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Cost of Fire: Exploring Fire Incident Data For A Design Tool Methodology
IFE Kent Group CPD Training29th May 2012
Chris SalterBEng (Hons), AIFireE
Stephen EmmittDino Bouchlaghem
Prof. G RamachandranCivil & Building Engineering
Loughborough University
Background
IRMP Project Introduction
IRMP Project
§ Part of the “Evaluation of Prevention and Protection Activities for Commercial, Public and Heritage Buildings” project
§ Full project details and this work can be viewed at http://irmp.lboro.ac.uk/
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IRMP Project Aims
“How to identify, measure and mitigate the social and economic impact that fire and other emergencies can be expected to have on individuals, communities, commerce, industry, the environment and heritage”
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PhD Aims
§ Understand the Architecture, Engineering and Construction (AEC) industries views on Fire Engineering and cost
§ Analyse fire incident data
§ Construct a decision support tool for fire engineering cost decisions
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Published Work To Date
§ C Salter, N Bouchlaghem (2011), Fire Engineering in the UK : A UK Practitioners View, International Conference on Building Resilience, Sri Lanka.
§ C Salter, G Ramachandran, N Bouchlaghem (2011), A Cost Benefit Tool for Fire Protection Engineers : An Analysis, 2nd IRMP Conference, Glasgow University.
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Introduction
§ 451 fire deaths, 12,200 injuries in 2008§ Department for Communities & Local Government (2010), 'Fire Statistics, United
Kingdom 2008’, Department for Communities and Local Government.
§ Fires cost the UK economy £8.3 billion in 2008§ Communities and Local Government. (2011), The Economic Cost of Fire: Estimates for
2008, DCLG Publications.
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Fire Fatalities in the UK 1998-2008
Taken from Fire Statistics 2008, Department of Communities and Local Government
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Cost of Fire Claims
Taken from Association of British Insurers (2009), 'Tackling Fire: A Call For Action’.
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Cost Reductions in Fire
“Fundamentally, cost reduction is the only value we have to make our engineering better.”Torero, 2012
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Optimal Costs
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Taken from The Economics of Fire Protection, Ramachandran (1998)
Previous Work
Work undertaken on costs of fires and fire statistics use
Proposed Decision Support Tool
§ Decision support tool aimed at new builds, not existing or heritage structures
§ Tool could potentially be used in retro fitting but costs would be different
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UK Fire Statistics
Sources of Data
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FDR 1 Data
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FPA Large Loss Database
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Data Analysis Assumptions and Notes
§ Only 2005 FDR 1 data used
§ Restricted data, potentially volatile results due to one year of data
§ FDR 1 Data entry - Many fields missing
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Alarm Probability
§ Calculated from the FDR 1 results
§ Only 38.4% of records in 2005 had AFD present
§ From the filtered FDR 1 data, probability of alarm activation and raising the alarm is 74.1%
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0
5
10
15
20
25
30
35
40
10 12 14 16 18 20 22
Freq
uenc
y of
Fire
s (%
)
Size of Fire (Grouped by m2)
Alarm ActivatedAlarm Not Activated
Does Activation Affect Damage?
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Statistical Tests
§ Mann Whitney Statistical Test§ Data is not normally distributed
§ Proves Alarm Activation does affect final damage
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Probability of Extinction Systems Operating
§ Only 2.8% of fires had an extinction system (2005)
§ Sprinklers most popular extinction system (59.1% of systems)
§ Focus on sprinklers - other groups not large enough for statistical analysis
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Probability of Sprinklers Activating
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Frequency Percent
Activated
Activated - Extinguished Fire
ActivatedActivated - Controlled Fire
Failed
Activated - Failed to Control Fire
Failed
Failed to Activate
82 14.2
115 19.9
24 4.2
357 61.8
Probability of Sprinklers Activating
§ FDR 1 records show 34.08% activation overall
§ Previous studies state sprinklers are 95.6% effective (Rutstein and Cooke, 1983, Vaidogas and Šakėnaitė, 2011)
§ However this figure is when sprinklers are activated, not overall.
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Probability of Sprinklers Activating
§ Only 43% of fires are large enough to activate fires (Rutstein and Cooke, 1983)
§ Ramachandran states that a fire has to be 3m² before a sprinkler activates.
§ However, records show activations over 3m² are 67.8%
§ 16.8% of activations of sprinklers are under 3m²
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Sprinkler Activation
§ FDR 1 form does not differentiate between sprinkler system types
§ Potentially could explain the smaller than 3m² activations
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Regression Analysis
§ Take the factors that affect the fire size and then create a predictive model
§ Allows each variable to quantitatively show how the variable affects the data
§ Basis behind previous work conducted by Lin et al on Taiwanese residential buildings
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Multiple Regression
§ Would allow a predictive output of damage
§ However, data required to be continuous - FDR 1 damage data is non continuous
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Logistic Regression
§ Logistic regressions allows regression with binary values
§ Would give output as probability of an event§ In this case, fire exceeding 200m²
§ Preliminary chi squared tests showed that largest effect was DANGSUBS and IGNTDISC
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Logistic Regression Results
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Observed PredictedPredicted Percentage Correct
Under 200m²
Over 200m²
Under 200m² 32,720 0 100.0
Over 200m² 1,568 0 0.00
OverallOverall 95.4
Calculating Costs
Fire Damage Costs
Previous Work - Costs
§ Work undertaken by§ Ramachandran (1982 - 2012)§ Beck (1987)§ Wright (1998)§ Ashe (2006)§ Lin (2009)§ Fraser-Mitchell (2010)
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Cost Data
§ 4 sources identified for cost data1. Rateable Values2. Average from FPA Database3. BCIS Tool4. Xactimate Software
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Rateable Values
§ Measure of a properties rental value
§ Collected by Valuation Office Agency
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Pros Cons
Split into different counties Underestimate total costs
Freely available and already calculated as £/m²
FPA Database
§ Taken from loss adjustors estimates
§ Only on incidents where a fatality happened or cost was estimated as over £100,000
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Pros Cons
Once calculated, only needs access to FPA data periodically to update costs
Based on estimates
BCIS Tool
§ Database of costs of a new build - Collected by RICS
§ Data submitted from construction companies
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Pros Cons
Already in £/m² Not free data
Very detailed data
Xactimate Software
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Xactimate Software
§ Cost estimation software from Xactware
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Pros Cons
Incredibly detailed cost breakdowns
Aimed at individual buildings, not across a building type
Updated quarterly
Costs
§ Rateable values and Xactimate discarded
§ Tool to use FPA data§ FPA and BCIS data comparison first
§ Examine the two datasets to see how they compare
§ Concerns that lost adjustors over estimate costs
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FPA/BCIS Comparison
§ FPA - 18 categories§ 18 occupancies
§ BCIS - 8 main categories§ 414 occupancies
§ Merge BCIS occupancies to match FPA occupancies
§ FPA data costs calculated into £/m²
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Costs - FPA
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0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Tota
l Los
s Es
timat
e (£
)
Area Damaged m2
Industrial ProcessingNon Residential -Misc
Food And DrinkRetail
WarehousesEntertainment and Culture
Permenant AgriculturalEducationReligious
Sport
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
9,000,000
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Tota
l Los
s Es
timat
e (£
)
Area Damaged m2
Non ResidentialEducation
Entertainment and CultureFood and Drink
Industrial ProcessingMedical
Permanent AgriculturalPublic Utilities
RetailSport
TransportWarehouses
Costs - BCIS
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Average Costs
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Occupancy FPA Cost £/m² BCIS Cost £/m²
Industrial Processing 4,775.50 743.79
Non Residential Misc 1,405.16 1,452.21
Food and Drink 1,980.61 1,788.56
Retail 1,941.96 1,017.19
Warehouses 1,662.49 736.13
Entertainment and Culture 1,273.74 1,649.69
Permanent Agriculture 785.82 875.20
Education 1,548.64 1,564.17
Religious 1,742.79 1,696.80
Sport 1,237.01 1,397.56
Average Costs
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Occupancy FPA Cost £/m² BCIS Cost £/m²
Industrial Processing 4,775.50 743.79
Non Residential Misc 1,405.16 1,452.21
Food and Drink 1,980.61 1,788.56
Retail 1,941.96 1,017.19
Warehouses 1,662.49 736.13
Entertainment and Culture 1,273.74 1,649.69
Permanent Agriculture 785.82 875.20
Education 1,548.64 1,564.17
Religious 1,742.79 1,696.80
Sport 1,237.01 1,397.56
Difference here seems to be
significantly different
Statistical Tests
§ t-test performed on the datasets to compare
§ Datasets are the same (to 95% certainty)
§ Either dataset can be used in the tool
§ Differences attributed to FPA data including loss of equipment and stored items
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FSEC Toolkit
§ FSEC Toolkit gives a cost figure for the time for FRS to arrive
§ Cost based on FPA publicly released figures
§ FSEC calculates average and then doubles them (based on work from 1977) - gives significantly higher figures than FPA costs
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Cost Model
§ Costs taken from either FPA or BCIS can form the model
§ Only focuses on property cost - not consequential
§ Fire growth follows αt² model
§ Costs should therefore follow the same model, not linear
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Discussions, Conclusions and Further Work
Conclusion - Statistics
§ Fire data collection adequate for current CLG usage, needs changing for predictive output
§ Statistical analysis of dataset show that predictive model is inaccurate with current data
§ FDR 1 - IRS dataset focus on reactive statistics, not predictive
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Conclusion - Statistics
§ If fire statistics are to be used predictively, alternative data needs to be collected
§ IRS data needs to be less binary
§ Regression analysis of fire incident data would give predictive output, if the data is correct
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Conclusion - Statistics
§ Log linear analysis potentially could be used to analyse FDR 1 data
§ FPA data may prove to be more beneficial once it has accumulated more records
§ Unclear how FPA database takes into account inflation
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Conclusions - Statistics
§ Care needs to be taken with sprinkler statistics§ Using only FPA data can skew sprinkler
reliability/activations§ Better consideration to difference between
activations and successes§ Better reporting needed for successful
activations
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Conclusions - Costs
§ Methods of calculating costs considered and compared
§ BCIS or FPA data can be used in future cost calculations
§ Methodology now in place if fire statistics improve
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