Manual 8 – Termite attack - FWPA · 2018-08-02 · Manual 8 – Termite attack PROJECT NUMBER:...

Manual 8 – Termite attack

PROJECT NUMBER: PN07.1052

August 2007

MARKET ACCESS

This report can also be viewed on the FWPA website

www.fwpa.com.auFWPA Level 4, 10-16 Queen Street,

Melbourne VIC 3000, AustraliaT +61 (0)3 9927 3200 F +61 (0)3 9927 3288

E [email protected] W www.fwpa.com.au

Manual No. 8: Termite Attack 1

USP2007/045

MANUAL NO. 8

Termite Attack

R.H. Leicester, C-H. Wang, and M.N. Nguyen

April 2008

This report has been prepared for Forest & Wood Products Australia (FWPA).

Please address all enquiries to:

Urban Systems Program

CSIRO Sustainable Ecosystems

P.O. Box 56, Highett, Victoria 3190


2

Acknowledgments

This Manual is one of a series of Manuals that have been produced as part of a project titled

‗Design for Durability‘. The authors are deeply indebted to the Forest and Wood Products

Australia for their funding and collaboration in this project over the past 10 years. The authors

would especially like to thank Colin MacKenzie (Timber Queensland) for the major role that

he has played in managing and guiding this project to completion. Thanks are also due to Dr.

Laurie Cookson (CSIRO), Dr. John French (Ecospan Consulting Services), Mr. Doug Howick (

AEPMA), Mr. Jim Creffield (CSIRO), Dr. Don Ewart (Granitgard), Mr. Nicholas Cooper

(Systems Pest Management), and Dr. Berhan Ahmed (University of Melbourne) for contributing

extensive data and expertise to the development of the models described in this Manual.

Finally our thanks go to Greg Foliente, Craig Seath, Sandra Roberts and numerous other

CSIRO personnel for their assistance and contribution to this project

© 2008 CSIRO

To the extent permitted by law, all rights are reserved and no part of this publication covered by

copyright may be reproduced or copied in any form without acknowledgment of this reference source.


3

Contents

EXECUTIVE SUMMARY ........................................................................................................ 6

1 INTRODUCTION .............................................................................................................. 7

1.1 Project Background ........................................................................................................ 7

2 TERMITE TALLY ............................................................................................................ 8

2.1 Zonation ......................................................................................................................... 8 2.2 Analysis of Termite Tally Based on Temperature Zonation .......................................... 9

2.2.1 Effect of Age of House ................................................................................................ 9

2.2.2 Effect of Temperature and Rainfall on the Termite Hazard..................................... 12 2.2.3 Effects of Frame and Floor Types on Termite Incidence ......................................... 15

2.3 Analysis of Termite Tally Data Based on Agro-Ecological Zonation ......................... 17

2.3.1 Zonation Procedure .................................................................................................. 17

2.3.2 Effect of Age of House .............................................................................................. 18 2.3.3 Effect of Frame and Floor Types on Termite Incidence .......................................... 23

2.4 Analysis of Termite Tally Data Based on Housing Clusters ....................................... 23 2.4.1 Zonation Procedure .................................................................................................. 23

2.5 Concluding comments .................................................................................................. 27

3 EXPERT OPINION MODEL .......................................................................................... 28 3.1 The Survey Questionnaire ............................................................................................ 28 3.2 Choice of Parameters ................................................................................................... 31 3.3 The Base Model ........................................................................................................... 38

4 THE PROBABILISTIC MODEL .................................................................................... 41

4.1 Introduction .................................................................................................................. 41

4.2 The Basic Probability Model ........................................................................................ 41 4.2.1 The probability Distributions ................................................................................... 41 4.2.2 The True Risk in the Past ......................................................................................... 42 4.2.3 True Risk in the Future ............................................................................................. 43 4.2.4 Apparent Risk in the Past ......................................................................................... 43 4.2.5 The Apparent Risk in the Future .............................................................................. 44 4.2.6 The Distribution Parameters .................................................................................... 44


4

4.3 The Practical Model ..................................................................................................... 46

5 COMPUTATION MODEL .............................................................................................. 49

5.1 Concept ......................................................................................................................... 49 5.2 Calibration of the Model .............................................................................................. 49 5.3 The Coefficient of Variation ........................................................................................ 52

6 RISK ASSESSMENT EQUATIONS .............................................................................. 54 6.1 Hazard Parameters ........................................................................................................ 54

6.2 Supplementary Hazard Parameters .............................................................................. 56 6.3 Expanded definitions of hazard parameters ................................................................. 57

6.3.1 Procedure for assessing the hazard h4 due to the quantity of wood occurring in a

garden and under a house .................................................................................................... 57 6.3.2 Definition of ground contact .................................................................................... 58

6.3.3 Hazard level h6 related to type of construction material ......................................... 58 6.3.4 Hazard h4 related to exposure of timber .................................................................. 59

6.4 Computational Procedure ............................................................................................. 59 6.4.1 Computing the mean time to attack mean(t) ............................................................ 59 6.4.2 Computed Risk and Hazard Score ........................................................................... 60

6.5 Computing Risk ............................................................................................................ 65

6.6 Acceptable Risk ............................................................................................................ 66 6.7 Risk Management ......................................................................................................... 67

6.7.1 Cost Assumptions ..................................................................................................... 67 6.7.2 Comparative Costs of Termite Protection Strategies ............................................... 67

6.8 Some Computed Examples .......................................................................................... 67

6.8.1 Applications to Risk Assessments ............................................................................. 67 6.8.2 Applications to Risk Management ............................................................................ 68

6.8.3 Comment ................................................................................................................... 69

7 APPLICATION FOR DESIGN GUIDE .......................................................................... 70

7.1 Procedure to compute risk ............................................................................................ 70 7.2 Hazard score components ............................................................................................. 70 7.3 The Hazard Score Total ................................................................................................ 72

7.3.1 Comment on Hazard Zone A (Tasmania) ................................................................. 72 7.4 Parameters for the risk equation ................................................................................... 73

7.5 Acceptable Risk ............................................................................................................ 73

8 APPLICATION FOR TIMBERLIFE .............................................................................. 75 8.1 Procedure to compute risk ............................................................................................ 75

8.2 Hazard score components ............................................................................................. 75 8.3 The Hazard Score Total ................................................................................................ 77

8.3.1 Comment on Hazard Zone A (Tasmania) ................................................................. 77 8.4 Parameters for Evaluating the Risk Equation .............................................................. 78

8.5 Risk Management procedure ........................................................................................ 78 8.5.1 Cost Components ...................................................................................................... 78 8.5.2 Effective Cost of Termite Protection Strategies ....................................................... 79

REFERENCES ......................................................................................................................... 80


5


6

Executive Summary

The purpose herein is to describe the development of a model to predict the

probability of attack of housing by termites. Such a model may be used to estimate

risk as part of an asset management strategy. There are three components to the model.

The first component is a survey by school students initiated by Dr John French and

analysed by Dr Laurie Cookson in 1999. This strategy provided statistical data on

some 5000 houses and will be referred to as the ―CSIRO Termite Tally‖ (Cookson and

Trajstman, 2002). Processed data from this tally is discussed in Section 2. The data

shows a strong effect of age of a house on the probability of attack. It also shows an

effect of the mean annual temperature on the probability of attack.

The second component is the development of a model of termite behaviour based on a

survey of the expert opinion of a limited number of six experts. This ―Expert Opinion‖

model uses a large number of parameters as input to provide a quantitative estimate of

the mean and variability of observed times of termite attack. This model is described

in Section 3.

The third important component of this work was the development of a probabilistic

model, described in Section 4. The form of the probability model is based on the data

obtained in the Termite Tally. It is completely defined by a single parameter, i.e. the

mean time to a termite attack. In this model an important distinction is made between

the observed or apparent attack rate and the true attack rate. In the application of the

model, it is assumed that a house occupant will be aware of only the most recent

history of termite attack on his house. The historical memory of the occupant in this

study is taken to be to be 20 years. This historical memory needs to be taken into

account when using field data on termite attack, such as the data from the CSIRO

Termite Tally mentioned above.

In Section 5, the data from both the Expert Opinion model and the probabilistic model

are combined to give a termite attack model that is suitable for practical use. This

model is calibrated with the data from the CSIRO Termite Tally.

In Section 6 the termite attack model is used to develop a simple hazard score system

that can be used to compute risk. With this capability, various risk management

strategies can be formulated and implemented quite simply. Some examples of these

are given. For example, Section 6.6 provides the conditions required to obtain a risk

that would be considered an ‗acceptable risk‘ in Australia; Section 6.7 gives a method

for computing the cost of implementing a termite management strategy, including the

costs that would be incurred if attack were to occur; Section 6.8.2 gives examples of

the costs associated with a variety of risk management strategies.


7

1 INTRODUCTION

Equation Section (Next)

1.1 Project Background

The purpose herein is to report on progress in the development of a model to predict the

probability of attack of housing by termites. Such a model may be used to estimate risk as part

of an asset management strategy, i.e. to anticipate the risks and costs associated with termite

attack and various mitigation programs. There are three components to the model.

The first is a survey by school students initiated by Dr John French and analysed by Dr

Laurie Cookson in 1999. This strategy provided statistical data on some 5000 houses and will

be referred to as the ―CSIRO Termite Tally‖ (Cookson and Trajstman, 2002). Processed data

from this tally is discussed in Section 2.

The second study of value is the model of termite behaviour based on an opinion survey

of a limited number of experts. It will be called the ―Expert Opinion model‖. This model

provides a quantitative estimate of the mean and variability of observed times of termite

attack.

The third important component of this work was the development of a probabilistic

model, described in Section 4.

The data from both the Termite Tally and the Expert Opinion model are then used to

calibrate the reliability model. In this model an important distinction is made between the

observed or apparent attack rate and the true attack rate. It is assumed that a house occupant

will be aware of only the most recent history of termite attack on his house. The historical

memory of the occupant in this study is assumed to be about 20 years.


8

2 TERMITE TALLY


2.1 Zonation

The raw data from the Termite Tally has been analysed to provide information in terms of

probabilities. In order to do this it is necessary to first group houses in terms of a specified set

of parameters; then for each group, the probability of a termite incidence can be approximated

by noting the proportion of that group that has been attacked in the past. These probabilities

can then be related to other parameters that are averaged for the group.

In this report, the following three types of groupings have been used:

temperature zones

agro-ecological zones

housing clusters.

The agro-ecological zones are based on a previous study by Cookson (1999). The

housing clusters used correspond to locations where the Termite Tally shows more than 78

houses within a circle of 100 km diameter.

The data within the temperature and agro-ecological zones can be further subdivided

according to the age of the homes.

It is important for the reader to understand the notation used. The most important of

these is the notation used for probability, which may also be interpreted as risk. This notation

is as follows:

P(location, event, reliability type, t) = probability that an event at a given location has

occurred before time t. In this report the reliability type of the

probability figure will be assumed to have two possible values; one

will be a perfect value denoted as an exact value; the other will be an

imperfect estimate based on the observations of the building

occupant. The time t denotes the time in years after the construction

of the target house.

The subscripts indicating locations are as follows

house = target house for which the risk of termite attack is to be assessed,


9

garden = garden surrounding the target house,

suburb = suburb within which the target house is located.

The subscripts indicating events are as follows:

attack = probability that termites have attacked a house

nest = probability that a nest with a mature colony has been established

within the specified location

termite = probability that termites have occurred within the specified location

The subscripts used to define the type of probability figure are as follows

true = the true or exact probability that an event has occurred

obsv = the estimated probability that an event has occurred, based on

observations of by the occupant of a house.

2.2 Analysis of Termite Tally Based on Temperature Zonation

The continent is divided into three zones as follows:

Zone 1: Tmean < 18C

Zone 2: 18C Tmean < 25C

Zone 3: Tmean 25C

where Tmean denotes the mean annual temperature. A hazard map based on temperatures is

shown in Figure 2.1. The incidence of termites in Tasmania is taken to be zero.

Figure 2.1. Termite hazard map based on temperature zones.

2.2.1 Effect of Age of House

Figures 2.2, 2.3 and 2.4 show the effect of age of house on the possibility of observation of

termites both in the house and in the garden for the three primary temperature zones. Each


10

plotted data point is based on a data taken from the Termite Tally; the average sample size is

165 with a minimum value of 51.

There is obviously a very strong effect of age of house on the incidence of termite

attack on a house. In fact, it was found that in the data of the Termite Tally, the probability of

termite attack on a house is more strongly correlated with the age of the house than with any

other parameter. It is of interest to note that the fitted lines for Zones 1 and 2 are roughly

parallel to each other.

0 20 40 60 80 100 120

House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

P(h

ouse

, att

ack, obsv, t)

zone 1 (T

mean < 18 o C) zone 2 (T

mean = 18 - 25C zone 3 (T

mean > 25 o C)

Inside Termite Incidence (by Temperature Zones)

R 2 1 = 0.918

R 2 2 = 0.855

zone 1

zone 2

zone 3

Figure 2.2. Effect of house age on the apparent incidence of termite attack

(temperature zonation).


11

0 20 40 60 80 100 120

House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7P

(ga

rde

n,

term

ite

, o

bsv,

t)

Outside Termite Incidence (by Temperature Zones)

zone 3

zone 2

zone 1

zone 1 (Tmean < 18oC)

zone 2 (Tmean = 18 - 25oC)

zone 3 (Tmean > 25oC)

Figure 2.3. Effect of house age on the apparent incidence of termites in the garden


0 20 40 60 80 100 120

House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Termite Incidence (by Temperature Zones)

house house garden garden

zone 1

zone 1

zone 3

zone 3

P(h

ouse

, att

ack, obsv, t)

P(g

ard

en, te

rmite,

obsv, t)

zone 2 zone 2

Figure 2.4. Effect of house age on the apparent incidence of termites


P(h

ou

se

, a

tta

ck,

ob

sv,

t) P

(ga

rde

n,

term

ite

, o

bsv,

t)


12

2.2.2 Effect of Temperature and Rainfall on the Termite Hazard

In the development of a hazard maps, it is of interest to examine whether temperature and

rainfall have a significant relationship to the incidence of termites to be found in gardens. To

do this the data of the Termite Tally was broken up into temperature-rainfall clusters as

shown in Table 2.1, the cluster boundaries being chosen so that the sample size within each

cluster is greater than 90. The data within each cluster was then averaged and plotted as

shown in Figures 2.5 & 2.6.

Figure 2.5 shows that there is a modest correlation between mean annual temperature

and probability of finding termites in the garden. Figure 2.6 shows that the addition of rainfall

consideration does not improve the accuracy of prediction; this is because, as illustrated in

Figure 2.7, the termite data has been chosen in locations for which there is a reasonable

correlation between rainfall and temperature. In Figure 2.8, data points related to rainfall have

been plotted, and it is seen that in fact there is very little relationship, if any, between rainfall

and the probability of finding termites in a garden.

Table 2.1. Temperature-rainfall divisions and sample size in each division

N=142

0

14

15

16

17

18

19

20

21

23

30 N=91

N=159

N=355

N=94 N=288

N=156 N=156 N=253 N=217

N=290 N=782 N=413

N=316

N=504

N=178

N=188

500 0 1000 1500 2000 2500 3000

Rainfall range (mm)

Tem

pera

ture

range

(C

)


13

12 16 20 24 28

Temperature (C)

0.1

0.2

0.3

0.4

0.5

0.6

0.7 P

(gard

en

, te

rmite

, o

bsv, t)

Termite Incidence by Temperature-Rain

R2 = 0.505

Figure2.5. Effect of temperature on the apparent incidence of termites in the garden

(temperature-rainfall zonation using all data).

0.1 0.2 0.3 0.4 0.5 0.6 0.7 Predicted probability of apparent termite occurrences

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Measu

red p

robab

ility o

f ap

pare

nt

term

ite o

ccurr

ence


2 R2 = 0.514

Figure 2.6. Use of temperature and rainfall data to predict the apparent incidence of

termites in the garden (temperature-rainfall zonation using all data).


14

12 14 16 18 20 22 24 26 28 Mean annual temperature (C)

400

800

1200

1600

2000 M

ean

an

nua

l ra

infa

ll (m

m)


R2 = 0.609

Figure 2.7. Relationship between temperature and rainfall (temperature-rainfall zonation

using all data).

400 600 800 1000 1200

Mean annual rainfall (mm)

P(g

ard

en,

term

ite,

obsv,

t )


0.15

0.25

0.35

0.45

T = 18 - 20 C T = 16 - 18 C regression

Figure 2.8. Effect of rainfall on the apparent incidence of termites in the garden

(temperature-rainfall zonation using all data).


15

2.2.3 Effects of Frame and Floor Types on Termite Incidence

It is noted that while the incidence of internal attack is roughly similar within each of the

three temperature zones, the data in Figure 2.4 shows that the external hazard (as indicated by

the probability of finding termites in gardens) varies considerably with the temperature zone.

One reason for this may be that the type of construction, degree of inspection and extent of

barrier protection varies from zone to zone, the greater protective measures being taken in the

higher hazard zones.

In the following, the effects of construction frame types and floor types on inside

termite incidence are examined in each temperature zone. Three frame types and three floor

types are considered:

Frames: timber steel, and masonry;

Floors: timber, concrete, and combined timber and concrete.

Table 2.2 and Table 2.3 show the percentages of each frame type and floor type used in

each temperature zone. There is no obvious trend in the type of construction used within the 3

zones, except perhaps that for Zone 3, which is essentially Darwin, there is relatively less

timber construction than within Zones 1 and 2.

Table 2.2. Percentage of frame types in the temperature zones

Zone 1 Zone 2 Zone 3

timber 72 75 30

steel 4 6 31

Masonry 24 19 38

Table 2.3 Percentage of floor types in the temperature zones

Zone 1 Zone 2 Zone 3

timber 55 48 29

timber + concrete 9 6 2

concrete 36 46 69

The mean house ages for each of the temperature zone-construction type clusters are

given in Tables 2.4 and 2.5 for the case of frames and floors respectively. The incidence of

building termite attack is divided by these average house ages to provide an apparent average

rate of attack for each cluster, and results of this are shown in Figures 2.9 and 2.10; as may be

expected, the annual frequency of attack increases with the hazard zone classification.


16

Table 2.4. Mean house ages (years) of various frame types in the three

temperature zones

Zone Number Steel Masonry Timber

Zone 1 11.9 38.3 27.7

Zone 2 10.8 19.8 30.6

Zone 3 12.7 14.6 27.8

Table 2.5. Mean house ages (years) of various floor types in the three

temperature zones

Zone Number Timber Concrete Timber + Concrete

Zone 1 37.5 15.6 40.9

Zone 2 38.2 14.4 37.8

Zone 3 24.6 16 18

1 2 3 Temperature zones

0.000

0.005

0.010

0.015

Estim

ate

d a

vera

ged

ann

ual p

rob

ab

ility

of

term

ite a

ttack

steel masonry timber

Internal Termite Incidence for Various Frame Types

Figure 2.9. Effect of zone on the apparent termite for various frame types



17

1 2 3 Temperature zones

0.000

0.005

0.010

0.015

Estim

ate

d a

vera

ged a

nnu

al pro

ba

bili

ty o

f te

rmite a

ttack

timber concrete timber + concrete

Internal Termite Incidence for Various Floor Types

Figure 2.10. Effect of zone on the apparent termite for various floor types


2.3 Analysis of Termite Tally Data Based on Agro-Ecological Zonation

2.3.1 Zonation Procedure

The agro-ecological zonation of termite hazard was developed by Cookson (1999) and is

based on agro-ecological regions of Australia as defined by the Commonwealth of Australia

(1991) and illustrated in Figure 2.11. These regions are then broken down into sub-regions as

shown in Table 2.6 and accordingly numbered. After dropping sub-regions 1 and 17 where

there are virtually no termite incidences found, all other sub-regions are grouped into 4 zones

as follows:

Zone 1 (low hazard): 14;

Zone 2 (medium hazard): 10, 11, 15, 18;

Zone 3 (high hazard): 5-8, 13, 19-21; and

Zone 4 (very high hazard): 2-4, 9, 12, 22.

A termite hazard map, based on this zonation is shown in Figure 2.12.


18

Table 2.6 Termite Incidence in Agro-Ecological Regions

Agro-

ecological

region

Agro-ecological

sub-regions

Sample

number

Mean age

(years)

Outside

incidence of

termites

(%)

Inside

incidence of

termites

(%)

1 (part) 1, Tasmania 98 37.8 1.0 0.0

1 (part) 17, Melbourne,

west of 145oE

202 40.5 6.9 8.9

1 (part) 14, Melbourne,

east of 145oE

591 29.5 11.5 11.3

1 (part) 18, Wollongong,

south of 34.16oS

126 32.1 26.4 24.6

1 (part) 13, Sydney 603 39.8 33.5 22.1

1 (part) 19, Newcastle,

north of 33.33oS

115 18.8 27.8 13.9

1 (part) 12, Perth 421 26.8 49.2 14.0

2 (part) 21, NSW portion 574 20.6 23.9 15.5

2 (part) 2, Brisbane 394 26.9 44.7 23.6

2 (part) 22, Bundaberg,

north of 26.5oS

162 21.8 23.5 13.6

3 3, Cairns +

Rockhampton

114 26.1 42.1 28.1

4 4, Townsville +

Weipa

62 22.6 45.2 12.9

5 5, Toowoomba 260 33.1 26.1 14.6

6 6, Bathurst 241 32.6 31.5 17.0

7 (part) 7, Dubbo +

Bendigo

348 33.7 31.6 17.2

7 (part) 20, Adelaide +

SA portions

241 35.8 36.1 20.7

7 (part) 15, WA portion 49 30.8 32.7 16.3

8 8, Mount Isa +

semi-arid

51 36.5 23.5 19.6

9 9, Darwin 85 14.4 67.0 17.6

10 10, Canberra

+Bega

363 26.9 20.9 11.6

11 11, Arid interior 22 28.0 27.2 18.2

2.3.2 Effect of Age of House

Figures 2.13, 2.14 and 2.15 show the effect of the age of house on the probability that termite

incidences have been observed either within the house or within the garden. The average

sample size used for each data point is 147, with a minimum value of 29. It is seen that again

there is a very strong relationship between probability of termite incidence and age of house

for each zone. There appears to be little difference between Zones 2 and 3 and these are

combined further analyses.

Figures 2.15 and 2.16 show again the probabilities of observation of termite incidences

in the houses and gardens, respectively, following the mergence of Zones 2 and 3.


19

Figure 2.11. Agro-ecological regions of Australia.

Port Hedland

Mount Gambier

Albany

Darwin

Hobart

Broome

Alice Springs

Geraldton

Perth

Adelaide

Cairns

Townsville

Rockhampton

Brisbane

Sydney

Newcastle

Canberra

Bega

Albury

Mildura

Dubbo

Narrabri

Mount Isa

Kalgoorlie

Melbourne

Charleville

1 Wet temperate coast

2 Wet sub-tropical coast

8 Semi-arid tropical and subtropical plainlands

3 Wet tropical coast and tableland

9 North-western wet / dry tropics

6 Sub-tropical highlands

5 Sub-tropical slopes

7 Temperate semi-arid slopes and plains

4 North-east wet / dry tropics

10 Temperate highlands

11 Arid interior


20

Figure 2.12. Termite hazard map based on agro-ecological regions.

0 20 40 60 80 100 120 House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

P(h

ou

se

, att

ack,

on

sv,

t)

zone 1 zone 2 zone 3 zone 4

zone 1

zone 2

zone 3 zone 4

Inside Termite Incidence (by Agro-ecological Zones)

Figure 2.13. Effect of house age on the apparent incidence of termite attack

(agro-ecological zonation).

Port Hedland

Mount Gambier

Albany

Darwin

Hobart

Broome

Alice Springs

Geraldton

Perth

Adelaide

Cairns

Townsville

Rockhampton

Brisbane

Sydney

Newcastle

Canberra

Bega

Albury

Mildura

Dubbo

Narrabri

Mount Isa

Kalgoorlie

Melbourne

Charleville

Zone 4

Zone 3

Negligible

Zone 2

Zone 1


21

0 20 40 60 80 100 120 House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

P(g

ard

en

, te

rmite

, o

bsv,

t)

zone 1 zone 2 zone 3 zone 4

zone 1

zone 2

zone 3

zone 4

Outside Termite Incidence (by Agro-ecological Zones)



0 20 40 60 80 100 120 House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

P(h

ou

se

, att

ack,

ob

sv,

t)

zone 1 zones 2 & 3 zone 4

Inside Termite Incidence (by Agro-ecological Zones)

zone 1

zones 2 & 3

zone 4 R 2 1 = 0.873

R 2 2&3 = 0.836

R 2 4 = 0.931

Figure 2.15. Effect of house age on the apparent incidence of termite attack (agro-

ecological zonation, zones 2 and 3 merged).


22

0 20 40 60 80 100 120 House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6 P

(gard

en

, te

rmite

, o

bsv, t)

zone 1 zones 2 & 3 zone 4

Outside Termite Incidence (by Agro-ecological Zones)

zone 1

zones 2 & 3

zone 4

R 2 1 = 0.673

R 2 2&3 = 0.462

R 2 4 = 0.598


(agro-ecological zonation, zones 2 and 3 merged).

0 20 40 60 80 100 120 House age (years)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

house garden

Termite Incidence (by Agro-ecological Zones)

zone 1

zones 2 & 3 zone

4

zone 1

zones 2 & 3

zone 4

Figure 2.17. Effect of house age on the apparent incidence of termites


P(h

ou

se

, a

tta

ck,

ob

sv,

t) P

(ga

rde

n,

term

ite

, o

bsv,

t)

P(h

ou

se

, a

tta

ck,

ob

sv,

t) P

(ga

rde

n,

term

ite

, o

bsv,

t)


23

2.3.3 Effect of Frame and Floor Types on Termite Incidence

As for the case of temperature based zonation, Figure 2.17 shows that while the hazard as

indicated by the termite incidence in gardens varies markedly from one zone to another, the

internal attacks on houses are remarkably similar in the various zones. Again it is worth

investigating the possibility that this is due to the implementation of better building,

inspection and barrier systems in the higher hazard zones.

Tables 2.7 and 2.8 are a summary of the statistics of building systems obtained from the

Termite Tally. Again it is noted that the highest risk zone contains a smaller proportion of

timber construction.

Table 2.7 Percentage of frame types in the agro-ecological zones

Zone 1 Zone 2 Zone 3 Zone 4

timber 88 76 74 58

steel 1 7 5 7

Masonry 11 16 21 35

Table 2.8. Percentage of floor types in the agro-ecological zones

Zone 1 Zone 2 Zone 3 Zone 4

timber 72 57 56 34

timber + concrete 4 9 9 8

concrete 24 34 35 58

2.4 Analysis of Termite Tally Data Based on Housing Clusters

2.4.1 Zonation Procedure

In this procedure, the data was examined to find clusters of houses. A cluster was deemed to

occur when at least 78 houses were found in a circle of 100 km diameter. The locations of the

clusters found are shown in Figure 2.19. Some relevant characteristics of the houses in these

clusters are given in Table 2.9.


24

Table 2.9. Termite incidences and climatic data of the housing clusters

Location Longitude

(deg)

Latitude

(deg)

Sample

size.

Incidence

of termites

indoors

Incidence

of termites

in the

garden

Mean

age

(years)

Mean

annual

temp

(C)

Mean

annual

rain-

fall

(mm)

Darwin 130.832 -12.461 79 0.177 0.671 14.12 27.2 1761.8

Sydney 151.221 -33.87 589 0.219 0.334 40.119 17.8 1270.6

Newcastle 151.538 -33.209 109 0.119 0.248 18.959 18.1 1096.8

Armidale 151.873 -30.441 90 0.078 0.222 39.061 16.7 839.4

Taree 152.687 -31.84 307 0.137 0.202 16.82 18 942

Wollongong 150.878 -34.411 91 0.341 0.297 45.505 19.1 1365.7

Canberra 148.816 -34.918 146 0.096 0.171 24.814 13.5 711

Mudgee 149.146 -32.25 99 0.101 0.232 34.929 16.9 637.1

Melbourne 144.948 -37.812 653 0.123 0.112 33.444 15.1 852.6

Bendigo 143.901 -36.434 78 0.141 0.385 31.115 16.2 524.3

Brisbane 153.022 -27.468 311 0.235 0.434 28.406 20 1228.8

Adelaide 138.599 -34.927 174 0.195 0.356 35.039 16.2 465.9

Perth 115.862 -31.95 343 0.146 0.499 27.693 17.8 803.1

110 120 130 140 150

-40

-35

-30

-25

-20

-15

-10

Locations of Clusters

Figure 2.18. Locations of housing clusters.

lati

tud

e (d

eg.)

longitude (deg.)


25

As with Appendix A, the purpose here is to examine whether temperature and rainfall

have a significant correlation with the incidence of termites as part of a procedure to develop

a hazard map.

Figure 2.19 shows that there is a modest correlation between temperature and the

incidence of termites in the garden. Figure 2.20 shows that there is some improvement if the

effect of rainfall is taken into consideration. Figure 2.21 shows that there is a good correlation

between rainfall and temperature; this is probably the reason why there is no great

improvement to be obtained by adding rainfall to temperature as a prediction parameter.

Figure 2.22 shows that there is very little relationship between rainfall and termite incidence,

even if separated into specific temperature zones.

12 16 20 24 28 Mean annual temperature ()

0.1

0.3

0.5

0.7

Termite Incidence by Clusters

R 2 = 0.626

Figure 2.19. Effect of temperature on the apparent incidence of termites in the garden

(housing cluster data).

P(g

ard

en, te

rmite,

obsv, t)


26

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Predicted probability of apparent termite occurrences

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 M

easure

d p

roba

bili

ty o

f a

ppare

nt te

rmite o

ccurr

ences

R 2 = 0.7171


Figure 2.20. Use of temperature and rainfall data to predict the apparent incidence

of termites in the garden (housing cluster data).

12 14 16 18 20 22 24 26 28 Mean annual temperature (C)

300

600

900

1200

1500

1800

Mea

n a

nn

ua

l ra

infa

ll (m

m)


R 2 = 0.681

Figure 2.21. Relationship between temperature and rainfall (housing cluster data).


27

400 600 800 1000 1200 1400 Mean annual rainfall (mm)

0.20

0.25

0.30

0.35

0.40

0.45

0.50 P

(gard

en, te

rmite,

obsv,

t)

T = 18 - 20 C T = 16 - 18 C regression


Figure 2.22. Effect of rainfall on the apparent incidence of termites in the garden (housing

cluster data).

2.5 Concluding comments

A feature of some interest is that there are very little differences between the incidence of

termite attack on houses between the various zones, whereas there is a considerable difference

in the termite hazard from one zone to the next, as indicated by the occurrence of termites

within gardens. This could be due to the fact that in high hazard areas more effective building

practices, pest control and inspection procedures are used. An attempt was made to examine

the data of the Termite Tally for information related to building practices, but the results were

inconclusive. The only definite statistic obtained was that timber is a dominant construction

material in all zones except the highest hazard zone.

One minor disadvantage associated with the use of agro-ecological zones is that it

involves soil types as a regional parameter and it may be desirable to use soil properties as a

local rather than a regional parameter as is currently done with the termite attack model.

The Termite Tally does not provide data that is in a form that is suitable for assessing

the effect of soil type on termite hazard. However, by evaluating the average soil properties in

each of the agro-ecological zones it may be feasible obtain a rough indication of their effect.


28

3 EXPERT OPINION MODEL


3.1 The Survey Questionnaire

The following is an example of the survey questionnaire that was used.

ESTIMATES OF TERMITE ACTIVITY INTENTION OF THIS QUESTIONNAIRE

The purpose of this questionnaire, is to obtain information to predict the time taken by termites to attack and

destroy a timber element within a building. To do this, a limited number of experts on termites will be asked

to give their opinions on the following matters:

(a) A list of parameters that will affect the rate of attack (if you wish, suggest alternative parameters to

the ones proposed here).

(b) Grouping of these parameters into high and low risk effects.

(c) An estimate of the time taken by termites to undertake certain events.

Note 1. The format chosen for the questions is to enable the response to be used to calibrate a

mathematical model of termite activity.

Note 2. It is expected that for a given situation, there will be a wide range of field experiences and

expectations.

Note 3. Examples of answers are given in order to assist understanding the format of the questionnaire.

However, these answers were provided by a non-expert and should not influence your response.

Note 4. To ensure that your response complies with the intent of the questionnaire, please ensure that a

member of the research team involved (i.e. Bob Leicester, Greg Foliente, Craig Seath, Colin

McKenzie) is present to assist you when you undertake this task.

Note 5. We will leave a spare copy of this questionnaire with you. You may use this to

provide alternative estimates (at a later date) if you wish.

CONTACT INFORMATION

Dr Bob Leicester ([email protected])

Dr Greg Foliente ([email protected])

CSIRO Building, Construction and Engineering

PO Box 56, Highett Victoria 3190

Phone: (03) 9252 6000 Fax: (03) 9252 6246


29

INSTRUCTIONS FOR ANSWERING QUESTIONNAIRE In the following, you are invited to estimate the time required for termites to undertake various

events related to the target house shown in Figures 3.1 and 3.2. We ask you to estimate two

times as follows:

Ttypical this would be a typical length of time that you would expect for the

event to take place

Tunlucky this would be a quick time; you would have to be unlucky to

experience this rate of attack.

(In the statistical interpretation of the data, it will be assumed that Ttypical denotes the

average time and Tunlucky denotes the quickest 10-percentile time)

The five events for which you will be required to provide time estimates are as follows,

establishment of nest

travel to a building

penetration or bypass of barriers

destruction of building timber

You will be asked to consider the effect of various parameters on the rate of termite activity.

Each parameter will be grouped into 3 categories designated as follows:

L = low termite activity, M = medium termite activity, H = high termite activity.

Examine these parameters, rate them, and if necessary make your own suggestions for

modifications to the parameters provided.

You will also be required to classify the importance (in your opinion) of the parameters as they

affect the time estimates. This importance rating will be on a scale of 0 to 10, with ―0‖ denoting

no importance and ―10‖ denoting extreme importance. An important parameter is one for which

you would expect to see a considerable difference in termite activity depending on whether the

parameter is a high risk one or low risk one. An unimportant parameter is one which (in your

opinion) will not have any effect on termite activity, regardless of whether it is a high risk one

or a low risk one.

DEFINITIONS

AWPC Australian Wood Preservation Committee see ―Protocols for Assessment of

Wood Preservatives‖, (H. Greaves, Chairman) Melbourne 1997, 24 pages.

AS 3660 Standards Australia ―Protection of Buildings from Subterranean Termites‖,

Sydney, 1995, 53 pages.

AS 1604 TimberPreservative-treatedSawn and Round. Standards Australia, 1993,

Sydney, 36 pages

Your name is……

Do you wish your name to be kept confidential?


30

Stage 4Destruction

Stage 3

(entry)

Stage 2

Stage 1Nest

Figure 3.1. Illustration of termite travel stages.

House50 m

50 m

50 m

50 mTermite

free land

Built

up

Suburbs

Figure 3.2. Hypothetical scenario

for time zero zeroestimateshouse

and land at time zero.


31

Estimates of these four event times were obtained via a limited survey of expert

opinion. Although the original plan had been to obtain estimates from at least 20 experts, it

was found difficult to find experts in termite behaviour who were comfortable with

quantifying their opinions in the form requested. Accordingly it was decided to enlist the

assistance of a limited number of Australia‘s leading experts. Not all of these experts were

willing to provide opinions on all questions asked so in the end all opinions obtained were

melded into a single composite response, for which the authors of this report take full

responsibility. The experts who took part in this exercise were Dr John French, Dr Berhan

Ahmed, Mr Jim Creffield and Mr Doug Howick all of whom either are or were at one time

research scientists within CSIRO, Dr Don Ewart (Development manager, Granitgard) and Mr

Nicholas Cooper (Manager, Systems Pest Management).

3.2 Choice of Parameters

The parameters chosen by the experts are listed in the Table 3.1 below.

Table 3.1. List of event times and associated parameters

Event time Influencing parameter t1 the time taken for the establishment of a mature colony within a distance of 50 m from the target house

P1: geographical location P2: age of surrounding suburbs P3: number of potential nest sites

t2 the time taken for the termite foraging galleries to progress to a house 20 m away from the nest site

P4: geographical location P5: soil condition P6: food source

t3 the time taken for termites to penetrate or bypass a chemical or mechanical barrier, if any

P7: geographical location P8: period between inspections P9: maintenance parameter

t4 the time taken (after penetrating the barrier) to reach and cause failure of a timber member

P10: geographical location P11: ground-contact building element P12: period between inspections P13: type of material attacked P14: timber environment

Descriptions of these parameters are given in the following. The hazard zones shown in

Figure 3.3 was based on a combination of consideration of the two hazard maps discussed in

Section 2.

For each parameter Pj, there is an associated parameter factor kj. This factor kj is given

the value of +1, 0 or 1 depending on whether the parameter has been chosen to correspond to

low, medium or high hazard situations respectively. The parameters chosen are described in

Tables 3.2 – 3.11.


32

Figure 3.3. Termite hazard zonation.

Table 3.2. Parameter k1, k4, k7, k10

k1, k4, k7, k10 Building location*

+1

0

1

Zone 1

Zone 2

Zone 3

*see Figure 3.1

Table 3.3 Parameter k2

k2

Age of suburb (yrs)

+1

0

1

<6

630

>30


33


k3

Number of potential nesting sites

+1

0

1

<2

25

>5

EXAMPLES OF POTENTIAL NEST SITES

The following refers to potential nest sites for harbouring mature

colonies which are not more than 50 m from the building.

Tree (diameter larger than 300 mm)

Tree stump or untreated pole (diameter larger than 200 mm)

Untreated landscape timber (e.g. sleepers, retaining walls)

(length >1.0 m, height >0.5 m)

Woodheap

(height >0.5 m, ground contact area 0.5 x 0.5 m, length of

periods that bottom layer

woodheap is untouched >1 year)

Compost heap

Wood ‘stepping stones’

Subfloor storage (height >0.5 m, ground contact area >0.5 x 0.5 m, length of

period which it is

untouched >1 year).

Solid infill under a verandah

Any part of a building with water leaking under it.


34

Table 3.5. Parameter k5

K5 Soil type

+1

< fissured clay, sandstone

fertile soil

coarse sand

0 sound clay; loam; silt

1 moist soil mixed with composted material

poor lateritic soils


K6

Typical distance between substantial

food sources (m)

+1

0

1

<20

520

>5

A typical example of a substantial food source would be a piece of

timber equal to or greater than 200 x 100 x 50 mm in-ground

contact

Table 3.7 Parameter k8, k12

k8, k12

Period between inspections (yr)

+1

0

1

<2

25

>5


35


K9

Period between chemical retreatments

+1

0

1

Tm

2Tm

>8Tm

Tm = period recommended by chemical producer


36


k11

Ground contact elements

+1

House supported by exposed concrete piers

or steel stumps more than 2 m high

0

Intact concrete slab on ground;

House on stumps less than 600 mm high

with ant caps and made of concrete or

treated timber* or heartwood of durable

species**

1

Floor connected to ground by stair cases of

untreated softwood, untreated non-durable

timber**, untreated sapwood of durable

timber;

Attached patio with solid infill

Concrete slab-on-ground with large cracks

and/or unprotected pipe penetrations

Floors connected to ground by elements

containing hidden cavities (e.g. masonry

construction, deeply grooved elements,

members in imperfect contact).

Brick veneer house

Leakage of moisture to ground

Timber floor less than 600 mm off the

ground

treated timber refers to timber treated according to AS 1604

and/or complying with AWPC recommendations

** for a listing of durable species, see timber of durability

class I and II in AS 1604


37


k13

Type of material attacked

+1

Treated timber*

Untreated heartwood of durability Class 1

Hardwoods

0

Untreated heartwood of durability Class 2

hardwoods

Untreated heartwood of all softwood species

1

Untreated hardwoods of durability

Classes 3 and 4

Untreated sapwood of all species

Composite wood boards

treated timber refers to timber treated in accordance AS 1604 and/or

complying with AWPC recommendations

** durable species refers to species of durability class I and II

according to AS 1604.


k14

Exposure of timber

+1

High human activity

High up a building

Humidity <30%

0

Exposed to rain

1

No disturbance and dark (e.g. wall stud,

double leaf masonry wall, roof member.)

Exposed to sources of moisture so as to be

periodically wet

Abandoned houses

Humidity >90%


38

3.3 The Base Model

The base model has been derived on the basis of expert opinion. It applies to a house

surrounded by 50 m of termite-free land as shown in Figs. 3.1 and 3.2. The distance of 50 m

was chosen because this is about the limit of the foraging distance of most termite species.

The model used endeavours to estimate four sequential event times t1 – t4 as defined in Table

3.12 and illustrated in Figure 3.4. Relevant data on these four event times were obtained via a

limited survey of expert opinion.

Stage 4Destruction

Stage 3 Stage 2

Stage 1Nest

Figure 3.4 Illustration of termite progress.

In the survey, a set of parameters affecting each event time was obtained from experts.

The set chosen is listed as P1, P2, …, P14, as tabulated in Table 3.12. For each parameter, the

experts were asked to list the importance of the parameter with regard to its influence on the

relevant event time; this importance was rated on a scale of 110, with 10 being the most

important; examples of the importance parameters chosen are also given in Table 3.12.

Table 3.12 List of event times and associated parameters

Event time Influencing parameter Importance factor

t1 the time taken for the establishment of a mature colony within a distance of 50 m from the target house

P1: geographical location P2: age of surrounding suburbs P3: number of potential nest sites

8 5 9

t2 the time taken for the termite foraging galleries to progress to a house 20 m away from the nest site

P4: geographical location P5: soil condition P6: food source

8 6 7

t3 the time taken for termites to penetrate or bypass a chemical or mechanical barrier, if any

P7: geographical location P8: period between inspections P9: maintenance parameter

4 10 7

t4 the time taken (after penetrating the barrier) to reach and cause failure of a timber member

P10: geographical location P11: ground-contact building element P12: period between inspections P13: type of material attacked P14: timber environment

8 5 9 7 7


39

For each of the times t1 – t4, the experts were asked to assess both ―typical‖ and

―unlucky‖ values. They were asked to do this when all parameters were set at their high risk

settings, resulting in times tH, and low risk settings, resulting in times tL. A set of values,

based on the responses received, is shown in Table 3.13, where the times t3 have the

following notation

t3B : time that a physical barrier is crossed or breached

t3C : time that a repellent chemical barrier is crossed or breached

t3D : time that a toxic chemical barrier is crossed or breached

t3E : t3 = 0 when there is no barrier

Table3.13 Estimates by experts of time parameters

TIME

Estimate of time t (yrs)

j For high risk parameters tH For low risk parameters tL

tH(typical) tH(unlucky) tL(typical) tL(unlucky)

t1 8 3 30 10 0.0295

t2 4 0.5 6 1.5 0.00964

t3B 4 1 70 15 0.0710

t3C 8 2 100 40 0.0556

t3D 6 1 60 20 0.0513

t3E 0 0 0 0 –

t4 1 0.5 80 20 0.0569

From the data in Table 3.13, the mean and coefficients of variation of times t1 – t4 can

be estimated. The following is an example applied to the time t that is defined by 3 parameters

a, b and c as follows

t = A(1 + kaa)(1 + kbb)(1 + kcc) (3.1)

Hence approximate equations for the mean value are

tH(typical) = mean(A). (1 – jIa) (1 – jIb) (1 – jIc) (3.2)

tL(typical) = mean(A). (1 – jIa) (1 – jIb) (1 – jIc) (3.3)

where j is a subjective dispersion factor related to the experts making the assessment, i.e.

a=jIa, b=jIb, c=jIc (3.4)

Solving (3.2) and (3.3) simultaneously leads to the values of j shown in Table 3.13. Then

using equation (3.4) leads to the following mean values of t1 – t4.


40

mean (t1) = 16.7(1 + 0.236 k1) (1 + 0.148 k2) (1 + 0.266 k3)

mean (t2) = 4.9 (1 + 0.0771 k4) (1 + 0.0578 k5) (1 + 0.0675 k6)

mean (t3B) = 27.4 (1 + 0. 497k7) (1 + 0.710 k8) (3.5)

mean (t3C) = 37.9 (1 + 0.222 k7) (1 + 0.556 k8) (1 + 0.389 k9)

mean (t3D) = 24.2 (1 + 0.205 k7) (1 + 0.513 k8) (1 + 0.359 k9)

mean (t3E) = 0.0

mean (t4) = 14.5 (1 + 0.455 k10) (1 + 0.284 k11) (1 + 0.512 k12) (1 + 0.398 k13)

(1 + 0.398 k14)

Similarly the spread of the time estimates can be used to provide a rough estimate of the

coefficient of variation of t1 – t4 as follows:

( ) ( ) ( ) ( )

( ) ( )

H H L L

H L

t typical t unlucky t typical t unluckycov(t)= 0.5

t typical t typical

(3.6)

this leads to the following estimates of the coefficient of variation:

cov (t1) = 0.646

cov (t2) = 0.813

cov (t3B) = 0.768

cov (t3C) = 0.675 (3.7)

cov (t3D) = 0.750

cov (t3E) = 0.0

cov (t4) = 0.625


41

4 THE PROBABILISTIC MODEL


4.1 Introduction

For engineering purposes, it is useful for a termite attack to be considered to be a probabilistic

event. This report describes the development of a model to predict the risk of attack on a

house in Australia. Such a model is useful for assessing (in a quantified manner) the value of

various protection strategy proposals.

4.2 The Basic Probability Model

4.2.1 The probability Distributions

The probability density function of the time for a house to be attacked by termites is assumed

to be of the type shown in Figure 4.1. The form of this function was chosen to fit the data

found in the Termite Tally. The equation for the density function is assumed to be

btap (4.1)

where a and b are the distribution parameters, and t is the time since time zero, the time at

which the house was constructed. The value of a may be either positive or negative, as shown

in Figure 4.1. The notation of ta and tmax will be used to denote the lower and upper end of the

probability density function.

For the case a 0,

ta = 0 (4.2a)

and for the case a < 0,

ta = – (a/b) (4.2b)

for both cases the integration max

a

=1t

tp dt leads to


42

2 2

max a a/ / 2 / 2/t a b a b a b t t b (4.3)

The mean value of the time of attack, denoted by mean(t), is then derived from

max

a

2 2 3 3max a max a

mean

/ 2 / 3

t

tt pt dt

a t t b t t

(4.4)

The variance 2

t of the time of attack is given by

max

a

22 2

23 3 4 4max a max a

mean

/ 3 / 4 mean

t

tt

pt dt t

a t t b t t t

(4.5)

The coefficient of variation V(t) of the time to attack is then given by

V(t) = (t) / mean(t) (4.6)

t max

t

a

p

(a) for positive a

p = a + bt

ta = 0

t max

t

p

(b) for negative a

p = a + bt

t a

age of house (yrs)

age of house (yrs)

t max

t

a

p

(a) for positive a

p = a + bt

t max

t

p

(b) for negative a

p = a + bt

t a

age of house (yrs)

age of house (yrs)

Figure 4.1. Probability density functions of the time of a termite attack.

4.2.2 The True Risk in the Past

The probability Ptrue (to) that a house has been attacked before time to is evaluated as follows:

For to < ta,

Ptrue (to) = 0 (4.7)

For ta < to < tmax,

o

a

true o

2 2o a o a/ 2

t

tP t pdt

a t t b t t

(4.8)


43

For to > tmax,

Ptrue (to) = 1 (4.9)

4.2.3 True Risk in the Future

The true risk in the future will be defined as the true probability that a house will be attacked

between time t1 and t2, where t1 denotes the time taken since the construction of the house.

This true risk will be denoted as Ptrue,future (t1 t2). To take into account the fact that houses may

have been attacked in the past, the assumption will be made that

Ptrue,future (t1 t2) = Ptrue (t2 – t1) (4.10)

where Ptrue (t2 – t1) is evaluated according to equations (4.7)–(4.9), but with mean(t) chosen as

discussed in Section 4.2.1 to account for the fact that the suburb is effectively t1 years older at

the start of the risk estimate than at the time when the house was built.

4.2.4 Apparent Risk in the Past

This model is required for the purposes of interpreting data obtained from interviewing

people. For practical purposes, the model needs to take into account the fact that reported data

comes from people with a memory of tmean years. The probability that there has apparently

been an attack in buildings of age to will be denoted by Papp (to).

For the case of to < (ta + tmem),

Papp (to) = Ptrue (to) (4.11)

For the case of (ta + tmem) < to < tmax

o

o mem

app o 0

t

t tP t p dt A Bt

(4.12a)

where

A = atmem – (b /2) 2memt (4.12b)

B = btmem (4.12c)

For the case of to > tmax it will be assumed that equation (4.12) still holds true up to a value of

Papp (to) = 1.0, even though this is mathematically not correct. However, the discrepancy will

be assumed to be due to the fact that some of the previously attacked buildings will have been

repaired and will eventually be attacked a second time.

In the Termite Tally, the average time a house had been occupied was 11 years. Taking this

into consideration, plus the results of processing the data in the Tally, it was decided to use

tmem = 20 years in application of the termite attack model.


44

4.2.5 The Apparent Risk in the Future

The apparent risk in the future will be defined as the apparent probability that a house will be

attacked between the time t1 and t2, where t1 and t2 denote the time taken since construction of

the house. This probability will be denoted by Papp,future (t1 t2). To take into account the fact

that some of the houses under consideration may already have been attacked in the past, the

assumption will be made that

Papp,future (t1 t2) = Papp (t2 – t1) (4.13)

where Papp,future (t2 – t1) will be evaluated according to equations (4.11) and (4.12) but using a

value of mean (t) chosen as discussed in Section 4.2.1. To account for the fact that the suburb

is effectively t1 years older than when the house was first built.

tmem tmax

t

P

true risk of attack

apparent risk of attack

0

1.0

ta + tM tmax

t

P

P = A + Bt

true risk of attack

P(house, attack, true, t)


P(house, attack, obsv, t)

0

1.0

ta

age of house (yrs)

age of house (yrs)

Pro

bab

ilit

y o

f at

tack

P

rob

abil

ity

th

at a

ttac

k h

as o

ccu

rred

(a) for positive a

(ii) for negative a

0

ta + tmem tmax

t

P

true risk of attack


0

1.0

ta

age of house (yrs)

(b) for negative a

tmem tmax

t

P

true risk of attack


1.0

age of house (yrs)

(a) for positive a

Pro

bab

ilit

y t

hat

att

ack

has

occ

urr

ed

P

rob

abil

ity

th

at a

ttac

k h

as o

ccu

rred

Figure 4.2. Schematic illustration of the cumulative distribution functions of the attack time.

4.2.6 The Distribution Parameters

It should be noted that two parameters, a and b, are required to define the probability

distribution function. However, after examining the data from the Termite Tally, it was

decided to take b to be a fixed value b = 0.0002. Hence only one parameter is required to

define the probability distribution function. It was therefore an obvious choice to use mean(t)

as the defining parameter. The relationship between a, mean(t) and V(t) are shown in Figures

4.3 and 4.4. The relationship between the mean value and their risk of attack within 50 years

is shown in Figure 4.

The equation relating mean(t) and the probability of attack within 50 years, denoted by

risk50 can be closely approximated by the equation

risk50 = 0.000138 mean(t) × mean(t) – 0.029749 mean(t) + 1.618 (4.14)

For risk50 = 0.2, a typical value of risk that occurs in Australia, the corresponding mean attack

time is mean(t) = 44 years. Equation (4.14) is shown plotted in Fog. 4.5.


45

-0.05

0

0.05

0.1

0 100 200 300

mean attack time [yrs]p

ara

mete

r 'a

'

Figure 4.3. Relationship between the mean time of attack and the model parameter ‗a‘.

Coefficient of variation

0

20

40

60

0 50 100 150 200

mean attack time [yrs]

Co

eff

icie

nt

oi

va

ria

tio

n [

%]

Figure 4.4. The relationship between the mean and coefficient of variation of the time of

attack.


46

Relationship between mean

attack time and true risk of

attack

0

0.5

1

0 50 100 150

mean attack time [yrs]

tru

e r

isk

of

att

ac

k in

50

ye

ars

Figure 4.5. Relationship between mean attack time and the risk of attack.

4.3 The Practical Model

For practical application, the base model must be modified so that the target house is closer

than 50 m to the adjoining suburbs. In addition, the model must allow for the possibility that

there may be mature nests nearby at time zero, the year in which the house is constructed.

This configuration is illustrated in Figure 4.6.

Figure 4.6. House and land surrounded by existing buildings and nest sites

at time zero.


47

Figure 4.7. Venn diagram of probabilities of the occurrence of termites at time zero.

The Venn diagram of probabilities at time zero is shown in Figure 4.7. The total

probabilities will be divided into three subgroups defined as follows:

P1 = Probability of termites in the garden

=Pgarden (4.15)

P2 = Probability of termites in the adjoining suburbs but not in the garden

=Psuburb–PsuburbPgarden/suburb (4.16)

P3 = Probability of no termites either in the garden or the adjoining suburbs

=1–Psuburb–Pgarden+PsuburbPgarden/suburb (4.17)

where Pgarden and Psuburb denote the probabilities that there are termites in the garden and the

suburb respectively at time zero, and Pgarden/suburb denotes the probability that there are

termites in the garden, whenever termites are found in the suburb.

Note that

P1+P2+P3=1.0 (4.18)

Note also that for the necessary condition P3 0, it is necessary that Pgarden/suburb Pgarden

when Psuburb = 1.

The mean time taken to destroy a house, based on expert opinion, is then assumed to be

given by

all probabilities

(area = 1.0)

probability of

termites in suburbs

but not in garden

probability of

termites in garden

probability of no

termites in garden

or suburbs


48

1 2 3 4

2 2 3 4

3 1 2 3 4

mean mean 0.5

10mean

20

mean

expertt P t t t

dP t t t

P t t t t

(4.19)

where d denotes the shortest distance from the fence to the house or 10 m, whichever is less

(see Figure 4.6).

Examination of data in the Termite Tally indicates that a suitable equation for

estimating Psuburb is

suburb suburb

suburb

suburb

100, 100 years;

1 100 years.

t tP

t

(4.20)

where tsuburb denotes the age of the suburb at time zero, the year in which the target house was

built.

Also, in the absence of any available data, it will be assumed that

Pgarden = 0.5Psuburb (4.21)

Pgarden/suburb = 0.5 (4.22)


49

5 COMPUTATION MODEL


5.1 Concept

The concept behind the computation model is that it commences with an estimate of the mean

attack time using the model developed by expert opinion in Section 3. This mean attack time

is then used to obtain the parameters of the probabilistic model which in turn is used to

estimate the risk of termite attack. However before this can be done, the predictions of the

expert opinion model must be calibrated against real data. To do this, information from the

Termite Tally described in Section 2 will be used.

The data from the Termite Tally indicates that for the probability distribution function

of attack times, suitable calibration choices are for a parameter b = 0.0002, and the mean time

to attack is given by a calibration factor β to be discussed, i.e.

mean(t)=β mean(texpert) (5.1)

5.2 Calibration of the Model

From the data of the Termite Tally as shown in Figures 2.3 and 2.16, it is assumed for

calibration purposes that on average

P(garden,nest,obsv,0)=0.25 (5.2)

Furthermore the following assumptions will be made

P(garden, nest, true, 0) = 2 x P(garden, nest, obsv, 0) = 0.5 (5.3)

P(suburb,nest,true,0)=P(garden,nest,true,0)=0.5 (5.4)

Substitution of these values and d = 2 into equation (4.19) leads to

mean(ttotal) = 0.25mean(t1)+0.9mean(t2)+t3+t4 (5.5)

In equation (5.5) the value of mean (ttotal) is an estimate of the average time to attack,

assessed entirely on the basis of expert opinion and corresponds to mean(texpert) in equation

(1). To allow for the fact that there may be a bias error by the experts in these estimates, a

calibration factor will need to be introduced, so that the best estimate of mean attack time,

mean (tmodel) is given by

mean(tmodel)=mean(ttotal) (5.6)

One estimate of the mean(tmodel) for average conditions can be obtained from the

Termite Tally where the data is grouped according to temperature zones. For this case, the


50

data for Zone 2 may be considered to be average and the values of the constants A and B used

in equations (4.12) are 0.08 and 0.004 respectively. This leads to a value of mean(tmodel) =

44.1 years.

A second estimate of a typical mean(tmodel) can be obtained from the Termite Tally

where the data is grouped according to agro-ecological zones. For this case, the data for the

combined Zones 2 and 3 may be taken as average. For this case, the values of A and B are

found to be 0.04 and 0.004 respectively. This leads to a value of mean(tmodel) = 50.2 years.

The data from the Termite Tally also indicates that for the probability distribution

function of attack times, suitable calibration choices are for a parameter b = 0.0002.

The model matches the Termite Tally data for average hazard zone conditions when the value

of mean(t)=44 yrs is used. Figure 5.1 shows a plot of a distribution with mean(t)=44 years,

and Figure 5.2 shows the same plot compared with the findings of the Termite Tally.

Taking into account the scatter of the Termite Tally data, the model appears to give as

good a fit as can be expected for average conditions. Reasons for the kink and the dotted

extension of the predicted graph in Fig 5.2 can be seen in Fig. 5.1.

0

0.5

1

1.5

0 50 100 150 200

age of house (years)

pro

ba

bilit

y t

ha

t h

ou

se

ha

s

be

en

att

ac

ke

d

true risk

apparent risk

mean(tmodel) = 44.1 years

Figure 5.1. Computed risk for the calibration case;

{true risk = P(house, attack, true, t); apparent risk = P(house, attack, obsv, t)}.


51

Figure 5.2. Calibration of expert opinion with data from the Termite Tally.

A matter to be decided in the choice of mean(texpert) for calibration purposes is to decide

to what extent were termite barriers used for the houses that featured in the termite survey.

Data taken from Table 10 of the report by Cookson and Trajstman (2002) shown in Table 5.1

below would tend to indicate that the average house was probably protected only by ant-caps.

In the following Table 5.2, the three estimates were obtained by taking all k-values to be

either -1, 0, +1 respectively.

Table 5.1 Estimate of termite barriers at the time of the Termite Tally

(After Cookson and Trajstman 2002)

Treatment Percentage of houses*

Soil poisoning 25

Ant caps only 20

*does not include 25% who were uncertain of treatment

The questionnaire did not ask whether the protection methods were installed before or after termite attack, so

we cannot determine directly which protection methods were in place t the time of the survey

Table 5.2. Suburban model (Expert Opinion model)

Mean time for activity (years)

Termite activity High est. Mean est. Low est.

Component

0.25*mean(t1) 8 4 2

0.9*mean(t2) 5 5 4

mean(t3) 75 30 6

mean(t4) 83 15 1

Total time mean(ttotal)

no termite barrier present 96 24 7

with termite barrier present 171 54 13


52

Table 5.3 shows a comparison between estimates of the mean time of termite attack

based on both Expert Opinion model and Termite Tally data. On the basis of this data, it

would appear that a suitable calibration factor would be in the range 1.0-1.5. For the

purpose of this report the value = 1.5 has been chosen, i.e.

mean(t)=1.5 mean(texpert) (5.7)

Table 5.3. Attack time of average models

Data source Mean value

(yrs)

Coefficient of

variation

Expert Opinion model

(no termite barrier present)

Expert Opinion model

(with termite barrier present)

Termite Tally (temperature zone 2)

Termite Tally (agro-ecological zones 2 & 3)

23.8

53.8

44.1

50.2

0.42

0.43

0.46

0.43

5.3 The Coefficient of Variation

An additional check on the uncertainty predictions of the model can be obtained by

comparing the coefficients of variation as predicted by the Expert Opinion and the Reliability

model. It is also to be noted in Table 5.3 that the computed coefficients of variation for all

cases are quite similar.

It is found that the coefficient of variation of the attack time corresponds well with the

reliability model if the true coefficients of variation of each of the time components t1 t4 are

given by

cov(t) = 0.7 cov(texpert) (5.8)

where cov(t) is the value of cov given by using equation (3.7) and (4.19).

A comparison between the variability of the model and the expert opinion is shown in

Figure 5.3. The two models are in reasonable agreement. No attempt was made to check the

uncertainty shown by the data from the Termite Tally, but this may be possible.


53

Figure 5.3. The coefficient of variation for a given value of the mean attack time (The thin lines

correspond to the various choices of the type of barrier system used).

factor 0.7 on expert estimates of cov

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200

mean(t) [yrs]

co

v(t

)

expert opinion

model

reliability model


54

6 RISK ASSESSMENT EQUATIONS


6.1 Hazard Parameters

As indicated, the model makes a quantified risk estimate on each specific house based on a

number of parameters related to that house. The input parameters chosen for practical

application are as follows:

termite hazard zone

age of surrounding suburbs

distance of house from boundary

wood in the garden and under the house

type of ground contact for the house

environment of vulnerable timber

However, for practical application some approximations are introduced. First, each hazard

parameter is given a hazard score, depending on whether it is considered to describe a high,

medium or low hazard. These hazard scores are denoted by h. They have been derived

empirically to fit exact equations. Then for each particular house, the hazard scores are added

so as to obtain a hazard score total, denoted by H. The scores for each particular hazard are

given in Table 6.1. The classification of a hazard level is denoted by c. The zone classification

for hazard h1 is shown in Figure 6.1. The descriptions for hazards h4 – h7 are given in Section

6.3. Once the hazard score has been evaluated for each of the parameters h1 – h7, the hazard

score total H is obtained as a summation of these scores as indicated in Table 6.2.

Table 6.1. Hazard scores for termite attack

c1 Location Zone(1)

Hazard score

h1

1 B 0

2 C 2

3 D 4

(1) See Figure 6.1; the hazard for zone A is considered to be negligible.


55

c2 Age of suburb(2)

Hazard score

h2 1 <10 yrs 0

2 10-70 yrs 2

3 >70 yrs 4 (2)

A suburb refers to an area in which at least 20% of the land is covered by buildings.

c3 Distance to nearest

boundary

Hazard score

h3 1 >8 m 0

2 2—8 m 0.5

3 <2 m 1.0

c4 Quantity of wood in

garden and under

house(3)

Hazard score

h4

1 low 0

2 medium 0.5

3 high 1.0 (3)

See Section 6.3.1.

c5 Hazard related to

ground contact(4)

Hazard score

h5 1 low 0

2 medium 1

3 high 2 (4)

See Section 6.3.2.


type of construction

materials(5)

Hazard score

h6

1 low 0

2 medium 1

3 high 2 (5)

See Section 6.3.3.


exposure of

material(6)

Hazard score

h7

1 low 0

2 medium 1

3 high 2 (6)

See Section 6.3.4.


56

Table 6.2. Evaluation of hazard score total

Hazard factor Hazard score

Location zone h1

Age of suburb h2

Distance to boundary h3

Wood in garden h4

Ground contact h5

Construction material h6

Timber exposure h7

Hazard Score Total H:

6.2 Supplementary Hazard Parameters

Table 6.3. Inspection parameter I

I Hazard

Level Period between inspections (yrs)

1 low <1

2 medium 1 – 5

3 high >5

Table 6.4. Maintenance parameter M

M Hazard

level

Period between chemical treatments

(yrs)*

1 low Tm

2 medium 2 Tm

3 high >8 Tm

* Tm denotes the period between re-treatments as recommended by the

chemical manufacturer.


57

6.3 Expanded definitions of hazard parameters

Figure 6.1. Termite hazard zonation.

6.3.1 Procedure for assessing the hazard h4 due to the quantity of wood occurring in a

garden and under a house

Table 6.5 shows in quantitative terms some typical distributions of wood corresponding to

low, medium and high hazard levels of termite attack. For other distributions of wood,

suitable estimates may be made through interpolation of these values.

Table 6.5 Definition of hazard h4 assessment due to occurrence of wood in the garden and

under the house

Hazard

class

Number of potential nesting

sites(1)

Typical distance between substantial

food source (m)(2)

Low <2 >20

Medium 2–5 5–20

High >5 <5

(1) Examples of potential nesting sites

The following refers to potential nest sites for harbouring mature colonies which are not more than 50 m from

the building.

Tree (diameter larger than 300 mm)

Tree stump or untreated pole (diameter larger than 200 mm)

Untreated landscape timber (e.g., sleepers, retaining walls, length > 1.0 m, height > 0.5 m)

Woodheap (height >0.5 m, ground contact area 0.5 x 0.5 m, length of periods that bottom layer woodheap is

untouched > 1 year)

Compost heap

Wood ―stepping stones‖

Subfloor storage (height >0.5 m, ground contact area >0.5 x 0.5 m, length of period which it is untouched

>1 year).

Solid infill under a verandah

Any part of a building with water leaking under it

(2) Example of a substantial food source

A typical example of a substantial food source would be a piece of timber equal to or greater than 200 50 mm

as the surface lying in ground contact.


58

6.3.2 Definition of ground contact

Table 6.6 gives examples of building construction that leads to high, medium and low hazard

of termite attack related to ground contact characteristics

Table 6.6 Examples of hazard h5 assessment due to the nature of the

ground contact of a house

Hazard class

Ground contact elements

Low

House supported by exposed concrete piers or

steel stumps more than 2 m high

Medium

Intact concrete slab on ground; House on stumps less than 600 mm high with ant caps

and made of concrete or treated timber(1)

or heartwood

of durable timber(2)

High

Construction does not comply with AS 3660.1

Building not inspective according to AS 3660.2

Concealed entry zones of any type

Floor connected to ground by stair cases of

untreated softwood, untreated non-durable

timber(3)

, untreated sapwood of durable timber;

Attached patio with solid infill

Concrete slab-on-ground with large cracks and/or

unprotected pipe penetrations

Floors connected to ground by elements

containing hidden cavities (e.g. masonry

construction, deeply grooved elements, members

in imperfect contact).

Brick veneer house Leakage of moisture to ground

Timber floor less than 600 mm off the ground

(1) treated timber refers to timber treated according to AS 1604.1–2002 [5].

(2) for a listing of timber durable classes 1 and 2, see AS 5604–2003 [8].

(3) for a listing of non-durable timber of classes 3 and 4 see AS 5604–2003 [8].

6.3.3 Hazard level h6 related to type of construction material

Table 6.7 gives examples of high, medium and low hazard of termite attack related to the type

of material used for construction.


59

Table 6.7 Examples of hazard h6 assessment related to the type of

construction material used

Hazard class Type of construction material attacked

Low

Treated timber(1)

Untreated heartwood of durability class 1(2)

Medium

Untreated heartwood of durability class 2(2)

High

Untreated hardwoods of durability classes 3 and 4(2)

Untreated sapwood of all species

Composite wood boards

(1) treated timber refers to timber treated in accordance AS 1604–2002 [5].

(2) for naturally durable timber classes, see AS 5604–2003 [8].

6.3.4 Hazard h4 related to exposure of timber

Table 6.8 gives a method for assessing the hazard h7 due to the nature of exposure of timber.

Table 6.8. Examples of hazard h7 related to exposure of timber

Hazard class Exposure of timber

low

High human activity

High up a building

Humidity <30%

medium Exposed to rain

high

No disturbance and dark

(e.g. wall stud, double leaf masonry wall, roof

member.)

Exposed to sources of

moisture so as to be periodically wet

Abandoned houses or

mostly vacant holiday houses

Humidity >90%

6.4 Computational Procedure

6.4.1 Computing the mean time to attack mean(t)

To compute the risk of a hazard attack, the mean attack time mean(t) is first necessary to

compute mean(t) using equations (3.5), (4.19) and (5.7). This value of mean(t) is then be used

to evaluate both the true and apparent risk of termite attack .


60

To do the computations, the input data is used to derive the hazard classification

parameters c1 – c7 as defined in Table 6.1. In addition, the inspection parameter I and the

maintenance parameter M as defined in Tables 6.3 and 6.4 respectively.

From the above input data, the factors k1 – k14 required for equation (3.5) are derived as

follows:

k1 = 2 – c1

k2 = 2 – c2

k3 = 2 – c4

k4 = 2 – c1

k5 = 0

k6 = 2 – c4

k7 = 2 – c1

k8 = 2 – I

k9 = 2 – M

k10 = 2 – c1

k11 = 2 – c5

k12 = 2 – I

k13 = 2 – c6

k14 = 2–c7 (6.1)

In addition to k1 – k14, there are two additional input parameters required), i.e. tsuburb and

d, These are indirectly defined by the hazards h2 and h3 respectively. The actual values are

taken to be given by values shown. in Table 6.1..

6.4.2 Computed Risk and Hazard Score

The value of mean(t) needs to be computed for each of 3 values for each of the seven input

hazard parameters h1 – h7. This gives 37 = 2187 values of mean(t) for each choice of barrier

type, inspection quality and maintenance quality. For each of these values the corresponding

value of the hazard score total H can be evaluated according to Table 6.2. Two examples of

these computations are shown in Figures 6.2 and 6.3. It was noted that for all cases a

correlation value of R2 > 0.82 was obtained, indicating that the hazard score total is a good

predictor of mean(t).


61

Figure 6.2. Relationship between mean time of attack mean(t) and the hazard score total H

for the case of no termite barrier and medium frequency of inspection.

Figure 6.3. Relationship between mean time of attack mean(t) and the hazard score total H

for the case of a toxicant chemical barrier, medium maintenance frequency and high

inspection frequency.

As indicated in Section 4.2.6, the value of mean(t) is directly related to the risk of

attack. In this study, the risk of attack within a 50 year period is used for illustrative purposes.

Slices of computed data were used to assess the relationship between hazard score total H and

mean attack time mean(t), and thereby the relationship between hazard score total H and the

true risk of attack within 50 years, denoted by risk(50).

The limits of mean(t) used to choose a data slice are given in Table 6.9. For each data

slice, the mean value of the hazard score total H obtained from the data slice is given in Table

6.10. Using this data, graphs such as those shown in Figures 6.4 and 6.5 can be plotted, which

no barrier, medium inspection

0

50

100

150

0 5 10 15 20

Hazard score total H

M

ea

n(t

),

yrs

toxicant chemical, medium

maintenance, high frequency of

inspection

0

100

200

300

0 10 20

Hazard score total H

Mean

(t),

years


62

show a roughly linear relationship between H, the hazard score total, and risk(50), the risk of

failure within 50.

On the basis of these graphs, it was decided to write the relationship between risk and

hazard score total as follows:

risk(50) = 20 + m*[H – H(20)] (6.2)

H = H(20) + [risk(50) – 20]/m (6.3)

where risk(50) denotes the probability of a termite attack within 50 years, and H(20) denotes

the hazard score total for which risk(50) = 20%

Computed values of H(20) and m are given in Table 6.11. Note that an average value of

m is taken for each type of barrier. These values were used to compute the values of risk.

Table 6.9. Description of data slices used to assess the relationship between

hazard score total and risk of attack

Data slice

No.

Risk(50)

(%)

Notation

for Hazard

score total

H

mean(t)

(yrs)

Data slice limits on

mean(t)

(yrs)

Lower limit Upper limit

1 20 H(20) 72 70 74

2 30 H(30) 63 61 65

3 40 H(40) 55 53 57

4 50 H(50) 47 45 49


63

Table 6.10 Hazard score totals for various values of risk of attack within 50 years

Barrier Inspection

quality

Maintenance

quality H(20)* H(30) H(40) H(50)

Physical 1 9.407 10.95 12.127 13.649

barrier 2 8.573 8.538 9.58 10.369

3 4.039 5.285 6.245 7.074

Toxic 1 1 na**

na**

na**

na**

Chemical 2 1 13.299 na**

na**

na**

3 1 6.764 8.095 8.986 10.583

1 2 na**

na**

na**

na**

2 2 10.362 11.5 13.153 na**

3 2 5.908 6.695 8.065 8.692

1 3 10.917 11.963 13.553 na**

2 3 7.963 9.007 10.176 11.628

3 3 4.292 5.528 6.574 7.765

Repellent 1 1 13.443 na**

na**

na**

chemical 2 1 9.439 11.209 11.698 14.071

3 1 5.639 6.583 7.866 8.554

1 2 11.124 12.092 13.73 na**

2 2 8.211 9.299 10.571 11.821

3 2 4.61 5.833 6.777 8.025

1 3 9.068 9.803 11.253 12.269

2 3 6.934 7.762 8.789 9.881

3 3 3.939 4.884 5.908 7.028

No barrier 1 5.871 6.69 7.137 8.014

2 4.402 5.362 5.957 7.035

3 2.417 3.348 4.213 5.451

*H(20) denotes the total hazard score that will result in a risk of 20% that a termite attack will

occur within 50 years

** The notion ―na‖ denotes that even for the maximum hazard score, the target risk should

not be attained


64

Figure 6.4. Risk for the case of no barrier.

Figure 6.5 Risk for the case of a steel mesh barrier.

Physical barrier

0

5

10

15

0 20 40 60

Risk(50) (%)

Hazard

sco

re t

ota

l H

high

med

low

inspection

quality

NO BARRIER

0

2

4

6

8

10

0 20 40 60

Risk(50) %

Hazard

sco

re t

ota

l H

high

med.

low

inspection quality


65

Table 6.11. Parameters for the Risk equations

Barrier

type

Maintenance

quality

Inspection

quality H(20)* m**

Physical

high 9.407 10

barrier med 8.573 10

low 4.039 10

Toxic

high

high no limit 8

Chemical med 13.299 8

low 6.764 8

medium

high no limit 8

med 10.362 8

low 5.908 8

low

high 10.917 8

med 7.963 8

low 4.292 8

Repellent high

high 13.443 8.5

chemical med 9.439 8.5

low 5.639 8.5

medium

high 11.124 8.5

med 8.211 8.5

low 4.61 8.5

low

high 9.068 8.5

med 6.934 8.5

low 3.939 8.5

No barrier

high 5.871 11.5

med 4.402 11.5

low 2.417 11.5

* H(20) denote the total hazard parameter that will cause a risk of attack of 20% in 50 years

** m denotes the inverse slope of the risk-hazard relationship of the type shown in Figures 6.4 and

6.5.

6.5 Computing Risk

The computational procedure is based on values of hazard parameters h1 – h7 shown in Table

6.1 and the supplementary parameters I (inspection) and M (maintenance) given in Tables 6.3

and 6.4 respectively.Unless otherwise stated, all risks herein will refer to the risks of a termite

attack occurring within a 50 year period.

It was shown in the previous Section that there is a near linear relationship between the

hazard score total H and the risk of an attack occurring within 50 years, denoted by Risk(50),

which will be written as follows:

Risk(50) = 20 + m*[H – H(20)] (6.4)

where H(20) denotes the value of H when Risk(50)=20%. Values of m and H(20) for various

protection strategies are given in Table 6.6


66

6.6 Acceptable Risk

One application for risk estimates is in the limitation of risk to a specified value. For example

such a limitation may be used to draft building regulations that are specific to a region of

Australia. Also, since the risk estimate may be made on a house by house basis, it may also be

used by a pest control operator who wishes to assess the risk of a house that he is treating and

then to take action so as to limit his liability exposure. Table 6.7 shows the level of

maintenance and inspection that is required to maintain the condition Risk(50)<20%.

Table 6.7. Maintenance and Inspection Quality Requirements

Barrier

type

Maintenance

quality

Inspection

quality

Limit on total

hazard score*

Physical high 9.4

med 8.6

low 4.0

Toxic high high no limit

Chemical med 13.3

low 6.8

medium high no limit

med 10.4

low 5.9

low high 10.9

med 8.0

low 4.3

Repellant high high 13.4

chemical med 9.4

low 5.6

medium high 11.1

med 8.2

low 4.6

low high 9.1

med 6.9

low 3.9

No barrier high 5.9

med 4.4

low 2.4

*hazard limit denotes the hazard value for which

the probability of attack within 50 years is 20%


67

6.7 Risk Management

6.7.1 Cost Assumptions

In assessing the cost of various protection strategies, the following assumptions are made for

costs over a 50 year period.

The cost of good quality inspection is assumed to be $500 every 2 years. Combining

this information with the classifications given in Table 4 leads to the following total costs

over a 50 year period:

cost for high quality inspection = $25,000

cost for medium quality inspection = $6,000

cost for low quality inspection = $2,000

The cost of good quality maintenance for chemical treatments is assumed to be $2,000

every 5 years. Combining this information with the classifications given in Table 5 this leads

to the following total costs over a 50 year period:

cost for high quality maintenance = $20,000

cost for medium quality maintenance = $10,000

cost for low quality maintenance = $2,000

The cost of installing a physical barrier such as Granitgard or Termimesh is taken to be

$1,000.

Should a termite attack occur, the cost of the potential damage is classified as follows:

cost of low damage = $2,000

cost of medium damage = $5,000

cost of high damage = $20,000

6.7.2 Comparative Costs of Termite Protection Strategies

The effective cost of a protection strategy is taken to be given as follows:

Cost of strategy = cost of installation of physical barriers + cost of maintenance of

chemical barriers + cost of inspection +

(probability of attack)*(costs incurred if an attack occurs) (6.5)

6.8 Some Computed Examples

6.8.1 Applications to Risk Assessments

Equation (6.4) above may be used directly to assess the risk of attack within a 50 year period.

As an example, the risk for average conditions is shown in Table 6.8. The computed results

show that the risk of attack in a 50 year life covers a wide range from 1% in the case of a

toxic chemical barrier to 61% in the case of no barrier at all. This type of information is useful


68

to the building user if he happens to be risk averse to termite attack and therefore would like

to keep the risk below a certain level, regardless of the cost required to do this.

Table 6.8. Risk of attack for average conditions of hazard

inspection and maintenance

Barrier type Risk of attack in 50 years

(%) None 61

Physical 14

Repellent chemical 18

Toxic chemical 1

Another example would be to use the risk computation to assess the termite protection

strategies to maintain risks at an acceptable, as was done for Table 6.7.

6.8.2 Applications to Risk Management

Some illustrative examples of the cost of various protection strategies are given in Tables 6.8-

6.10.

Table 6.8 shows that when no barrier is used, and the potential damage is $5000, a low

frequency of inspection is the lowest cost option. Table 6.9 shows that for the average

situation the physical barrier appears to offer the lowest cost option. Table 6.10 shows that for

situations involving low hazard and low potential damage, the no barrier and low inspection

option offer the lowest cost solution; whereas for a high hazard and high potential damage

situation, the use of a toxic chemical barrier and a low or medium inspection and maintenance

schedule offers the lowest cost strategy.

Finally, it should be noted that use of the highest quality inspection and maintenance

regime would involve a cost of $45,000 over a 50 year period. While such actions will reduce

the risk of a termite attack to negligible proportions, even in the highest hazard situations, the

cost is still larger than the amount of $20,000 which is assumed to be the value for a high

damage potential (even if there are two attacks within the next 50-year period); i.e. while the

use of high quality inspection and maintenance may be justified in terms of giving peace of

mind to a risk averse clients, it cannot be justified in terms of cost effectiveness.

Table 6.8. Cost of protection strategy for average condition when no barrier is used.

(Potential damage to a home is $5,000)

Quality of

inspection

Cost of protection strategy (x $1,000)

Low hazard Medium hazard High hazard

High 25.0 27.2 29.5

Medium 6.8 9.1 11.4

Low 4.4 6.7 9.0


69

Table 6.9. Cost of a protection strategy for average conditions of hazard, inspection,

maintenance and potential damage

Barrier type Cost of protection strategy

(x $1,000)

None 9.1

Physical 7.7

Repellant chemical 16.9

Toxic chemical 16.1

Table 6.10. Comparison of the cost of protection strategies for high

and low risk situations

Barrier type Level of quality

control

Cost of strategy

[$1,000]

High hazard and

high potential

damage

Low hazard and

low potential

damage

Toxic chemical High

(inspection and

maintenance)

45 45

Medium

(inspection and

maintenance)

22.6 16.0

Low

(inspection and

maintenance)

20.8 4.9

No barrier High

(inspection) 43.1 25.0

Medium


Low


6.8.3 Comment

The above are just some examples of the applications that can be made with a Model that

produces quantified assessments of the risk of a termite attack. The Model should be viewed

as a method for placing all known expert opinion and field data into a unified theory. It is a

simple matter to incorporate other expert opinion or new field data into the Model as these

become available. Without the benefit of this type of Model to quantify risk, it is not possible

to optimise risk management strategies.


70

7 APPLICATION FOR DESIGN GUIDE


7.1 Procedure to compute risk

First the Hazard score H is evaluated using Tables 7.1-7.8. Then, using these hazard scores,

the value of risk(50), the probability of an attack in 50 years is evaluated using the following

equation

risk(50) = 20 + m*[H – H(20)] (7.1)


the hazard score total for which risk(50) = 20%.

Details of the derivation of the procedure, and explanations of the various parameters

cited can be found in ―Manual No. 8 Termite Attack‖ by R.H. Leicester, C-H Wang and M.N.

Nguyen (April 2008).

7.2 Hazard score components

Table 7.1 Hazard score for location Zone

Location Zone* Hazard score

B 0

C 2

D 4

Table 7.2 Hazard score for age of suburb*

Age of suburb Hazard score

<10 yrs 0

10-70 yrs 2

>70 yrs 4

* Suburb refers to area within which at least 20%

of the land is covered by buildings.


71

Table 7.3 Hazard score for distance to nearest property boundary

Distance to nearest

boundary

Hazard score

>8 m 0

2—8 m 0.5

<2 m 1.0

Table 7.4 Hazard score for wood in Garden (and under house)

Quantity of wood in garden and

under house*

Hazard score

low 0

medium 0.5

high 1.0

Table 7.5 Hazard score for contact of house with ground

Hazard related to ground contact* Hazard score

low 0

medium 1

high 2

Table 7.6 Hazard score for type of construction material

Hazard related to type of

construction materials*

Hazard score

low 0

medium 1

high 2

Table 7.7 Hazard score for exposure conditions of timber

Hazard related to exposure

of material*

Hazard score

low 0

medium 1

high 2


72

7.3 The Hazard Score Total

Table 7.8 Evaluation of hazard score total


Location zone

Age of suburb

Distance to boundary

Wood in garden

Ground contact

Construction material

Timber exposure

Hazard score total H =

7.3.1 Comment on Hazard Zone A (Tasmania)

Currently, Tasmania does not have subterranean termites, which damage houses and

accordingly termite management measures are not warranted.


73

7.4 Parameters for the risk equation

The parameters m and H(20) to compute the risk of termite attack according to equation (7.1)

can be from Table 7.9


Barrier

type

Maintenance

quality

Inspection

quality H(20)* m**

Physical

high 9.407 10


low 4.039 10

Toxic

high

high no limit 8


low 6.764 8

medium

high no limit 8

med 10.362 8

low 5.908 8

low

high 10.917 8

med 7.963 8

low 4.292 8

Repellent high

high 13.443 8.5


low 5.639 8.5

medium

high 11.124 8.5

med 8.211 8.5

low 4.61 8.5

low

high 9.068 8.5

med 6.934 8.5

low 3.939 8.5

No barrier

high 5.871 11.5

med 4.402 11.5

low 2.417 11.5


** m denotes the inverse slope of the risk-hazard relationship.

7.5 Acceptable Risk

One application for risk estimates is in the limitation of risk to a specified value. For example

such a limitation may be used to draft building regulations that are specific to a region of

Australia. Also, since the risk estimate may be made on a house by house basis, it may also be

used by a pest control operator who wishes to assess the risk of a house that he is treating and

then to take action so as to limit his liability exposure. Table 7.10 shows the level of

maintenance and inspection that is required to maintain the condition Risk(50)<20%.


74

Table 7.10. Maintenance and Inspection Quality Requirements

Barrier

type

Maintenance

quality

Inspection

quality

Limit on total

hazard score*

Physical high 9.4

med 8.6

low 4.0

Toxic high high no limit

Chemical med 13.3

low 6.8

medium high no limit

med 10.4

low 5.9

low high 10.9

med 8.0

low 4.3

Repellant high high 13.4

chemical med 9.4

low 5.6

medium high 11.1

med 8.2

low 4.6

low high 9.1

med 6.9

low 3.9

No barrier high 5.9

med 4.4

low 2.4

*hazard limit denotes the hazard value for which

the probability of attack within 50 years is 20%


75

8 APPLICATION FOR TIMBERLIFE


8.1 Procedure to compute risk

First the Hazard score H is evaluated using Tables 8.1-8.8. Detailed definitions of the hazards

have been given in Section 6.3. Then, using these hazard scores, the value of risk(50), the

probability of an attack in 50 years is evaluated using the following equation

risk(50) = 20 + m*[H – H(20)] (8.1)


the hazard score total for which risk(50) = 20%.

The risk can also be combined with the costs associated with the termite management

strategy and the cost of failure, should such a failure occur according to the equation (8.2)

given in Section 8.5

Details of the derivation of the procedure, and explanations of the various parameters

cited can be found in ―Manual No. 8 Termite Attack‖ by R.H. Leicester, C-H Wang and M.N.

Nguyen (April 2008).

8.2 Hazard score components

Table 8.1 Hazard score for location Zone

Location Zone* Hazard score

B 0

C 2

D 4

Table 8.2 Hazard score for age of suburb*

Age of suburb Hazard score

<10 yrs 0

10-70 yrs 2

>70 yrs 4

* Suburb refers to area within which at least 20%

of the land is covered by buildings.


76

Table 8.3 Hazard score for distance to nearest property boundary

Distance to nearest

boundary

Hazard score

>8 m 0

2—8 m 0.5

<2 m 1.0

Table 8.4 Hazard score for wood in Garden (and under house)

Quantity of wood in garden and

under house*

Hazard score

low 0

medium 0.5

high 1.0

Table 8.5 Hazard score for contact of house with ground

Hazard related to ground contact* Hazard score

low 0

medium 1

high 2

Table 8.6 Hazard score for type of construction material

Hazard related to type of

construction materials*

Hazard score

low 0

medium 1

high 2

Table 8.7 Hazard score for exposure conditions of timber

Hazard related to exposure

of material*

Hazard score

low 0

medium 1

high 2


77

8.3 The Hazard Score Total

Table 8.8 Evaluation of hazard score total


Location zone

Age of suburb

Distance to boundary

Wood in garden

Ground contact

Construction material

Timber exposure

Hazard score total H =

8.3.1 Comment on Hazard Zone A (Tasmania)

Currently, Tasmania does not have subterranean termites, which damage houses and

accordingly termite management measures are not warranted.


78

8.4 Parameters for Evaluating the Risk Equation

As an example, Table 8.9 shows the inspection and maintenance regimes that must be put in

place to ensure that the risk of termite attack is no greater than 20% in a 50 year period.


Barrier

type

Maintenance

quality

Inspection

quality H(20)* m**

Physical

high 9.407 10


low 4.039 10

Toxic

high

high no limit 8


low 6.764 8

medium

high no limit 8

med 10.362 8

low 5.908 8

low

high 10.917 8

med 7.963 8

low 4.292 8

Repellent high

high 13.443 8.5


low 5.639 8.5

medium

high 11.124 8.5

med 8.211 8.5

low 4.61 8.5

low

high 9.068 8.5

med 6.934 8.5

low 3.939 8.5

No barrier

high 5.871 11.5

med 4.402 11.5

low 2.417 11.5


** m denotes the inverse slope of the risk-hazard relationship of the type shown in Figures C3–C5.

8.5 Risk Management procedure

8.5.1 Cost Components

In assessing the cost of various protection strategies, the following assumptions are made for

costs over a 50 year period.


79

The cost of good quality inspection is assumed to be $500 every 2 years. Combining

this information with the classifications given in Table 4 leads to the following total costs

over a 50 year period:

cost for high quality inspection = $25,000

cost for medium quality inspection = $6,000

cost for low quality inspection = $2,000

The cost of good quality maintenance for chemical treatments is assumed to be $2,000

every 5 years. Combining this information with the classifications given in Table 5 this leads

to the following total costs over a 50 year period:

cost for high quality maintenance = $20,000

cost for medium quality maintenance = $10,000

cost for low quality maintenance = $2,000

The cost of installing a physical barrier such as Granitgard or Termimesh is taken to be

$1,000.

Should a termite attack occur, the cost of the potential damage is classified as follows:

cost of low damage = $2,000

cost of medium damage = $5,000

cost of high damage = $20,000

8.5.2 Effective Cost of Termite Protection Strategies

The effective cost of a protection strategy is taken to be given as follows:

Cost of strategy = cost of installation of physical barriers + cost of maintenance of

chemical barriers + cost of inspection +

(probability of attack)*(costs incurred if an attack occurs) (8.2)


80

References

Cookson, L.J. and Trajstman, A. 2002. Termite Survey and Hazard Mapping. CSIRO Forestry

& Forest Products Report No. 137.

http://www.ensisjv.com/ResearchCapabilitiesAchievements/WoodProductsProcessingandProt

ection/WoodProcessing/TermiteHazardMapping/tabid/369/Default.aspx

Commonwealth of Australia, Ecologically Sustainable Development Working Groups. Final

Report — Agriculture. Australian Government Publishing Service, Canberra, 240 pp.,

1991.

http://www.ensisjv.com/ResearchCapabilitiesAchievements/WoodProductsProcessingandProtection/WoodProcessing/TermiteHazardMapping/tabid/369/Default.aspx

http://www.ensisjv.com/ResearchCapabilitiesAchievements/WoodProductsProcessingandProtection/WoodProcessing/TermiteHazardMapping/tabid/369/Default.aspx

Manual 8 – Termite attack - FWPA · 2018-08-02 · Manual 8 – Termite attack PROJECT NUMBER:...

Documents

Transcript of Manual 8 – Termite attack - FWPA · 2018-08-02 · Manual 8 – Termite attack PROJECT NUMBER:...