2) for a range of different building occupancy types. A linear regression line was fitted to the data for each occupancy type, with the slope of the line representing the rate of damage (m2/min) produced by a delay in attendance. Occupancies were then categorised in accordance with this (linear) fire growth rate * * * *

high: public buildings, factories and universities medium: retail, hotels and schools low: hospitals, licensed premises and offices very low: care homes.

Their review of previous research found that the application of fire growth models to assigning fire cover was limited for a number of reasons, one of which was that ythe distribution of fire sizes, rates of fire growth etc. has not been reported. Wright and Archer also examined the level of fire loss incurred in terms of average area of damage in m2 and financial loss (using insurance claim data) for a range of different building occupancy types. Using this information they were able to derive a relationship between fire brigade response time and financial fire loss for application to fire cover risk assessment. 2.3. Previous real fire library studies Sardqvist et al. [25] have previously examined a sample of the RFL data for fires in non-residential premises in London 1994–1997. They obtained Complimentary Cumulative Distribution Functions (CCDF) for the different time intervals and fire areas recorded in the RFL and were able to use the data to derive correlations between the water flow rate and total water applied to a fire and the fire damage

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area. The majority of fires they analysed did not spread after the arrival of the fire brigade, while half did not grow any larger after they were discovered, due to selfcontainment. They identified three categories of fire: * *

*

Small self-contained fires that were confined to a single small object (1 m2 or less) Medium fires that spread in size by factor of up to ten after discovery but which were contained upon fire brigade arrival (1–40 m2) Large fires that continued to spread in size after fire brigade arrival typically by a further factor of ten (40–80 m2). However, they did not consider the effect of different occupancy types on fire size.

Charters et al. [26] made a preliminary analysis of an RFL data sample, investigating fire growth rates, fire detection times and some aspects of human behaviour (number of occupants in a building at the time of a fire and their premovement time). They obtained a probability distribution of fire growth coefficients (assuming t2 growth) for shops and commercial premises. They also found a probability distribution of fire detection times (from ignition) for shops and commercial premises showing an extended tail. This tail was mainly due to incidents where there were no occupants (i.e. fire occurred outside business hours), no automatic fire detection system installed or cases where the fire did not spread beyond the first item in an unoccupied part of the building. The results also suggested the existence of a dependency between fire growth rate and detection time: the faster the fire growth rate, the shorter the time taken to detect the fire.

3. Method 3.1. The real fire library data used in the analysis A sample of incidents containing suitable fire spread data was extracted from the RFL for the five-year period between 1996 and 2000. The larger number and special nature of fires in dwellings suggested that the fires in this occupancy type should be analysed separately to those in other buildings. An overall sample of 2044 fires in residential dwellings and 464 fires in other types of buildings was therefore used. Data employed in the analysis included: (i) Event times: (a) time of ignition of the fire, (b) time the fire was discovered, (c) time the fire brigade was first called out, (d) time of fire brigade arrival at the scene of the fire. (ii) Fire areas: (a) area of the fire at the time of discovery, (b) fire area when the fire brigade arrived at the scene of the fire, (c) final fire damaged area.

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Type of occupancy in which the fire occurred. Ignition source. First material involved in the fire. Any first-aid fire-fighting actions taken by the occupants.

However, not all of these items of data were recorded for every incident (i.e. some items of data were missing for certain incidents). 3.2. Uncertainties associated with the data collection process An old computer science adage goes ‘‘garbage in–garbage out’’. This is equally true for the data collected into the RFL. The quality of information obtained from the RFL will only be as good as the data that has been entered into it and how well that data is manipulated and retrieved once it is in the database. The data entered into the database has effectively been filtered through specially trained fire investigators. This should have provided a measure of quality control on the data collected. However, by the very nature of the fire incidents involved, some of the data collected, in some cases, will be subject to a degree of uncertainty and subjectivity (this is particularly true of the time of ignition). Such uncertainties are difficult to quantify, but where a sufficient quantity of data is available over an ensemble of incidents, it is assumed here that statistically meaningful distributions may still be obtained. Inevitably, with the collection and entry of large amounts of data into the database by many investigators, mistakes are made and errors occur. A validation process has therefore been undertaken by the authors to try to identify and correct those instances where the data is suspicious or incorrect. Date and time data fields seem to be especially prone to error. It is all too easy to transpose digits or day and month entries or forget to use the next day’s date when an incident spans the times around midnight. Such date errors can render any analysis using them meaningless, producing very large time differences, illogical sequences of events and totally distort any statistics calculated using the erroneous values. While some of these date/time values are easy to identify (e.g. years like 1901) most when taken in isolation appear perfectly valid. It is only when compared with other date/times recorded for the same incident that the errors become apparent (e.g. fire discovery and fire brigade arrival at dates spaced months apart). Routines have therefore been implemented and used to check each of the date/time fields stored in the relevant tables, relative to the other data/time fields in that table to ensure that the values stored are self-consistent (i.e. check that the time intervals between them are smaller than a specified threshold) and if appropriate that they occur in a logical order (e.g. time of fire brigade arrival should not occur before time of fire start). Where possible cross-checks have also been made between fields in different tables to check for consistency. Incidents where the data specified are inconsistent have been identified and either corrected or removed.

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There are particular difficulties in estimating the actual time of ignition. The specialist London Fire Brigade investigators rely on a combination of their own judgement and the eyewitness interviews to provide the best possible estimate. In many cases it was not possible to obtain a reliable estimate and such incidents have therefore been excluded from analysis. However, in some cases it was possible to give either a specific estimate or to set an upper and lower bound upon the time of ignition. Such estimates represent the ‘‘best guess’’ of the ignition time and should be treated with a degree of caution, particularly for fires which may have smouldered for long periods and where the exact nature of the ignition ‘‘event’’ is hard to define. For dwellings where there was a relatively large sample of data available for a single occupancy type only the incidents where a specific estimate of ignition time was given by the investigators have been used in the analysis. However, in the case of other buildings, where the total sample size available was smaller and divided over a number of different occupancy types, a greater degree of uncertainty in the ignition time has been allowed to increase the number of incidents open to analysis. Thus, incidents where the difference between the upper and lower bound on the time of ignition was reasonably small (15 min or less) were also included in the analysis. In such cases the ignition time used in any calculations has been estimated as the average time, midway between the upper and lower bounds specified. 3.3. Occupancy groups As part of the analysis, other buildings were divided into the following occupancy groups (similar to those used by Wright and Archer [24], but with the addition of warehouses): * * * * * * * * * *

care homes (for nursing the elderly and the mentally handicapped) factories (industrial premises, manufacturing) higher/further education (university, college, adult education) hospitals (general, psychiatric) licensed premises (e.g. public house, restaurants, bars etc.) offices public buildings (e.g. museums, art galleries, libraries) retail (shops, department stores, fast food restaurants) schools (infant, primary, secondary) warehouses (storage).

3.4. Fire growth parameter estimation The (average) fire growth parameter, a (kW/s2) for each incident was estimated by performing a least squares fit of a t2 growth curve based on areas of the fire when it was discovered and when the fire brigade arrived and the time intervals between ignition and discovery and ignition and fire brigade arrival, together with the assumption that the fire area was zero at the time of ignition (i.e. the curve passes

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through the origin) such that: a¼

q00 ðA1 t21 þ A2 t22 Þ ; t41 þ t42

ð2Þ

where q00 is the average rate of heat release per unit area of the fire (kW/m2); A1 the area of the fire when it was first discovered (m2); A2 the area of the fire when the fire brigade arrived (m2); t1 the time interval between ignition and discovery of the fire (s); and t2 the time interval between ignition and fire brigade arrival (s). Based upon estimates suggested in the literature [19,20] a value of 250 kW/m2 was used to represent the heat release rate per unit area of the fire, q00 ; for all of the occupancy groups used except for retail (where a value of 500 kW/m2 was assumed) and warehouses (storage). In the case of warehouses, the heat release rate was estimated by using a method suggested in the CIBSE guide [27] (for well-ventilated compartment fires) dividing the fire load density for a given occupancy by a conservative burning time of 20 min (1200 s). Here we consider the early stages of fire growth, before the fire becomes ventilation controlled (e.g. the first 20 min of burning). In these early growth stages it is assumed that the fire will be fuel controlled and that a heat release rate per unit area may be used in the first approximation. Based upon survey data, DD 240 [20] gives the fire load density for manufacturing and storage as 1180 MJ/m2, while Buchanan [28] suggests a similar value of 1200 MJ/m2 for storerooms. Using these values, the heat release rate per unit area of the fire for warehouses/storage was therefore estimated to be 1000 kW/m2. The fire growth parameter values calculated were subsequently classified in accordance with the scheme shown in Table 1. Class boundaries were selected as the midpoints between the specific values of a that are traditionally used to characterise slow, medium, fast and ultra-fast fire growth rates [19,20]. An additional very slow growth class was also added to delineate those fires, which exhibited extremely low rates of growth (typically found for smouldering fires). 3.5. The log-normal distribution The distributions of fire sizes and fire growth parameter values observed were both reasonably well approximated by the log-normal distribution.

Table 1 Fire growth parameter value classification scheme employed in the analysis Growth rate class

Range of a (kW/s2)

Time to reach 1055 kW (s)

Very slow Slow Medium Fast Ultra fast

o 0.000412 0.000412–0.006594 0.006594–0.026375 0.026375–0.1055 >0.1055

>1600 400–1600 200–400 100–200 o100

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To illustrate this, Fig. 1 shows the frequency histograms and cumulative probability plots for both fire damage area and fire growth parameter data obtained from the sample of fires investigated in residential dwellings (taken as a whole). If the fire damage area is log-normally distributed then the natural logarithm of the fire damage area will be normally distributed. Fig. 1(a) shows the frequency histogram of the natural logarithm of the fire damage area. A comparison with the expected normal distribution curve (also shown) suggests that the natural logarithm of the fire damage area is (approximately) normally distributed and hence that the fire damage area distribution is approximately log-normal in form. Further evidence of this lognormal behaviour is given by the cumulative probability plot shown in Fig. 1(b). The straight line shown on this plot indicates the variation that would be expected for a log-normal distribution (the dashed lines represent the 95% confidence intervals). For the most part, the fire damage area data does indeed fall quite close to this line (i.e. it varies in accordance with the log-normal distribution) although there is some

(a)

(b)

(c)

(d)

Fig. 1. Frequency histograms and cumulative probability plots for the fires investigated in residential dwellings, comparing the distribution of the data sampled with the log-normal distribution fitted, (a) frequency histogram of the natural log of fire damage area data along with normal distribution curve, (b) probability plot of fire damage area data, (c) frequency histogram of natural log of fire growth parameter data along with normal distribution curve, (d) probability plot of fire growth parameter data.

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deviation apparent at the tails. The deviation at small fire damage areas suggests that the observed number of small fires is greater than would be expected according to the log-normal distribution. Similarly, very large damage fires occur more frequently than would be expected if they were distributed log-normally. It also apparent that there are a series of ‘‘steps’’ in the distribution of data points. These steps reflect the natural tendency of the investigators to round the fire damage area values recorded (e.g. many fires with areas close to 1 m2 in size will be recorded as having an area of 1 m2 exactly). The corresponding frequency histogram and cumulative probability plots found for the fire growth parameter data (for the sample of fires investigated in residential dwellings) are shown in Figs. 1(c) and (d). The log-normal distribution would also appear to approximate the distribution of the fire growth parameter data reasonably well. However, a measure of deviation is once again apparent at the tails of the distribution, suggesting that very small fire growth rates occur more frequently and very large fire growth rates occur less frequently than would be predicted if they were strictly log-normal in form. A similar pattern of (approximately) log-normal behaviour is found for the distribution of both the fire damage area and fire growth parameter data, obtained from the sample of fires investigated in other buildings, as shown in Figs. 2(a)–(d). The histograms of the logarithm of both of the variables appear to be reasonably normally distributed, whilst the data in both of the cumulative probability plots falls close to the log-normal line. There is also generally less deviation apparent at the distribution tails (particularly in the case of the fire growth parameter data) although there is again evidence that very large damage fires (this time in other building types) occur more frequently than would be otherwise predicted. If x is log-normally distributed and z ¼ ln x, then z is normally distributed. The log-normal distribution parameters (m; s) can therefore be estimated using m ¼ meanðzÞ; s ¼ standard deviationðzÞ: The expected value of a log-normal distribution is the value that is most likely to occur (i.e. the average fire size or growth rate) and is given by   s2 EðxÞ ¼ exp m þ ð3Þ 2 while the median value of the distribution is simply x50 ¼ expðmÞ:

ð4Þ

It is also useful to have some measure of the location of the tail of the distribution to provide an indication of the extreme values that could occur. The 95th percentile of the log-normal distribution provides a single parameter measure of the largest value that will occur in 95% of cases and is given by [29] x95 ¼ expðm þ 1:645sÞ:

ð5Þ

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(a)

(b)

(c)

(d)

Fig. 2. Frequency histograms and cumulative probability plots for the fires investigated in other buildings, comparing the distribution of the data sampled with the log-normal distribution fitted, (a) frequency histogram of the natural log of fire damage area data along with normal distribution curve, (b) probability plot of fire damage area datac (c) frequency histogram of natural log of fire growth parameter data along with normal distribution curve, (d) probability plot of fire growth parameter data.

4. Results for residential dwellings 4.1. Fire size The complimentary cumulative distribution function (CCDF) for fire damage area for the sample of fires investigated in residential dwellings is shown in Fig. 3 (the CCDF gives the probability of a fire exceeding a particular fire size [29]). One-third of the dwelling fires in the sample had a fire area less than or equal to 1 m2 in extent, while the fire size was under 10 m2 in almost 80% of cases. However, there were also some incidents in the tail of the distribution where a much larger fire damaged area was achieved. In a number of these cases the fire propagated into a roof space or ceiling void and was subsequently able to spread over a large area of the structure. The distribution of fire sizes was found to be reasonably approximated by a lognormal distribution (i.e. the natural log of fire size is approximately normal), estimating the mean and standard deviation from the transformed natural log variable (z ¼ ln x). The log-normal parameters characterising the distribution of fire

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1 Residential dwellings

0.9

Other buildings

Probability of exceedance.

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.01

0.1

1

10

100

1000

10000

Fire damage area (m2)

Fig. 3. The CCDF for fire damage area for the samples of fires investigated in both residential dwellings and other buildings. Each distribution gives the probability of fire damage exceeding a given area.

sizes found for all dwelling fires in the sample are given in Table 2(a). The corresponding expected fire size value found for this distribution using Eq. (3) was 7 m2, while the location of the 95th percentile of the distribution, x95 ; obtained with Eq. (5) was 27 m2. From the information collected by the fire investigators it is possible to discriminate between those fires where the occupant took some form of first-aid fire-fighting action against the fire (e.g. poured water over the fire or used a portable fire extinguisher) and the fires where no such action was taken. The log-normal distribution parameters, expected fire sizes and x95 values calculated for the two corresponding dwelling fire size distributions are shown in Table 2(b). For the distribution of dwelling fire damage areas where the occupants took no fire-fighting action the expected fire size was 7 m2, but for the cases where the occupants did take action the expected fire size was only 5 m2. The effect of occupant fire-fighting action was also reflected in the x95 fire size values which was 27 m2 for the distribution where no fire-fighting action was taken by the occupants, but 19 m2 for the distribution where some form of action was taken by the occupants. A twosample t-test confirms that there is a statistically significant difference between the means of the two log-normal distributions (po0:001), i.e. m is significantly lower for fires where the occupant took some form of fire-fighting action. It is also possible to discriminate between the dwelling fires investigated on the basis of whether a smoke detector was installed in the property. The log-normal parameters characterising these two types of distribution are shown in Table 2(c). A two-sample t-test shows that the difference between the means of the two distributions (and hence the median fire damage area) is statistically significant (po0:001). However, both the expected fire damage and x95 damage values found for the distribution of fire sizes where a detector was installed are only slightly lower than those found for the distribution representing the cases where no detector was fitted. This would suggest that installation of a smoke detector in dwellings does not

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Table 2 Log-normal parameters characterising the distribution of fire damage area in dwelling fires sampled Fire damage area dwellingsa

Estimated log-normal distribution parametersb EðxÞ (m2)

x95 (m2)

N

m

s

Group by (a) All dwelling fires

1991

0.90

1.45

7

27

(b) Fire fighting action by occupant Yes No

352 1639

0.40 1.01

1.55 1.40

5 7

19 27

(c) Smoke detector installed Yes No

290 1701

0.61 0.95

1.54 1.42

6 7

23 27

94 258 75 202 77 366 197 551 155 17

0.21 0.17 0.69 0.55 0.81 1.18 1.09 1.19 1.11 1.95

1.36 1.58 1.32 1.52 1.39 1.22 1.35 1.35 1.51 1.60

3 4 5 6 6 7 7 8 9 25

12 16 17 21 22 24 27 30 36 98

(d) Source of ignition White goods Cooking appliances TV, hifi, etc. Heating appliances Other sources Smoking materials Candles Other naked flames Electrical supply and lighting Unknown

(a) All dwelling fires. (b) By whether fire-fighting action was taken by the occupant. (c) By whether a smoke detector was installed. (d) By source of ignition. The parameters m and s are the mean and standard deviation of the natural logarithm of the fire size. The expected fire size of the distribution, EðxÞ; calculated using Eq. (3). The fire size at the 95th percentile of the distribution, x95 ; calculated using Eq. (5). a Based on a sample of 1991 fires in other buildings where the final fire damage area was specified. b N is the number of fires in the group sampled.

in itself produce a large reduction in the amount of damage that can be typically expected. Intuitively, it might be expected that some forms of ignition source would be more likely to give rise to larger fires than others. For example, fires due to domestic appliances such as washing machines might often be contained within the framework of the appliance and so not spread to other items. On the other hand fires started by smoking materials left on an item of furniture might be expected to be more likely to spread, resulting in larger fires. This expectation does generally appear to be supported by the data as can be seen by examining the expected fire-size and x95 values obtained for each of the ignition source groups shown in Table 2(d). The fire size distribution for white goods (such as washing machines, fridges and freezers) exhibits the smallest expected fire size (3 m2) and x95 (12 m2) values. Perhaps more of a surprise is that the largest expected fire size

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(9 m2) and x95 (36 m2) values (apart from unknowns) are generated by the fire size distribution found for incidents started by electrical supply and lighting equipment. On the basis of the expected fire size and x95 values, the ignition sources can be divided into two broad groups: those more likely to produce smaller fires (white goods and cooking appliances) and those more likely to produce larger fires (electrical supply and lighting equipment, naked flames, candles and smoking materials). The former could be due to containment of the fire (by the case of the appliance) or the likelihood of rapid discovery, while the latter group features ignition sources, which would be more likely to spread to another item (different forms of naked flame) or are likely to go for a long time before detection (electrical supply and lighting, smoking materials). 4.2. Time from ignition to discovery (discovery time) Descriptive statistics for the time from ignition to discovery (and for the other time intervals examined) in the dwelling fires sampled are summarised in Table 3. The majority of the dwelling fires in the sample were discovered relatively quickly with a median time from ignition to discovery of 4 min and 75% of the fires being discovered in the 10-min period after ignition. However, there were also a number of cases located in the tail of the distribution where there was a prolonged interval of several hours between ignition and discovery of the fire. Such long discovery times usually occurred in incidents where there was a smouldering fire (typically involving smoking materials igniting settees, sofas or bedding) or where there was nobody in the vicinity to discover the fire. For example, a smouldering towel covering hair tongs (an electrically operated hair styler) left on by accident (7 h) a smouldering electric blanket (7 h)

*

*

Table 3 Descriptive statistics for selected time intervals in the dwelling fires sampled Dwellings

Descriptive statistics

Times interval (minutes) a

Ignition and discovery Discovery and call to FBb Call and arrival of FBc Ignition and FB arrivald a

N

Min

Max

Median

Mean

Std. dev.

t95

584 1651 1761 525

0 0 1 2

430 48 21 438

4 2 4 11

16.2 2.2 4.6 24.2

50.9 2.6 2.0 52.3

53 6 8 65

Based upon a sample of 584 fires in dwellings where specific ignition and discovery times were given. Based upon a sample of 1651 fires in dwellings where the discovery and call times were specified and the fire brigade took some form of action against the fire. c Based upon a sample of 1761 fires in dwellings where the call and arrival times of the fire brigade were specified and the fire brigade took some form of action against the fire. d Based upon a sample of 525 fires in dwellings where the specific ignition and fire brigade arrival times were supplied and the fire brigade took some form of action against the fire. b

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a smouldering cigarette left on a settee (5 h) the deliberate ignition of paper in an unoccupied house (6 h).

* *

Table 4 shows a breakdown of the discovery time in dwelling fires by the different types of ignition source that started the fire. The majority of the cooking fires in dwellings were found in less than 5 min (63%), while few such fires took more than 30 min to be discovered (2%). The majority of the dwelling fires involving other naked flames and TV’s and hifi’s were also found in under 5 min (67%). Conversely, the majority of dwelling fires involving smoking material took more than 5 min to be discovered (77%), with a significant proportion having long discovery times in excess of 30 min (26%). There was also a greater proportion of fires due to electrical distribution and lighting that took in excess of 5 min to be found (61%), while 15% had discovery times in excess of 30 min. 4.3. Time from discovery to call to fire brigade (call time) For the dwelling fires sampled, the median time between discovery of the fire and the call to the fire brigade was 2 min (Table 3). Almost half (44%) of the calls to the fire brigade were made in the first minute after discovery of the fire while 95% of calls were made in under 6 min. There were also a small number of incidents where there was a considerable delay between discovery of the fire and the call to the fire brigade being made. In some of these cases a smouldering fire

Table 4 A breakdown of dwelling fires by source of ignition and time between ignition and discovery Dwellings

Time from ignition to discovery of fire a

Source of ignition

Discovered at ignition (%)

Under 5 min (%)

5–30 min (%)

More than 30 min (%)

Number of fires

Smoking materials Other naked flames Cooking appliances Candles Tv, hifi etc. Electrical supply and lighting White goodsb Heating appliances Other sources Unknown Allc

1 25 23 4 46 24

22 42 40 43 21 15

51 29 35 48 21 46

26 4 2 6 12 15

88 171 107 54 24 41

7 30 12 — 19

33 19 38 33 33

53 40 42 67 39

7 11 8 — 9

15 57 37 3 584

a

Rows may not add to 100% due to rounding error. Includes washing machines, fridges, dishwashers, etc. c Total based on 584 fires investigated in dwellings where specific ignition and discovery times were given. b

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that was discovered and apparently extinguished by the occupant, was subsequently found to be still burning and required the assistance of the fire brigade. For example, in one incident a person placed a heater too close to the bed and awoke to find the bed alight. They believed the fire to be extinguished, but woke up nearly an hour later suffering with burns and the realisation that the fire was still smouldering (thus the call to the brigade was made 48 min after the fire was first discovered). In other cases a neighbour or passer-by discovered the fire, but delayed calling the brigade either because they were not sure, thought some one else would or because they did not know how to make such a call. Fig. 4 shows a comparison between the call time CCDF for cases where the occupant took first-aid action against the fire with the CCDF distribution of call times where no fire-fighting action by the occupant was taken. The distribution for dwelling fires where the occupant took some form of fire-fighting action has a higher proportion of fires exceeding a given call time. These results suggest that incidents where the occupant took first-aid fire-fighting action were more likely to have a longer delay between discovery of the fire and call to the brigade (i.e. evidence that the use of first-aid fire-fighting action by the occupant can delay the call to the fire brigade). A Man-Whitney U test [30] performed on these groups also implies that there is a statistically significant difference in the location of these two distributions (po0:001). 4.4. Time from call to arrival of Fire Brigade (attendance time) The median attendance time of the fire brigade to fires in dwellings was 4 min, with 95% of the incidents being attended within 8 min (Table 3).

1 0.9

Probability of exceedance.

0.8

No fire fighting by occupants

0.7

Fire fighting by occupants

0.6 0.5 0.4 0.3 0.2 0.1 0 1

10

100

Time between discovery and call to brigade (mins)

Fig. 4. The CCDF comparing the time from discovery of fire to call to fire brigade being made for cases where fire-fighting action was and was not taken by the occupants in residential dwellings.

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4.5. Time from ignition to arrival of Fire Brigade (fire age) In the case of dwelling fires the median time between ignition and the arrival of the fire brigade at the scene of the fire was 11 min (Table 3). There were also a number of incidents where there was a significant length of time between ignition and attendance of the brigade. These were primarily due to the long times between ignition and discovery examined previously. 4.6. The distribution of event times for fires in dwellings The frequency distribution of discovery times and call times were not readily approximated by one of the standard distributions (e.g. normal, log-normal, etc.) for which parameters could be estimated. As an alternative, the proportion of fires exceeding a given time for each distribution is therefore tabulated in Table 5. However, the distribution of Fire Brigade attendance times can be approximated by a normal distribution using the mean and standard deviation parameters specified (for time between call and arrival of the fire brigade) in Table 3. 4.7. Fire growth rate The necessary data was available to allow t2 fire growth rate parameters to be calculated (between ignition and arrival of the fire brigade) for 481 of the dwelling

Table 5 Proportion of fires in dwellings exceeding a specified time for selected time intervals Dwelling fires

Proportion of fires exceeding the specified time

Time (min)

Between ignition and discovery of firea

Between discovery of fire and call to the fire brigadeb

Between ignition and fire brigade arrivalc

0 1 2 3 4 5 10 15 20 30 60

0.81 0.68 0.58 0.52 0.48 0.39 0.25 0.19 0.14 0.09 0.04

0.86 0.56 0.25 0.14 0.09 0.05 0.01 (0.003) (0.002) (0.001) —

1.00 1.00 0.99 0.99 0.97 0.92 0.54 0.34 0.24 0.14 0.05

a

Based upon a sample of 584 dwelling fires where specific ignition and discovery times were given. Based upon a sample of 1651 dwelling fires where the discovery and call times were specified and the fire brigade took some form of action against the fire. c Based upon a sample of 525 fires in dwellings where specific ignition and fire brigade arrival times were given and the fire brigade took some form of action against the fire. b

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fires sampled (using only those incidents where specific estimates of the ignition and discovery times were given, the fire areas at discovery and brigade arrival were specified and the fire brigade took some form of action against the fire). Table 6 shows the growth rate parameter values of these fires classified in accordance with the ranges specified in Table 1. Whilst the majority of dwelling fires had growth rates that were either slow or very slow (smouldering), around 10% achieved medium growth rates and 3% developed at rates that were fast (or in one case ultra fast). The frequency distribution of fire-growth parameter values, a; can also be approximated using a log-normal distribution (i.e. such that ln(a) is normally distributed). The log-normal distribution parameters estimated from the dwelling fire-growth parameter data are shown in Table 7 along with the expected fire growth parameter value, EðaÞ; (found using Eq. (3)) and the location of the 95th percentile, a95 ; (found using Eq. (5)) for the distribution. Table 7(a) gives the estimated log-normal distribution parameters (ma ; sa ) for the dwelling fire growth parameter data sample treated as a whole. The expected fire growth parameter value, EðaÞ; calculated for this distribution is 0.006 kW/s2 (just below the threshold of classification for a medium growth rate fire) while the a95 value is 0.024 kW/s2. The estimated distribution parameters, EðaÞ and a95 values for selected ignition sources are shown in Table 7(b). Dwelling fires where the sources of ignition were white goods or tv’s and hifi’s had the lowest expected growth rates and a95 values, whilst fires started by heating appliances had the highest. The distributions obtained for naked flames and candles had the highest ma values while the deviation sa was highest for fires started by smoking materials and heating appliances. Table 7(c) presents the estimated distribution parameter values and EðaÞ values for some selected types of first material ignited in dwelling fires. Dwelling fires where the first material involved was a flammable liquid or vapour produced the highest expected growth rate (0.023 kW/s2), a95 value (0.085 kW/s2) and ma value. The deviation, sa ; was highest for the groups of fires where the materials first ignited were upholstered furniture or rubbish.

Table 6 Number and percentage of dwelling fires sampled in each fire growth parameter class Fire growth parameter classa

Frequency

Percent (%)

Very slow Slow Medium Fast Ultra fast All

142 276 49 13 1 481

30 57 10 3 o1 100

a

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Table 7 Log-normal parameters characterising the distribution of fire growth parameters in dwelling fires Fire growth parameter, a

Estimated log-normal distribution parametersb

Dwelling firesa

N

ma

sa

EðaÞ (kW/s2)

a95 (kW/s2)

Group by (a) All dwelling fires

481

7.00

1.98

0.006

0.024

(b) Selected source of ignition White goods TV, hifi, etc. Electrical supply and lighting Smoking materials Candles Other naked flames Cooking appliances Heating appliances

11 20 31 75 44 148 83 48

7.07 7.31 7.69 7.77 6.52 6.36 7.18 7.31

1.39 1.62 2.19 2.24 1.59 1.48 2.02 2.41

0.002 0.002 0.005 0.005 0.005 0.005 0.006 0.012

0.008 0.010 0.017 0.017 0.020 0.020 0.021 0.035

(c) Selected first material ignited Electrical insulation Curtains Clothing Bedclothes Cooking oil Paper and cardboard Rubbish and packaging Upholstered furniture Flammable vapour or liquid

59 18 25 52 45 54 23 43 30

7.56 6.23 6.46 6.93 7.03 6.66 7.16 7.79 5.59

1.86 1.08 1.37 1.84 1.90 1.76 2.18 2.59 1.90

0.003 0.004 0.004 0.005 0.005 0.006 0.008 0.012 0.023

0.011 0.012 0.015 0.020 0.020 0.023 0.028 0.029 0.085

(a) All dwelling fires. (b) For selected sources of ignition. (c) For selected types of materials first ignited. The parameters ma and sa are the mean and standard deviation of the natural logarithm of a: The expected fire growth parameter of the distribution, EðaÞ; calculated using Eq. (3). The value of the fire growth parameter at the 95th percentile of the distribution (Eq. (5)). a Based on a sample of 481 fires in dwellings where the value of a could be estimated, the fire brigade took some form of action and a specific ignition time was estimated. b N is the number of fires in the group sampled.

5. Results for other buildings 5.1. Fire size Just over a quarter (26%) of the fires in all other buildings sampled (n ¼ 441; where the final fire damage area was specified) had a fire damage area less than or equal to 1 m2, while the fire size was less than 10 m2 in three-quarters (75%) of the incidents investigated (see Table 8 and Fig. 3). However, there were also a number of extreme cases where the fire damage was much larger, extending over hundreds or even thousands of square metres. Assuming that fire sizes can be modelled using a log-normal distribution, the overall log-normal distribution parameters (m; s) estimated for fires investigated in

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Table 8 Percentage of fires in other buildings belonging to each fire damage size group by occupancy type Other buildings

Fire damage area a

Occupancy group

Less than 1 m2 (%)

1–10 m2 (%)

10–100 m2 (%)

100 m2 or more (%)

Number of fires

Care homes Factories Further education Hospitals Hotels Licensed premises Offices Public buildings Retail Schools Warehouses Allb

65 13 29 47 45 26 30 12 17 35 — 26

29 55 43 50 53 56 44 53 53 38 40 49

6 23 29 3 3 16 22 24 27 26 30 20

— 9 — — — 2 3 12 3 — 30 5

17 47 14 30 38 50 63 34 94 34 20 441

a b

Rows may not add to 100% due to rounding error. Total based on 441 fires investigated in other buildings where the fire damage area was specified.

all other buildings taken together are shown in Table 9(a), along with the expected fire size (18 m2) and x95 value (66 m2). As with dwelling fires, a comparison can be made between the distributions of fire damage area in other buildings, based upon whether first-aid fire-fighting action was taken by the occupant or not. The estimated log-normal parameters (m; s) and expected fire size and x95 values found for the two distributions are shown in Table 9(b). For the cases where the occupants took no fire fighting action, the expected fire size was 24 m2, but for the cases where some form of action was taken by the occupants the expected fire size was only 8 m2. A similar pattern was shown by the x95 fire size values, which was 86 m2 for the distribution of fires where no fire-fighting action was taken by the occupants, but just 30 m2 for the distribution of fires where it was. A two-sample t-test confirms that the m value is significantly lower for the cases where the occupant(s) took fire-fighting action against the fire (po0:001). A comparison can also be made between the fire size distributions found with and without sprinklers (Table 9(c)). There was no significant difference in the means (and hence the median fire size) found between the two distributions (a two-sample t-test on the two means produces a p ¼ 0:861). However, both the expected fire size and x95 values found were substantially lower for the distribution where a sprinkler system was present, suggesting that sprinkler acts to curtail the occurrence of larger fire sizes. As with dwellings, it is once again possible to discriminate between the fires in other buildings investigated on the basis of whether an Automatic Fire Detector (AFD) system was installed or not. The log-normal parameters characterising these

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Table 9 Log-normal parameters characterising the distribution of fire damage area in other building fires sampled Fire damage area other buildingsa

Estimated log-normal distribution parametersb N

m

s

EðxÞ (m2)

x95 (m2)

Group by (a) All other building fires

441

0.92

1.99

18

66

(b) Fire fighting action by occupant Yes No

130 311

0.41 1.13

1.81 2.02

8 m2 24 m2

30 86

(c) Sprinklers installed Yes No

20 421

1.00 0.92

1.53 2.01

9 m2 20 m2

33 68

(d) AFD system installed Yes No

177 264

0.26 1.37

1.85 1.95

7 26

27 97

(e) Selected sources of ignition Other sources Cooking appliances Other electrical appliances Heating appliances Smoking materials Naked flames Electrical supply and lighting

17 51 46 42 64 163 52

0.65 0.44 0.45 0.64 1.02 1.25 0.88

1.45 1.68 1.85 1.77 1.94 1.89 2.67

5 6 9 9 18 21 85

21 25 33 35 67 78 195

(a) All fires in other buildings. (b) By whether fire-fighting action was taken by the occupant. (c) By whether sprinklers were installed. (d) By whether an AFD system was installed. (e) For some selected sources of ignition. The parameters m and s are the mean and standard deviation of the natural logarithm of the fire size. The expected fire size of the distribution, EðxÞ; calculated using Eq. (3). The fire size at the 95th percentile of the distribution, calculated using Eq. (5). a Based on a sample of 441 fires in other buildings where the final fire damage area was specified. b N is the number of fires in the group sampled.

two types of distribution are shown in Table 9(d). Both the expected fire size and x95 damage values found for the distribution of fire sizes in other buildings where an AFD system was installed are less than a third of those found for the distribution where no AFD system was present. A two-sample t-test also confirms that the large difference between the means of the two distributions is statistically significant (po0:001). Table 9(e) gives the estimated log-normal parameters (m; s) together with the expected fire sizes and x95 values found for some selected ignition sources starting fires in other buildings. Based upon the expected fire size and x95 values, the ignition sources can be broadly divided into three groups. The distributions obtained for appliances (cooking, heating and other electrical) and other sources produced

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relatively small expected fire sizes of between 5 and 9 m2 (with x95 values of 35 m2 or less). On the other hand the distributions found for fires started by smoking materials and naked flames both had higher mean values, m; and consequently produced larger expected fire sizes (18 and 21 m2, respectively) and x95 values (67 and 78 m2). However, by far the largest expected fire size, at 85 m2, and x95 value (195 m2) was found for fires started by electrical supply and lighting equipment (as was also the case in dwellings). This was primarily as a consequence of a large standard deviation s; suggesting that this type of ignition source was responsible for a wide range of fire sizes—some small, others extremely large. A breakdown of fire size by occupancy group is also given in Table 8. It is evident that the largest fire sizes occurred in warehouses, with 30% (6 fires, n ¼ 20) of the fires investigated in this occupancy group damaging areas in excess of 100 m2. There were also a significant number of very large fires (greater than 100 m2) that occurred in factories and public buildings. In contrast the majority of the fires that were investigated in care homes, hospitals and hotels were less than 1 m2 with few exceeding 10 m2 in area. The log-normal distribution parameters (m; s) estimated for fires occurring in each type of occupancy are shown in Table 10 along with the expected fire size and 95th percentile (x95 ) values calculated using Eqs. (3) and (5). The expected fire size found for warehouses, at 170 m2, is significantly higher than that found for any of the other occupancies, as is the x95 value (586 m2) and the mean m: In contrast, the expected fire sizes, x95 and m values found for care homes, hospitals and hotels are all significantly lower than those found for the other occupancy groups.

Table 10 Log-normal parameters characterising the distribution of fire damage area for different occupancy groups Fire damage area other buildingsa

Occupancy group Care homes Hospitals Hotels Licensed premises Schools Retail Higher/further education Offices Factories Public Buildings Warehouse

Estimated log-normal distribution parametersb N

m

s

EðxÞ (m2)

x95 (m2)

17 30 38 50 34 94 14 63 47 34 20

0.64 0.08 0.29 0.78 0.69 1.17 0.56 0.83 1.68 1.80 2.87

1.44 1.28 1.52 1.70 1.89 1.84 2.24 2.14 1.91 1.92 2.13

1 2 2 9 12 18 22 23 34 38 170

6 8 9 36 45 66 70 78 124 142 586

The parameters m and s are the mean and standard deviation of the natural logarithm of the fire size. The expected fire size of the distribution, EðxÞ; calculated using Eq. (3). The fire size at the 95th percentile of the distribution, calculated using Eq. (5). a Based on a sample of 441 fires in other buildings where the final fire damage area was specified. b N is the number of fires in the group sampled.

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5.2. Time from ignition to discovery of fire (discovery time) To increase the number of usable incidents for other buildings, some cases in which there was a greater degree of uncertainty in the ignition time were included using the average time between the upper and lower bounds on the ignition time specified for incidents where the difference between the two bounds was fifteen minutes or less. Descriptive statistics for the time from ignition to discovery (together with the other time intervals examined) for fires in other buildings sampled are summarised in Table 11. The median time from ignition to discovery of fires in other buildings was 4 min (same as for dwellings) with nearly 80% of the fires being discovered in 10 min or less. However, there were also a small number of incidents in the tail of the distribution where the fire took more than 30 min to be discovered. Reasons for such long discovery times identified included smouldering cigarettes being left on a sofa or bed or the building being left unoccupied overnight. A breakdown of discovery time by occupancy group is shown in Table 12. A relatively high percentage of the fires in factories were discovered at ignition (35%) typically because they involved some form of explosion that would be detected immediately. Fires in retail premises also tended to be more frequently discovered at ignition time (28%) often because they involved an accident with cooking appliances that were being used by the occupier at the time of the fire. In contrast fires in office buildings had a higher incidence of discovery times in excess of 30 min (16%) than the other occupancies, largely as a result of cases where there was a smouldering fire in an office started by cigarettes or other smoking materials.

Table 11 Descriptive statistics for selected time intervals in the other building fires sampled Other buildings

Descriptive statistics

Times interval (minutes) a

Ignition and discovery Discovery and call to FBb Call and arrival of FBc Ignition and FB arrivald

N

Min

Max

Median

Mean

Std. dev.

t95

215 342 369 180

0 0 1 3

599 24 13 606

4 2 4 10

10.3 1.9 4.4 17.8

42.2 2.1 1.6 46.3

30 5 7 40

a Based upon a sample of 215 fires in other buildings where the ignition and discovery times were specified and the ignition time range estimated was less than or equal to 15 min. b Based upon a sample of 342 fires in other buildings where the discovery and call times were specified and the fire brigade took some form of action against the fire. c Based upon a sample of 369 fires in other buildings where the call and arrival times of the fire brigade were specified and the fire brigade took some form of action against the fire. d Based upon a sample of 180 fires in other buildings where the ignition and fire brigade arrival times were specified, the ignition time range estimated was less than or equal to 15 min and the fire brigade took some form of action against the fire.

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Table 12 A breakdown of discovery time by occupancy group for fires in other buildings Other buildings

Time from ignition to discovery of fire a

Occupancy group

Discovered at ignition (%)

Under 5 min (%)

5–30 min (%)

More than 30 min (%)

Number of fires

Care homes Factories Higher/further education Hospitals Hotels Licensed Premises Offices Public buildings Retail Schools Warehouses Allb

6 35 12

53 20 50

41 35 38

— 10 —

17 20 8

5 17 14 4 9 28 — — 14

62 56 36 44 18 26 40 43 39

33 28 45 36 64 41 55 57 41

— — 5 16 9 4 5 — 5

21 18 22 25 11 46 20 7 215

a

Rows may not add to 100% due to rounding error. Total based on 215 fires investigated in other buildings where the ignition and discovery times were specified and the ignition time range estimated was less than or equal to 15 min. b

Table 13 A breakdown of other building fires by source of ignition and time between ignition and discovery Other buildings

Time from ignition to discovery of fire a

Source of ignition

Discovered at ignition (%)

Under 5 min (%)

Cooking appliances Heating appliances Other appliances Smoking materials Naked flames Electrical distribution Other sources Unknown Allb

33 18 8 — 10 24

36 18 58 27 46 29

27 65 35 45 42 38

3 — — 27 2 10

33 17 26 22 89 21

25 — 14

25 — 39

50 100 41

— — 5

4 3 215

5–30 min (%)

More than 30 min (%)

Number of fires

a

Rows may not add to 100% due to rounding error. Total based on 215 fires investigated in other buildings where the ignition and discovery times were specified and the ignition time range estimated was less than or equal to 15 min. b

The influence of the ignition source upon the time to discovery of fires in other buildings is summarised in Table 13. While a third of the fires involving cooking appliances were discovered at ignition, only 3% of these fires took more than 30 min to be discovered. Conversely, none of the fires started by smoking materials were

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discovered at ignition, but 27% of these fires took more than 30 min to be discovered. 5.3. Time from discovery to call to Fire Brigade (call time) Table 11 also shows the descriptive statistics found for the time interval between the discovery of the fire and the call being made to alert the fire brigade in other building fires (note that only those incidents where the fire brigade attended and took some form of fire-fighting action against the fire were considered). The median time between discovering a fire in other types of building and calling the Fire Brigade was 2 min (the same as in dwellings). Almost half of the calls to the fire brigade were made in the first minutes, while 95% of calls were made in the first 5 min. However, there were also a small number of incidents where there was a significant delay, in excess of 5 min, between discovering a fire and calling the brigade. Reasons for a delay in calling the fire brigade identified were: 5.3.1. False alarms The occurrence of repeated false alarms in a building means that an alarm may not be treated seriously in the event of a real fire, delaying the call to alert the fire brigade and consequently hampering rescue and fire-fighting operations, with potentially fatal consequences. For example, in one incident investigated in a hotel, the smoke detection system was subject to frequent false alarms giving rise to a casual attitude amongst the staff. Thus, when a settee in one of the guestrooms was deliberately set on fire, the hotel’s AFD system detected the smoke, but the alarm was ignored and it took staff 10 min to realise the danger and finally call the fire brigade. During this time period some of the corridors in the hotel became heavily smoke logged making occupant egress extremely difficult. In another example, a deliberate fire was set in a cloakroom, situated on the first floor of a primary school. Upon discovery of the fire, the deputy headmaster actuated a break glass on the floor of origin, but then had to report to an office on the ground floor to confirm that it was not a false alarm, before the fire brigade were actually called out. Hence, the call to the brigade was not made until 7 min after the fire was first discovered, during which time the fire had begun to spread extremely rapidly causing extensive damage. 5.3.2. Small smouldering fire There were a number of incidents involving small smouldering fires where the occupants were not sure whether to call out the fire brigade or thought that the fire was extinguished, only to discover it was still smouldering, giving rise to prolonged intervals between discovery and call to the brigade. 5.3.3. Time to escape In some instances the occupant was impeded by the fire or incapacitated by the effects of smoke inhalation and took time to exit the building and raise the alarm.

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5.3.4. First aid fire fighting In several cases the call to the brigade was delayed while the occupants of the building took first aid fire fighting action against the fire. 5.3.5. Investigating the fire After discovering the initial fire cues (e.g. smelling smoke, hearing a strange noise or alarm) the occupant spent time investigating the cause, delaying the call to the fire brigade. Fig. 5 shows a comparison between the call time CCDF distributions obtained in other building fires on the basis of whether the occupant took first aid action against the fire or not. In contrast to the situation found in dwellings, there would appear to be little difference in the distribution of call times found between the two types of incident (i.e. there is no evidence that taking first aid fire-fighting action in other building fires generally delays the call being made to alert the brigade). A MannWhitney U test performed on these two groups supports this finding, implying that there is no statistically significant difference in the central location of the two distributions (p > 0:05). 5.4. Time from call to arrival of Fire Brigade (attendance time) The median attendance time of the fire brigade to fires in other buildings was 4 min, with 95% of the incidents being attended within 7 min (Table 11). 5.5. Time from ignition to arrival of the Fire Brigade (fire age) For fires in other buildings the median time between ignition and arrival of the fire brigade was 10 min, while 95% of the intervals between ignition and brigade arrival did not exceed 40 min (Table 11). 1

Probability of exceedance.

0.9 0.8

No fire fighting by occupants

0.7

Fire fighting by occupants

0.6 0.5 0.4 0.3 0.2 0.1 0 1

10

100

Time between discovery and call to brigade (mins)

Fig. 5. The CCDF comparing the call times for cases where fire-fighting action was and was not taken by the occupants in other buildings.

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5.6. The distribution of event times for fires in other buildings The frequency distribution of discovery times and call times were not readily approximated by one of the standard distributions (e.g. normal, log-normal, etc.) for which parameters could be estimated. As an alternative, the proportion of fires exceeding a given time for each distribution is therefore tabulated in Table 14. However, as with dwelling fires, the distribution of Fire Brigade attendance times can be approximated by a normal distribution using the mean and standard deviation parameters, specified in Table 11, for the time interval between the call and arrival of the fire brigade. 5.7. Fire growth rate The requisite data was available to allow t2 fire growth parameter values to be calculated (between ignition and arrival of the fire brigade) for 164 of the fires investigated in other buildings. Table 15 shows the growth parameters values of these fires classified in accordance with the ranges specified in Table 1, together with a breakdown by occupancy group. Whilst the majority (81%) of the fires in other buildings were either slow or very slow (smouldering), 10% developed at rates that could be classed as being fast or ultra fast. Such fast and ultra-fast growth fires occurred mainly in warehouses (3, n ¼ 6), factories (4, n ¼ 16) and retail Table 14 Proportion of fires in other buildings exceeding a specified time for selected time intervals Other buildings

Proportion of fires exceeding the specified time

Time (min)

Between ignition and discovery of firea

Between discovery of fire and call to the fire brigadeb

Between ignition and fire brigade arrivalc

0 1 2 3 4 5 10 15 20 30 60

0.86 0.74 0.60 0.53 0.48 0.38 0.21 0.12 0.08 0.04 0.02

0.79 0.52 0.23 0.11 0.07 0.03 0.01 (0.003) (0.003) — —

1.00 1.00 1.00 0.99 0.97 0.89 0.49 0.30 0.18 0.07 0.03

a Based upon a sample of 215 fires in other buildings where the ignition and discovery times were specified and the ignition time range estimated was less than or equal to 15 min. b Based upon a sample of 342 fires in other buildings where the discovery and call times were specified and the fire brigade took some form of action against the fire. c Based upon a sample of 180 fires in other buildings where the ignition and fire brigade arrival times were specified, the ignition time range estimated was less than or equal to 15 min and the fire brigade took some form of action against the fire.

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Table 15 Number of fires in other buildings belonging to each fire growth parameter class by occupancy group No of other building fires

Fire growth parameter class

Occupancy group

Very slow

Slow

Medium

Fast

Ultra fast

Total

Care homes Factories Higher/further education Hospitals Hotels Licensed premises Offices Public buildings Retail Schools Warehouse Other building fires (all) Other building fires (%)

5 4 1 7 5 4 6 2 4 6 — 44 27%

4 7 4 10 7 10 11 6 18 9 2 88 54%

— 1 — — — 2 2 1 9 — 1 16 10%

— 3 — — — — — 1 4 1 2 11 7%

— 1 — — — 1 — — 2 — 1 5 3%

9 16 5 17 12 17 19 10 37 16 6 164 100%

a Based on a sample of 164 fires in other buildings where the value of a could be estimated, the fire brigade took some form of action and the uncertainty in the time of ignition was 15 minutes or less.

premises (6, n ¼ 37). In contrast, only slow or very slow growth rate fires were recorded in care homes, hospitals, hotels and further education premises (although the latter suffers from a very limited sample size). As with dwellings, the frequency distributions of fire growth parameter values, a, were once again modelled using a log-normal distribution. The overall log-normal distribution parameters (ma ; sa ) estimated for fires investigated in all other buildings taken together are shown in Table 16(a), along with the expected fire growth parameter value (0.012 kW/s2) and a95 value (0.044 kW/s2) equivalent to a medium growth rate fire. Table 16(b) gives the estimated log-normal parameters (ma ; sa ) together with the expected fire growth parameter and 95th percentile (a95 ) values found for some selected types of ignition source starting fires in other buildings. Fires that were started by electrical supply and lighting equipment exhibited the highest expected growth rate (0.043 kW/s2) and a95 (0.134 kW/s2) with the distribution displaying both the greatest mean (ma ) and standard deviation (sa ) values. The expected fire growth rate and a95 values obtained for fires started by cooking appliances, at 0.029 and 0.096 kW/s2, respectively, were also relatively high. The estimated log-normal distribution parameters and expected fire growth and a95 values for selected types of first materials ignited in fires in other buildings are shown in Table 16(c). As was the case in dwellings, fires where the first material involved was a flammable liquid or vapour produced the highest expected growth rate (0.070 kW/s2), a95 value (0.231 kW/s2) and mean (ma ) value. The log-normal distribution parameters (ma ; sa ) estimated for fires occurring in each type of occupancy are shown in Table 17 along with the expected fire size and a95 : The expected fire growth parameter value found for warehouses, at 0.107 kW/s2,

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Table 16 Log-normal parameters characterising the distribution of fire growth parameters in the other building fires sampled Fire growth parameter, a other buildingsa

Estimated log-normal distribution parametersb N

ma

sa

EðaÞ (kW/s2)

a95 (kW/s2)

164

6.5

2.0

0.012

0.044

(b) Selected source of ignition Heating appliances Smoking materials Naked flames Other electrical appliances Cooking appliances Electrical supply and lighting

12 18 65 18 28 16

7.4 7.4 6.4 6.5 6.0 5.8

1.9 2.1 1.7 2.2 2.2 2.3

0.004 0.005 0.007 0.018 0.029 0.043

0.013 0.018 0.027 0.060 0.096 0.134

(c) Selected first material ignited Paper and cardboard Clothing and textiles Electrical insulation Furniture and furnishings Rubbish and packaging Flammable vapour or liquid

25 21 20 16 10 29

6.9 7.1 6.8 6.8 6.7 5.1

1.3 1.5 2.0 2.1 2.2 2.2

0.002 0.003 0.009 0.010 0.015 0.070

0.009 0.010 0.031 0.036 0.050 0.231

Group by (a) All other building fires

(a) All other building fires. (b) For selected sources of ignition. (c) For selected types of materials first ignited. The parameters ma and sa are the mean and standard deviation of the natural logarithm of a: The expected fire growth parameter of the distribution, EðxÞ; calculated using Eq. (3). The value of the fire growth parameter at the 95th percentile of the distribution (Eq. (5)). a Based on a sample of 164 fires in other buildings where the value of a could be estimated, the fire brigade took some form of action and the uncertainty in the time of ignition was 15 min or less. b N is the number of fires in the group sampled.

is significantly higher than that found for any of the other occupancies, as are the a95 mean ma : Conversely, the expected fire growth parameter, a95 and ma values found for care homes, hospitals and higher and further education are all lower than those found for the other occupancy groups.

6. Discussion 6.1. Large fire sizes There were a relatively small numbers of fires in dwellings that resulted in large fire damage areas, particularly in cases where the fire had been able to spread into a roof space or ceiling void. In other buildings the largest fire damage areas occurred in warehouses which by their very nature often present a large open space through which fire can spread with relative ease. From an examination of the incidents

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Table 17 Log-normal parameters characterising the distribution of fire growth parameters for each occupancy group in the other building fires sampled Fire growth parameter, a other buildingsa

Occupancy group Care homes Higher/further education Hospitals Hotels Offices Schools Public buildings Licensed premises Retail Factories Warehouse

Estimated log-normal distribution parametersb N

ma

sa

EðaÞ (kW/s2)

a95 (kW/s2)

9 5 17 12 19 16 10 17 37 16 6

7.7 7.2 7.1 7.7 7.1 7.3 6.2 6.6 5.4 5.9 4.0

1.1 0.8 1.3 2.1 1.8 2.0 1.9 2.2 1.9 2.2 1.9

0.001 0.001 0.002 0.004 0.004 0.005 0.012 0.016 0.027 0.030 0.107

0.003 0.003 0.007 0.014 0.016 0.019 0.045 0.053 0.101 0.100 0.405

The parameters ma and sa are the mean and standard deviation of the natural logarithm of a: The expected fire growth parameter of the distribution, EðaÞ; calculated using Eq. (3). The value of the fire growth parameter at the 95th percentile of the distribution (Eq. (5)). a Based on a sample of 164 fires in other buildings where the value of a could be estimated, the fire brigade took some form of action and the ignition time range estimated was 15 min or less. b N is the number of fires in the group sampled.

involved, reasons identified for the occurrence of large fire sizes in other types of building included: *

*

*

*

* *

* *

Fires involving sandwich panel cladding, promoting rapid flame spread and making it difficult to detect and fight the fire (warehouses); Fires in premises engaged in manufacturing and storage of fuel sources with a fast heat release rate such as plastic furniture or other plastic products (warehouses and factories); Explosions triggered by the ignition of flammable vapours released by solvents (factories); Involvement of highly combustible packaging material such as polystyrene (warehouse and retail); Ignition of PU foam settees (hotels and public buildings); Fire spread into a timber roof space where it was able to extend over a large area (licensed premises, offices and schools); Use of petrol or other accelerants in deliberate fires (licensed premises, retail); Inadequate fire separation between compartments allowing fire spread (all).

6.2. Fires started by electrical supply and lighting One perhaps surprising result of the analysis, was the large fire damage areas (indicated by the expected fire size and x95 values) found for distributions of fires

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started by electrical supply and lighting, relative to the other sources of ignition, particularly naked flames (other) where some form of deliberate action was usually involved. In dwellings, this would appear to be primarily due to electrical faults (e.g. wiring overheating, problems with power supplies, etc.) causing fires in roof spaces. Such roof voids often provide a large undivided space through which a fire can spread relatively easily over a significant area, while the timber roof construction typically used, offers an extensive fuel source to facilitate fire growth. It would also seem likely that fires started by electrical supply and lighting in a roof space (i.e. in a relatively remote part of the dwelling above the siting of many domestic smoke alarms) would generally take longer to be discovered and hence have the opportunity to cause a larger amount of damage. The situation is less clear-cut in the case of other buildings, since the high expected fire size and x95 values obtained for fires started by electrical supply and lighting are based on only a relatively small number of incidents that caused a large amount of damage in the tail of the distribution. Thus, it is difficult to determine if the result is genuine or just a consequence of the limited sample size and should be treated with a degree of caution. To illustrate this, Fig. 6 shows a comparison of the boxplots characterising the distribution of fire sizes found for fires in other buildings started by electrical supply and lighting and other naked flames. While the boxplot obtained for other naked flames shows a high concentration of incidents at moderately large fire sizes, the boxplot found for electrical supply and lighting reveals a much smaller number of cases extending over a wider range of fire sizes. Thus while the distribution found for

Fire damage area (square metres)

1200

1000

800

600

400

200

0 N=

52

163

Electrical supply

Other naked flames

Ignition source group

Fig. 6. Comparison of boxplots showing the distribution of fire damage area obtained for fires started by other naked flames with that found for fires started by electrical supply and lighting sources. The asterisks denote the outliers of each distribution located at large area values.

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other naked flames has the higher mean value, that obtained for electrical supply and lighting has a more extended tail. One possible explanation for the results found could be that electrical supply and lighting is a popular scapegoat for the cause of large fires started in other buildings where there has been extensive damage and the source of ignition is more difficult to determine. However, Hall [31] has observed that there is no evidence to support such a bias occurring in practice: Many knowledgeable individuals believe that the assessments of probable cause are subject to persistent biases. In fact, no credible evidence, consistent among a variety of fire departments, exists to support the notion that fire officers consistently label fires as suspicious, smoking-related, electrical in origin (all very popular suspicions in certain quarters), or anything else when they are really not sure of the cause. If the effect is a genuine one, then on the basis of an examination of the limited number of incidents sampled it is possible to speculate that the large fire sizes in the tail of the distribution may at least in part be attributed to fires started by electrical wiring or light fittings in services and concealed spaces (e.g. lift shaft, sandwich panels, etc.). Such fires, embedded within the structure of a building have the potential to spread over large areas relatively easily and would be more difficult to both detect and fight. 6.3. Fires involving flammable liquids and gases Much less surprising were the high fire growth rates attributable to the ignition of flammable liquids and gases found for both dwellings and other buildings. Such rapid growth rates reflect the rapid nature of the combustion process in flammable vapours. The results also support the observation made in DD240 [20] that Most fires that do not involve flammable liquids or gases initially grow relatively slowly. 6.4. First aid fire-fighting action by occupants In both dwellings and other buildings the expected fire size and x95 values were smaller for the distribution of fires where the occupants took some form of first-aid fire-fighting action against the fire. These results might suggest that occupant firefighting actions can reduce the level of fire damage incurred. However, it is possible that they simply reflect a tendency for building occupants to only attempt to tackle relatively small fires. It is also possible to examine the effect of the fire size at discovery of the fire on the success of first-aid fire-fighting action by occupants in extinguishing fires by considering whether the fire brigade subsequently took further action against the fire or not. Thus, in cases where no further action was taken by the brigade, the first-aid

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measures are deemed to have been a success while in cases where further action by the brigade did occur they are assumed to have failed. Table 18 shows the log-normal parameters characterising the distributions of fire area at discovery on the basis of success (or failure) of first-aid fire-fighting measures for both dwellings and other buildings. In dwellings, both the expected and x95 values were lower for fires where first-aid fire-fighting action was a success. A twosample t-test confirms that the m value was significantly lower for the distribution where the occupant’s fire-fighting action against the fire was successful (po0:01). This pattern was repeated for fires in other buildings. On the basis of the x95 results it can be observed that the majority of fires in dwellings that were successfully extinguished by the occupants were under 3.1 m2 in area when they were discovered. Similarly, in other buildings, the majority of fires that were successfully extinguished by the occupants were less than 2.3 m2 in area or when they were discovered. This would suggest that fires that are of greater size when they are discovered are unlikely to be successfully extinguished by occupant firefighting action alone. 6.5. The effect of a smoke detector on fire damage size in dwellings The results suggest that the installation of smoke detectors in dwellings only produced a relatively modest reduction in the amount of fire damage that could be typically expected. This can be attributed to the large number of detectors installed in dwellings that did not actuate when a fire occurred (approximately 50% in the sample examined here) due primarily to a battery being flat or having been removed altogether. To illustrate the consequences of this, Table 19 shows the log-normal parameters characterising the distribution of fire sizes for dwellings where a detector Table 18 Log-normal parameters characterising the distributions of fire area at time of discovery on the basis of success (or failure) of first-aid fire-fighting measures for both dwelling and other building fires Fire area at discovery

Estimated log-normal distribution parameters m

s

EðxÞ (m2)

x95 (m2)

Dwelling firesa Fire fighting action by occupant Success 92 Failure 240

0.69 0.29

1.11 1.26

0.9 1.7

3.1 6.0

Other building firesb Fire fighting action by occupant Success 32 Failure 92

0.84 0.09

1.02 1.42

0.7 2.5

2.3 9.4

N

a

Based on specified and b Based on specified and

a sample of 332 fires in the occupant took some a sample of 124 fires in the occupant took some

dwellings where the area of the fire at the time of discovery was form of fire fighting action against the fire. dwellings where the area of the fire at the time of discovery was form of fire fighting action against the fire.

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Table 19 Log-normal parameters characterising the distributions of fire damage area for fires in dwellings where a smoke detector was installed on the basis of whether the system was actuated or not Fire damage area dwelling fires, smoke detector installeda Smoke detector actuated Yes No a

Estimated log-normal distribution parameters N

m

s

EðxÞ (m2)

X95 (m2)

147 143

0.38 0.84

1.40 1.64

4 9

15 35

Based on a sample of 290 fires in dwellings where a smoke detector was installed.

was installed and the detector either did or did not actuate. The distribution representing the cases where the detector did actuate has expected fire size and x95 damage values that are substantially lower than (less than half) those found for the cases where the detector did not actuate. Of course the primary purpose of smoke detectors is life-saving, not property loss reduction. Thus, while any reduction in fire damage would be welcome, detectors should not be derogated on this basis, even if no significant reduction is apparent. 6.6. Comparisons with other studies Based upon the log-normal mean parameter values and expected fire sizes found for each distribution the occupancies can be divided into four broad fire damage (loss) classes: * *

* *

low: care homes, hospitals and hotels; medium: licensed premises, schools, retail, further education, offices and dwellings; high: factories and public buildings; very high: warehouses.

These results emphasise the very high fire loss values that occurred in warehouse (storage) premises. Wright and Archer [24] give figures (obtained from UK fire data) for the average fire damage areas that occurred in a number of different occupancies (note that these simple arithmetic average area values may be skewed to the right due to the nature of the distribution). These results are largely consistent with the fire damage classes identified above (e.g. factories and public buildings both had relatively high average damage areas whilst those found for care homes and hospitals were relatively low). The one real exception to this is the average damage area found for schools which has a relatively high value (similar to that found for public buildings) inconsistent with being classified in the medium fire loss class. Unfortunately, the limited amount of data available to characterise the fire growth parameter distributions for some of the occupancies, means that only a broad

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classification is really justified. Based upon the log-normal mean values found for each distribution the occupancies can be divided into two fire growth classes: * *

high: warehouses, factories, public buildings and retail; low or medium: care homes, further education and hospitals, hotels, offices, schools, licensed premises and dwellings.

These relative classifications are generally consistent with those made by Wright and Archer (based upon area growth rates derived using linear regression), where fires in public buildings and factories were also identified as likely to produce high growth rates, while those occurring in care homes and hospitals were comparatively low. However, there are also some differences. While fires in universities were identified as producing a high rate of damage by Wright and Archer, the results here suggests that the growth rate of fires in higher/further education establishments was (on average) relatively low. This discrepancy may be attributable to the very limited sample of incidents available for this occupancy type (only 5 fires) suggesting the values estimated to characterise the distribution in this case might be unreliable. The results presented here also suggest that retail should be classified as producing high growth rate fires, while the study made by Wright and Archer identifies this occupancy as being medium. At least some of this variance may be due to the difference in the way fire growth rate was characterised in terms of linear area growth rate by Wright and Archer, against t2 heat release rate here and the use here of a higher heat release rate (per unit area) for this occupancy type. 6.7. Why do some fires grow fast in dwellings? The circumstances surrounding the fires investigated in dwellings with the highest fire growth rates are summarised in Table 20. Using this information it is possible to identify a number of scenarios in which fires with high growth rates occurred. 6.7.1. Ignition of a flammable gas or liquid A flammable gas or liquid such as mains gas, paint vapour, paraffin or butane was ignited resulting in an explosion and/or rapid flame spread. 6.7.2. Cigarettes left burning on upholstered furniture The careless disposal of a cigarette leading to the ignition of a settee or sofa and rapid flame spread. 6.7.3. Children playing with matches Children left unsupervised playing with matches igniting curtains or sofa. 6.7.4. Candles left burning unattended and too close to other items Incidents where items were left in close proximity to a candle and which became involved.

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Table 20 Circumstances surrounding the fires with the highest fire growth parameter (a) values investigated in dwellings a (kW/s2)

tdb (min)

Circumstances of fire

0.130

0.8

0.074

—

0.069 0.053 0.042 0.039 0.039

1 0.6 2 — 1

0.035

1

0.034

0.9

0.031

—

0.031

1

0.030

4

0.030

—

0.029 0.023

2 —

0.023

2

0.022

0.8

0.022

1

0.021 0.021

2 1

Deliberate suicide attempt by an elderly person igniting mains gas with the piezo electric starter on a cooker. A workman using a blow lamp in the roof space of a house unintentionally ignited timber soffit boards. The resulting fire spread rapidly throughout the roof space. Unintentional disposal of a cigarette on a double divan mattress. Unintentional disposal of a cigarette on a Polyurethane foam sofa. Gas leak ignited by a naked flame after a failed attempt to fit a cooker. Butane gas leak from a domestic blow lamp in an utility room. Occupier unintentionally ignited excess paraffin whilst lighting a portable heater in the living room. Rubbish in bedroom unintentionally ignited whilst using paint-stripper. Fire subsequently spread into roof space. Candle left alight whilst occupier fell asleep in bed-sitting room, ignited the duvet cover. The Fire then spread rapidly to cushions and curtains. Fire started when child playing with matches in the living room ignited curtains and rapidly spread to involve room contents. The occupier was trying to thin gloss paint by warming it on a gas cooker hob. The flammable vapour released collected beneath the ceiling and triggered an explosion when the layer spread down to the level of the gas ring. Fire started by a child playing with matches ignited tissue paper in the living room and rapidly spread to settee. A radiant heater ignited an armchair that was placed too close. The fire spread rapidly to involve the room furniture. Cooking oil ignited in a chip pan left unattended in the kitchen. Spillage of paraffin from lighted oil heater in dining room was unintentionally ignited and rapidly involved the room contents. Clothes left to dry in the living room were placed too close to a radiant heater and were ignited. The fire then spread rapidly to involve the room furniture. The occupier was burning off paint with a blow lamp in the bedroom and unintentionally ignited the timber window frame. The resulting fire rapidly involved the room contents. A youth under the influence of alcohol ignited some paper whilst playing with a cigarette lighter in the bedroom. The fire then involved the carpet and spread rapidly. A child playing with a naked flame ignited some boxes under the staircase. A candle in the living room ignited a book that had been left too close and rapidly spread to involve the room contents.

The estimated fire size doubling time, tdb ; for each incident is also given (where available).

6.7.5. Fires started by paint-strippers and blow lamps Unintentional fires started whilst carrying out renovation work with a paint stripper or blow lamp resulting in rapid fire spread (particularly in roof spaces).

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6.7.6. Items placed too close to a radiant heater Cases where clothing or furniture was placed too close to a radiant heater, resulting in the item igniting and fire spreading rapidly. The table also shows the estimated fire size doubling time, tdb ; (i.e. the time required for the fire to double in area) for incidents where the necessary data was available, based upon the growth (in fire area) between discovery and arrival of the fire brigade. If the growth in fire area is assumed to be exponential in form, then the fire size doubling time can be estimated using Dt12 lnð2Þ tdb ¼ ; ð6Þ lnðA2 =A1 Þ where A1 is the area of the fire when it was first discovered (m2); A2 the area of the fire when the fire brigade arrived (m2); and Dt12 the time interval between discovery of the fire and arrival of the fire brigade (min). Wright and Archer [24] suggest that for uncontrolled fires, a fire with a doubling time of 4 min can be equated to fast fire growth, reaching flashover in 2 min, whilst a fire having a doubling time of 1 min can be equated to ultra-fast fire growth, reaching flashover in 1 min. Most of the doubling times obtained in Table 18 were 2 min or under confirming the rapid nature of the fire growth in these extreme cases. 6.8. Why do some fires grow fast in other buildings? The circumstances associated with the fires investigated in other buildings displaying the highest fire growth parameter values for different occupancy groups are summarised in Table 21. Using this information it is possible to identify several factors that are common to fires displaying high growth rates in other buildings: 6.8.1. Ignition of flammable liquid or gas vapour As was the case in dwelling fires, many of the fires that displayed high growth rates occurred in incidents where a flammable liquid or gas vapour such as petrol, cooking oil or thinner solvent used in an industrial process was ignited resulting in an explosion and/or rapid flame spread. 6.8.2. Intense line heat source Such a source can produce a large concentrated radiative heat flux able to rapidly ignite any close by fuel sources. 6.8.3. An extensive distributed fuel source Fuel items that enable a fire to be able to spread rapidly over a large area. Examples encountered include racks of clothing, piles of waste cardboard, carpet, and timber scaffold and in particular combustible wall and ceiling linings such as those made from fibreboard and timber. The relative orientation of flames spreading

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Table 21 Circumstances surrounding the fires with the highest fire growth parameter (a) values investigated in occupancy groups found in other buildings Occupancy group

a (kW/s2)

tdb (min)

Circumstances of fire

Warehouses

0.145

3

0.086

—

0.057

2

0.109

—

0.049

1

0.039

—

0.028

—

Licensed premises

0.159

1

Offices

0.023

5

Public buildings

0.034

2

0.023

1.3

0.309

1.5

0.108

0.7

Fire due to an electrical fault in the false ceiling of a large warehouse (furniture manufacturer). It subsequently involved the sandwich panel cladding materials (made from expanded polystyrene) on the roof and walls resulting in rapid fire spread and severe damage to the entire structure. Sparks from an angle grinder ignited the vapour produced by thinners used in the warehouse of a furniture manufacturer leading to rapid flame spread. A deliberate fire started by burning material being pushed through a window that spread rapidly via a combustible ceiling lining made of fibreboard. Luminous discharge ignited a flammable liquid in a paint and coatings manufacturing plant. The resulting explosion and rapid flame spread via building cladding caused extensive damage. A faulty lead to an appliance ignited the flammable vapour produced by thinner/stripper solvents used in the tanning and dressing of leather. Change of production process used in a paint mixing machine intended to increase output led to overheating and ignition of flammable vapours resulting in an explosion and rapid flame spread in the preparation room of a paint and coatings manufacturing plant. A fire spread rapidly along a combustible timber ceiling in an industrial building used for manufacturing furniture. Deliberate fire set in a Public house. The use of accelerant by the perpetrator resulted in rapid fire growth over a large area. Fire started by careless disposal of cigarette in refuse bin under desk. Subsequently ignited timber desk and spread via carpet to involve the whole office. Fire started by portable heater left next to curtains in a studio. Curtains and wall carpet acted as an extended source and promoted rapid fire spread. Fire in an art gallery lecture room undergoing refurbishment with the roof removed started by faulty extension lead. Once involved, the timber scaffold boards and plastic sheeting cover used promoted rapid flame spread causing extensive damage. Cooking oil in a commercial deep fat fryer ignited when it was left unattended resulting in rapid fire spread over the shop floor of a retail food shop. Fire in Bookmakers started by a defect in a wall mounted TV monitor, spreading to five other adjacent monitors all located in close proximity to the ceiling. This acted as line heat source igniting the combustible timber lined ceiling and promoting rapid flame spread under the ceiling.

Factories

Retail

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Table 21 (continued) Occupancy group

Schools

a (kW/s2)

tdb (min)

Circumstances of fire

0.085

0.8

0.078

—

0.057

—

0.027

2

0.066

0.5

Deliberate ignition of stack of waste carpet and cardboard left outside a shop acted as an extensive fuel source. The fire rapidly spread inside the building to involve both the shop and dwelling situated on the floor above. A defective thermostat in a deep fat fryer resulted in cooking oil overheating and igniting producing rapid flame spread in a restaurant. Fire occurred whilst the occupant was repairing a motorcycle leaking petrol in the under-ventilated showroom of a car accessory shop. Starting the bike up ignited some of the petrol vapour causing flames to flashback into the shop. Deliberate ignition of petrol introduced through window of wholesale clothing supplier. Fire spread rapidly via petrol accelerant and racks of clothing acting as a distributed fuel source. Deliberate fire started in children’s clothing left in a changing room in a primary school. De-lamination of multi-layered paint from the walls accelerated the fires progress along the corridor producing very rapid flame spread (paint ‘fireball’).

The estimated fire size doubling time, tdb ; for each incident is also given (where available).

over combustible wall and ceiling linings mean that the flames and hot gas products released are concentrated on the surface and that the surface ahead of the flame is pre-heated enhancing the flame spread rate. 6.8.4. Insulation material and sandwich panels A fire in a cavity is able to spread readily, undetected—flames and hot gases produced are also concentrated in confined space promoting preheating and rapid flame spread. 6.8.5. Burning thermoplastics These release heat at a much faster rate than conventional fuels increasing the likelihood of the fire involving other fuel items. 6.8.6. Paint de-lamination Consider a layer of paint that is exposed to the heat from a fire. While the paint is in contact with the wall substrate, which act as a heat sink, it is prevented from igniting. However, if multi-layer paint is exposed to heating, the surface coating is not directly bonded to the substrate and can readily become de-laminated, separating from the wall. Once it is no longer in contact with the wall substrate, the de-laminated paint fragments can heat up and burn rapidly, enhancing the burning rate and accelerating the de-lamination of further paint. Under the right

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circumstances this can produce a runaway feedback reaction and result in very rapid flame spread along a surface [32]. Table 21 also shows the estimated fire size doubling time, tdb ; for incidents where the necessary data was available. Most of the doubling times observed in these extreme cases were under 2 min. The fire doubling times were particularly small in the cases of a school fire where paint de-lamination caused extremely rapid flame spread along a corridor and in a bookmakers where a row of TV monitors caught fire directly under a combustible timber lined ceiling. 6.9. Limitations One important limitation stems from the limited sample of data available for analysis, particularly in the case of other buildings, where the data sampled must be divided over a number of different occupancies. The situation is particularly acute in the calculation of fire growth parameter distribution values for certain occupancies, where only a very small number of incidents were available. In such cases the results obtained must be treated with caution and may be best regarded as being indicative of the possible magnitude of fire growth parameter that may occur, rather than providing a definitive value. Hopefully, this situation will become better resolved in time, as more data from fire investigations becomes available. The analysis presented here looks only at consequences of fire, examining the frequency distributions of fire loss and fire growth rates given that a fire has occurred and been investigated by the fire brigade. However, an assessment of risk must also take into account the frequency of fire occurrence as well as the consequences of a fire. An earlier paper by Holborn et al. [33] has considered the frequency of occurrence of fires in different types of workplace occupancies (in this analysis the level of fire damage was treated simply in terms of the percentage of fires spreading beyond the first room). These results may be combined with the consequences quantified here (in terms of fire damage size) to provide an overall assessment of the relative risk to a particular occupancy type. Based upon the fire investigator attendance criteria it might also be argued that the sample of fires investigated represents the more ‘‘significant’’ (i.e. large damage, high growth rate) fires that occur and which are attended by London Fire Brigade. The distributions found here might therefore be regarded as being somewhat pessimistic in the sense that they do not include the effect of a large number of smaller fires, which the fire investigators (or even the fire brigade) did not attend. Morgan [21] has observed that any design based on an average fire growth curve implies a failure rate of one in two. He therefore suggests that to ensure a reasonable estimate of the likely fire growth, the fire growth parameter selected to represent a given design fire should be a specified number of standard deviations above the mean value of the (fire growth parameter) distribution appropriate to a particular class of occupancy. As an alternative the x95 value has also been supplied in this analysis to provide a measure of the location of the tail for both fire damage area and fire growth parameter distributions. However, it would appear that the x95 location for the log-normal distributions in many cases underestimate the actual location of

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the 95th percentile in the original data set, i.e. the tail of actual distribution extends to larger values than would be predicted by the fitted log-normal distribution. The current study was limited to London buildings and so the results may not be directly applicable to some structures in other countries (e.g. Scandinavia, North America) which have dissimilar construction practices. Indeed, it would be interesting to compare the results found here, with similar data obtained in other countries, to examine any similarities and differences in fire behaviour that might be apparent.

7. Conclusion Data obtained from fire investigations made by London Fire Brigade between 1996 and 2000 has been used to characterise the distributions of fire damage size, fire growth rates and the discovery and call times exhibited by fires, in a form suitable for use with probabilistic risk assessment, in both residential dwellings and other types of building. The analysis of the results has also highlighted several problem areas. In residential dwellings there were a number of cases where serious damage was caused to a property as a result of fires in roof spaces, especially with fires started by electrical supply and lighting and through the careless use of paint-strippers and blow lamps. Extensive damage occurred in a significant number of the warehouse (storage) fires investigated. Such structures often present a large undivided space for a fire to spread through. The involvement of sandwich panel cladding and fuel sources with a fast heat release rate (e.g. plastics) were also found to be important contributing factors in several cases. A number of reasons for the occurrence of high growth rates in the fires investigated were identified including the involvement of flammable vapours and liquids, thermoplastics, insulation materials in concealed spaces, paint de-lamination and other extensive distributed fuel sources. There were also a number of incidents where there was a significant delay between discovery of a fire and the call to brigade. Reasons found for such delays occurring in dwellings were that the fire was thought to be extinguished, only for it to be later discovered that it was still burning or that a neighbour discovered the fire but delayed calling the brigade, either because they were not sure that it was the correct thing to do or because they did not know how to make such a call. In other buildings, there were several cases where previous false alarms meant that a real fire was not treated seriously and was either initially ignored or had to be investigated and confirmed before a call to the brigade could be made.

Acknowledgements The authors would like to express their thanks to London Fire Brigade for their support in carrying out this research and to all the LFB fire investigators who

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collected data from the scene of fires into the Real Fire Library and made this analysis possible.

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