Pole Service Life – An Analysis of Country Energy Data
Nathan Spencer
Koppers Wood Products Pty. Ltd., Sydney, Australia
nathan_spencer@koppers.com.au
Leith Elder
Country Energy, Goulburn, Australia
leith.elder@countryenergy.com.au
ABSTRACT
Country Energy‟s network services the majority of rural NSW, and includes more than 1.3
million utility poles. The network also spans a wide range of environmental hazards and is
therefore an excellent example when trying to determine pole life expectancies in various
conditions.
Inspected, condemned and failed pole data was gathered from Country Energy‟s asset
management database and analysed to determine life expectancies for different pole types and
different species of timber poles, both over the whole network, and by region. This paper
presents the results of this study, suggests opportunities for this type of study in the future,
and presents a discussion of the constraints to be considered when analysing the data.
INTRODUCTION
Timber utility poles have been used around the world ever since the telegraph was invented.
Ever since this beginning utilities and suppliers have put an enormous amount of research
into all aspects of timber utility poles, from strength and stiffness to expected service life. In
the past, studies into timber pole service life have focussed on small samples of poles
intended to represent the wider pole community, or on samples placed in high hazard test
sites intended to represent the worst case scenario. The latter is usually used to test and
compare different preservative formulations and other protective techniques.
Unfortunately these studies – as valuable as they are – do not always produce information
which can be used by utilities to accurately predict future demand. This is best shown by the
lack of realisation of the predicted demand for utility poles published in the report
“Australian Timber Pole Resources for Energy Networks” (1). It would appear that one of the
main reasons utilities have trouble predicting their future pole demand is because previous
estimates of Australian pole service life have not corresponded well with what is suggested
by crude estimates based on total poles divided by number of replacements each year. In
addition, the authors are unaware of any realistic service life analysis previously completed
for concrete or steel poles, and therefore this has also been considered in this study.
One set of reports that the authors are aware of exists for some service life analysis done on
Tasmanian hardwood poles for the Hydro Electric Commission (HEC, now Aurora). The first
was completed in 1981 (2) and looked at 55,000 pressure impregnated poles over 4 regions.
This was done using poles in one year age groups up to 24 years old (the oldest treated poles
in the network). A life table analysis was completed, and since the oldest poles were only 24
years old the expected service life was predicted from the total number of pole years survived
above age 1, divided by the total number of poles in the sample. This gave a life expectancy
across the board of 40 years, but extrapolating the survival curves it was estimated that they
would last 55 years on average.
Following on from this, a report was commissioned by HEC and produced in 1994, to look at
a number of aspects network performance and cost effective options for network maintenance
and pole replacement. The most relevant aspect of this report was that although it made some
educated assumptions along the way, it attempted to predict the number of pole replacements
required for the next 30 years. This included allowance for pole maintenance and staking, but
used assumptions relating to a skewed bi-modal distribution curve, rather than the survival
rates calculated in the original 1981 paper. Surprisingly, even with all the assumptions, the
results show that for 2008 the model predicted around 8,000 pole replacements, which is
approximately twice what was actually used, but still much closer than expected, and
something that could almost be accounted for in a positive difference between the actual and
predicted efficacy of ground line maintenance chemicals.
Another paper (3) was presented to the annual conference of the Electricity Supply Engineers
Association of NSW in 1986 which included a summary of numerous papers that had
previously attempted to predict pole service life. This paper suggested a service life of 30
years for untreated timber, and 40 years for treated timber. Based on the authors experiences,
the value of 30 years for untreated timber in this paper appears to have stuck in the minds of
many in the industry and has been used since to depict the life expectancy of all timber poles
– treated and untreated – despite the values produced in later studies.
In 1988 a study was undertaken by the Connell Group (4) using data from Monaro County
Council, which produced Weibull parameters of 17.24 for scale and 15 for shift which added
together gave a characteristic life (63.2% of poles predicted to fail) of 34.24 years for
untreated poles.
Another study by Ron Stillman (5) used data on CCA treated poles from SEQEB and HECT.
The Tasmanian data gave Weibull parameters of 43 for scale and 5 for shift giving a
characteristic life of 48 years with a standard error of 12 years. The Queensland data yielded
96 for scale and 0 for shift giving a characteristic life of 96 years with a standard error of 40
years. The same study gave Weibull parameters of 114 for scale and 15 for shift for concrete
poles in Queensland giving a characteristic life of 129 years with a standard error of 25 years.
These are just some examples of previous attempts a service life prediction, all using sub-sets
of total populations, rather than the whole population, which current record keeping
techniques and databases give us access to. The two Tasmanian studies are the best examples
known of the use of life table analysis, and of accuracy of predicted replacement rates, and
hopefully they can be improved upon using the techniques presented in this paper.
Country Energy‟s network is made up of the regions shown in Figure 1. The current regional
boundaries have been formed over many years of combining smaller council utility interests
into separate dedicated utilities, which in turn were amalgamated into Country Energy, and
the regions in Figure 1. Even within the last 2-3 years there has been another change to the
regions, with Riverina and South Western regions being combined to form the Southern
region.
This is important background knowledge when considering the validity of the current
database. It is really quite impressive that Country Energy have managed to provide the
amount of data, and the detail of the information, as easily as they did. It is a testament to the
management practices in place during amalgamations to keep continuity of data, and also to
the inspection and record keeping system in place.
Figure 1: Country Energy network.
METHODOLOGY
Choice of Statistical Method
The original intent of this paper was to use a Weibull analysis to determine the characteristic
life expectancy and probability of failure with age. This was to be based on similar methods
to those used in a recent study (6) in which 100,000 poles were inspected in Canada (out of a
population of approximately 2 million) and the data analysed and fitted to different statistical
models to predict trends for the entire network.
After much analysis of the Country Energy data it was found that there was a significant
amount of „bad‟ data which was corrupting the Weibull analysis. Examples of this include
inspected poles with negative ages, figures of more than 250,000 poles that are less than 5
years old (we know from actual historical usage rates of around 6,000 poles p.a. for timber,
that this is not the case), numerous poles with installation years of 1901 (over 150,000 poles
above 100 years old), and even examples of entries that have a material type of Steel and a
species of Blackbutt.
Attempts were made to produce more appropriate Weibull distributions by ignoring the data
with pole ages less than 5 years, and ages greater than 100 years. This gave slightly more
realistic results, but still nowhere near expected based on actual replacement rates.
Using both the whole data and the limited age range produced Weibull curves that quite
closely matched the „actual‟ data distribution used to create the Weibull curve fit. This was
quite deceiving as it seemed to fit the data well, but the service lives predicted for all pole
material types and timber species were well below that which is feasible given the current
replacement rates within Country Energy. An example of such a curve is given in Appendix
B for all the timber in the Northern region. After some consideration it was decided to try and
analyse the data using Abridged Life Tables. These are most commonly used to analyse
human or other biological populations. It was subsequently found that the Abridged Life
Table has a number of advantages over the Weibull analysis for this type of study:







Life tables were developed to analyse populations where only the number of
specimens in an age group and the number of deaths within the age group is known.
Weibull analysis and its close relatives (i.e. Gompertz, Log-Logistic) appear to be
better suited to predictions from a small number of specimens out of a larger group, or
for making predictions of life expectancy and survival curves based on experiments
where the initial population is known and failures noted over time.
Weibull analysis is quite cumbersome for such large amounts of data, especially if
attempts are made to apply censoring to the data (i.e. if the pole was condemned in a
particular year is it actually still ok for half the inspection interval, full interval, etc.)
Life table analysis, although it does include some assumptions, does not require
censoring to give adequate life estimates.
Life table analysis gives specific mortality rates and probabilities of survival for each
interval which can be used in prediction of future pole demand. Weibull analysis on
the other hand is aimed at producing a smoothed fit to the data and using this to
predict future demand. This allows the Life Table to better handle various failure
modes and phenomenon that can cause misalignment of predicted and actual failure
rates when using any of the curve-fitting methods.
The life table is easy to construct from the significant amount of data available, and
can be easily updated year on year to refine predictions, which is important when
considering future pole life expectancy due to affects of different timber
preservatives, untreated poles, different types of steel or concrete poles with different
levels of protection, etc. All of which have been introduced at different times,
affecting different age brackets within the data.
Life tables look at the number of poles of a certain age, and the number of condemned
poles at a certain age. Since the snapshot of the network is obtained from a 5 year
inspection rotation, it is expected that the life tables are less affected by this than the
Weibull analysis.
After considering these options and comparing the results from initial trials, it was decided to
base the analysis on Abridged Life Tables, and not Weibull Analysis as originally intended.
Simpler analysis has also been used to produce some of the results in this paper. These show
failure mechanisms, population make-up, etc. They are based on simple counts of the data
and percentage comparisons.
Constructing the Abridged Life Tables
The data was extracted from Country Energy‟s database into a spreadsheet format. The data
included inspection, condemned pole and failure data for 2004-2008 inclusive, each on a
separate sheet. As an example Table 1 shows the data extracted for the inspected poles, Table
2 shows the data extracted for the condemned poles, and Table 3 shows the data extracted for
the failed poles.
Table 1: Example of Inspected pole data.
Region
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
FSC Area
Bathurst F
SC
Bathurst F
SC
Bathurst F
SC
Bathurst F
SC
Bathurst F
SC
Bathurst F
SC
Bathurst F
SC
Material
Timber
Timber
Timber
Timber
Timber
Timber
Timber
Type
Natural Round
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Species
Age
Total
Spotted Gum
0
2
Ironbark
0
7
Blackbutt
0
23
Tallowood
0
5
Spotted Gum
0
20
Red Mahogany
0
2
Red Bloodwood
0
1
Table 2: Example of Condemned Pole Data
Region
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
Central
Western
FSC
Bathurst
FSC
Bathurst
FSC
Bathurst
FSC
Bathurst
FSC
Bathurst
FSC
Bathurst
FSC
Bathurst
FSC
Asset
ID
Material
Previous
Material
408961
Timber
Timber
407875
Timber
Timber
406786
427805
Timber
Steel
(Column)
424480
Timber
408031
Timber
401445
Timber
Timber
Type
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Previous Type
Copper Chrome
Arsenic(CCA)
Fabricated
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Copper Chrome
Arsenic(CCA)
Natural Round
Natural Round
Natural Round
Natural Round
Natural Round
Natural Round
Task Description
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Pole - Condemned
- Replace
Cause
Internal Decay BelowIDYB
External Decay AboveEXDA
External Decay AboveEXDA
External Decay BelowEXDB
Internal Decay BelowIDYB
Internal Decay BelowIDYB
Internal Decay BelowIDYB
Completed
Date
First Installed
Age
15/02/2005
1/01/2003
2
19/05/2004
1/01/1901
103
19/05/2004
9/08/2000
4
26/11/2008
1/01/1981
28
6/12/2004
6/12/2002
2
25/06/2004
21/05/2004
0
15/02/2005
25/09/2003
1
Table 3: Example of Failed Pole Data.
Asset
Asset ID Label
Task
Source
Description Type
2537465 52249
Pole - Pole
Failure
Reported Completed Task
Cause of
Date
Date
Status Failure
Notes
Fault
10/02/200
Reporting 4
4/11/2003
pole fell over at
Closed Unknown ruby hills
2618511 1529
Pole - Pole
Failure
Pole - Pole
Failure
Maintenan
ce
2/02/2004 11/12/2003 Closed Storm
Fault
Reporting 6/01/2004 26/12/2003 Closed Car
2625688 16159
Pole - Pole
Failure
Fault
15/01/200
Reporting 4
13/01/2004 Closed Termite
818681
940424
Cause
Descriptio Service
n
Status
Pole
Pole replaced, Failure
storm damage Image
CAR HIT
POLE HARD
REPLACE
POLE
...TERMITE
INVESTATION
Asset
Owner
Country
In Service Energy
Country
In Service Energy
Country
In Service Energy
Country
In Service Energy
Previous Date Material
Material Material Changed
Type
Copper
Chrome
Arsenic(
Timber
CCA)
Concrete Timber
Timber
Timber
2/02/2004
9:24
Previous Date Type First
Type
Changed Installed
Natural
Round
Cast
Natural
Round
Copper
Chrome
Arsenic( Natural
CCA)
Round
22/10/200
8 8:07
1/01/2004
Age
0
11/12/2003
0
1/01/1991
13
19/12/200
8 13:57
1/01/2003
1
As Table 1 to Table 3 show, not every field has been filled out, particularly for the failed
poles. Since there were only 429 failures over the 5 year period (not all being actual “pole
falling to the ground” failures), and the accuracy of the failed pole records being
questionable, it was decided to ignore the failed poles in the life expectancy analysis and
consider the poles as “failed” when they are condemned.
The following steps summarise the extraction of the relevant data for use in the Abridged
Life Tables (note that when referring to the „Material‟, „Type‟ and „Species‟ fields for
condemned poles, the „Previous Material‟, „Previous Type‟ and „Previous Species‟ columns
were used):
1. A macro was written in Microsoft Excel to automatically search for missing values in
the „Material‟, „Type‟ and „Species‟ fields. If a missing or unknown value was found
the macro looked at the other two columns to see if their values could be used to fill in
the appropriate value, otherwise, unknown was entered. For instance, if the „Material‟
field was empty or unknown and the species was a timber species the macro would
change the „Material‟ field to timber. If the species field was empty also, but the type
was a timber preservative or “Unknown – Timber”, the „Material‟ field would be
updated with timber. The macro included many other checks and balances for these
three fields, and was applied to both the inspected and condemned pole data.
2. A „Total‟ column was added for the condemned pole data, with a value of one for
each row (one entry per row).
3. The unnecessary columns were removed from the condemned pole data to reduce file
size.
4. The filter command was used to check that there were no remaining blanks in the
„Material‟, „Type‟ and „Species‟ columns, then the „Advanced filter‟ command was
used to extract a list of unique species present in the tables.
5. A macro was then written to count the number of poles in the table representing each
species. The total of the count was then checked against the sum of the total column
to ensure all poles were counted (small discrepancies of 1-2 poles were sometimes
found, but considered insignificant).
6. Another macro was then produced that counted the number of poles of each species,
in each particular age range. The age range chosen for this study was 5 year brackets,
starting at 4 years and less, then up to 100 years and over.
7. The raw data was then copied out using Excel‟s „Advanced filter‟ into region-specific
spreadsheets and the macro‟s created in steps 5 and 6 re-run to produce regionspecific data.
The Abridged Life Tables were then constructed using the species/pole type and age specific
tables, an example of which is included in Appendix A. There are a number of different
methods available for creating Abridged Life Tables. Before proceeding, a comparison of the
results between the Reed-Merrill, Greville, and Keyfitz-Frauenthal methods was completed.
After some consideration, the results were extremely close for the three methods, and since
the more recent Keyfitz-Frauenthal method was significantly more complex and lessconservative than the Greville method, the Greville method was chosen for the analysis.
It is worth noting that the three different methods mentioned above are all essentially ways of
interpolating or „smoothing‟ the data when constructing an Abridged Life Table, and they
gave results extremely similar to the general equations (7) for a Full Life Table (1-year age
intervals).
The following variables were calculated for use in the Greville tables:
n mx
=
𝑁𝑜.𝐶𝑜𝑛𝑑𝑒𝑚𝑛𝑒𝑑
𝑁𝑜.𝐼𝑛𝑠𝑝𝑒𝑐𝑡𝑒𝑑
The central death rate between age „x‟ and age „x + n‟. This is simply the number
of condemned poles in the age interval, divided by the number of inspected poles
in the age interval.
nqx
=
𝑚𝑥
1
1 𝑛
+𝑚 𝑥 +
𝑛
2 12
𝑚 𝑥 −𝑙𝑛 𝑐
The mortality rate, or probability that a pole aged „x‟ will be condemned before
reaching age „x + n‟. Ln(c) is assumed to be 0.095 (8), assuming an approximately
exponential increase in nmx. nqx is set at 1.0 for the oldest age group (i.e. 85+
years).
npx
= 1-nqx
The probability of a pole at age „x‟ not being condemned before it reaches age „x +
n‟.
n lx
= (1-nqx-n)×nlx-n
This is the number of poles surviving to the start of the age interval based on an
assumed theoretical population at x = 0. In these calculations, the assumed initial
pole population is 1,000,000. This is somewhat irrelevant to the results, it is just a
facilitator for the remaining calculations.
ndx
= nlx – nlx+n
Number of poles condemned during the age interval in question.
nLx
= 𝑚𝑥
𝑑
𝑥
The total pole years survived for the age interval.
nTx
= nTx+n + nLx
Total pole years survived beyond the start of the age interval.
nex
=
tpx
𝑇𝑥
𝑙𝑥
Additional life expectancy from the start of the age interval.
𝑙
= 𝑥𝑙 +𝑡
𝑥
The probability that a pole that reaches age „x‟ will survive for another „t‟ years.
PPf
=
1−𝑙 𝑥
𝑙0
The proportion of poles expected to have been condemned by age „x‟.
From these values the total life expectancy for poles reaching each age interval can be
calculated (age of poles at start of interval + additional life expectancy), as well as some
estimations of average pole lives.
A number of methods were looked at for giving “expected” or “average” service lives from
the life tables. These included the following;
1. Averaging the life expectancy for each age group.
2. Using the age by which approximately 50% of the poles are expected to fail.
3. Using the age by which 63.2% of the original pole population are expected to have
failed.
4. Using a weighted average life (WAL) expectancy of each group. This was weighted
by the number of poles surviving to the start of each age interval (nlx).
5. Considering the life expectancy for the first age interval only.
Due to the variability in data availability and quality, the most accurate and meaningful data
was found to be the weighted average life expectancy, with the life expectancy for the first
age interval of possible value also. The main reason the first option was not considered
meaningful in this study was that the average of each age interval‟s life expectancy doesn‟t
take into account the number of poles in each age group, and can be swayed in an unconservative fashion by a data set that has high proportions of early failures, but very few
poles in older groups with very few failures.
The median (50% poles failed) value would normally be useful, but the need to terminate
some data sets at relatively young ages due to inadequate data beyond the final age group
used (ranged between 35+ and 80+ by region and type) meant that in some circumstances the
50% failed poles had not been reached by the final age group. This meant that there were
some data groups where the median life could not be predicted, and thus it was not a good
comparison between different regions, species, material types, etc. The same issue was
encountered for the 63.2% failure level, which was only considered because of the
similarities to Weibull analysis.
Other information that has been extracted from the country energy data such as makeup of
reasons for condemning, and reasons for condemning in each age group have simply been
counted from the raw data and compared with either total number of condemned poles, or
total number of inspected poles.
Note: Since the Greville method for abridged life tables (as with the other methods) produces
errors when there are zero failures within an age group, any such instances had „1‟ entered
into the number of condemned poles column manually. This was only done if the age groups
beyond this point had significant data, otherwise the analysis was limited to the lowest age
group where the data was still appropriate to avoid manual entry and still give meaningful
results. To avoid these issues, a full life table could be considered, rather than the abridged
life tables. This was not done in this study due to time constraints and the amount of data
analysis required.
It is also noted that the 5-year inspection data interval used for the study includes some poles
that have been inspected twice. This makes no difference to the accuracy of the data because
more poles being counted also means more poles being condemned. In fact, greater accuracy
may be gained by increasing the period from which data is gathered to 10 years. However,
this may not accurately show effects of changes in population make-up (treated/untreated,
etc.) and should only be considered with his in mind. Data processing ability may hinder this
approach.
RESULTS & DISCUSSION
The first part of this analysis concentrated on determining the accuracy of the data so that we
could move forward with maximum confidence in the analysis. Figure 2 shows the number of
poles in each age group for the three main material types; timber, concrete and steel. The
timber poles are also shown in two forms; timber poles with known species, and all identified
timber poles.
Timber - Known Species
200000
Timber - Total
Number of poles in service
Concrete
Steel
150000
100000
50000
0
4
14
24
34
44
54
64
74
84
Age group, years (4 = 0 to 4, 9 = 5 to 9, etc. 104 = >100)
94
104
Figure 2: Population breakdown of major pole material types by age across the whole
Country Energy network.
As can be seen in Figure 2, there appears to be some anomalies in the data for poles less than
four years old and greater than 99 years old. Some of this is due to human errors, but for the
majority of the poles above 99 years old it is due to poles with unknown ages due to lost
identification and the default installation year in the database (1901). The difference between
total timber poles and timber poles with known species can also be attributed to lost
identification. For the timber poles with ages four years or less with no species identified,
they are likely to be older poles (greater than 35 years old), with the inspector putting the
installation date as the inspection date. This is likely to mean that estimations of life
expectancy are conservative. It should also be noted that the age group 0-4 years includes
some poles with negative ages!
From Koppers‟ (the only significant timber pole supplier to Country Energy from 2004 to
2008) sales data for 2004 to 2008 for Country Energy, we know there were only around
52,000 poles sold to Country Energy over the 5 year period. However, even the data for poles
with known species gives a figure of 142,879 for the same period (202,514 for total timber).
This is clearly incorrect, and the decision was made to take the conservative route and ignore
all pole data for ages less than or equal to four years, and greater than 99 years. This is
conservative because the poles that are not in the correct age group are likely to be older than
average and would further increase the life expectancy beyond that obtained from an analysis
ignoring the obviously incorrect data.
Network-Wide Analysis
Given that there is such a wide range of timber species and other pole types in Country
Energy‟s network (33 in total), it was also important to narrow down the species and types
considered for further analysis. By looking at the figures in Table 4 it was clear that the main
non-timber pole types are steel and concrete, whilst the combined Ironbarks, Spotted Gum
and Blackbutt make up 53.3% of the total poles recognised as timber, 47.4% of the total pole
population, and 80.7% of the timber poles where the species is recorded in the database.
Therefore, it was decided to concentrate on the five specific material types mentioned, along
with all the timber poles grouped together.
Table 4: All inspected pole types and timber species.
Number of
Poles
% of Known
Timber Species
% of Total
Poles
% of Total
Timber
213910
22.4%
13.2%
14.8%
Blackbutt (New England)
1514
0.2%
0.1%
0.1%
Coast Grey Box
9026
0.9%
0.6%
0.6%
Grey Box
13745
1.4%
0.8%
1.0%
Grey Gum
24038
2.5%
1.5%
1.7%
Grey Ironbark
71866
7.5%
4.4%
5.0%
Ironbark
94058
9.9%
5.8%
6.5%
Messmate
2691
0.3%
0.2%
0.2%
Aluminium
592
N/A
0.0%
N/A
Concrete
116738
N/A
7.2%
N/A
Steel
61231
N/A
3.8%
N/A
Stobie
570
N/A
0.0%
N/A
Pine
3110
0.3%
0.2%
0.2%
Red Bloodwood
18281
1.9%
1.1%
1.3%
Red Box
846
0.1%
0.1%
0.1%
Red Gum
1193
0.1%
0.1%
0.1%
Red Ironbark
21334
2.2%
1.3%
1.5%
Red Ironbark (Narrow Leaf)
596
0.1%
0.0%
0.0%
Red Mahogany
9023
0.9%
0.6%
0.6%
Red Stringybark
1862
0.2%
0.1%
0.1%
368201
38.6%
22.7%
25.5%
Southern Mahogany
158
0.0%
0.0%
0.0%
Stringybark
2586
0.3%
0.2%
0.2%
Sydney Blue Gum
12306
1.3%
0.8%
0.9%
Tallowwood
51421
5.4%
3.2%
3.6%
White Box
1047
0.1%
0.1%
0.1%
White Mahogany
11366
1.2%
0.7%
0.8%
Pole Material
Blackbutt
Spotted Gum
Number of
Poles
12259
% of Known
Timber Species
1.3%
% of Total
Poles
0.8%
% of Total
Timber
0.8%
Yellow Box
977
0.1%
0.1%
0.1%
Yellow Stringybark
6583
0.7%
0.4%
0.5%
Unknown
272
N/A
0.0%
N/A
489656
N/A
30.2%
33.9%
174
N/A
0.0%
N/A
Pole Material
White Stringybark
Timber – Unknown Species
Not Applicable - Tower
Table 5 shows the Weighted Average Life (WAL) expectancy calculated using the methods
described in the Methodology section, along with the Life Expectancy (LE) of a pole at five
years old for the main pole material types and the three main timber species. Also included is
a summary of the total number of poles inspected over the five year time period, the total
number ignored from the life tables due to the inaccuracies described above and the same for
the condemned pole data.
Table 5: Entire Country Energy network – summary of results and quantity of data.
W.A.L.
L.E. @ 5 years
old
Inspected Poles
(Total/Ignored)
Condemned Poles
(Total/Ignored)
All Timber
61
51
1,345,831 / 335,122
36,440 / 19,153
Concrete
41
39
116,738 / 24,866
227 / 102
Steel
67
64
61,231 / 35,334
198 / 152
Blackbutt
65
60
213,915 / 47,505
1,392 / 618
Spotted Gum
69
65
368,206 / 50,287
1,013 / 403
Combined Ironbarks
77
74
187,854 / 44,156
887 / 549
Pole Type / Species
Table 5 gives a good idea not only of life expectancy for different pole types, but also a good
comparison of the amount of data that contributed to each result. Generally, the timber and
concrete figures seem reasonable with at least 75% of the inspected data and about 50% of
the condemned data being within the target ages for the analysis. Steel on the other hand only
considers about 42% of the inspected pole data and only 23% of the condemned data. This is
mainly due to the relatively young population of steel poles in Country Energy‟s network (as
can be seen in Figure 2). However, even with apparently conservative data, the life
expectancy of steel is about the same as for timber. In the case of concrete poles, which have
a much more established population almost entirely in the Southern region of the network,
the life expectancy is well below that of the average timber pole across the network.
The results agree with general expectations that Ironbarks are more durable than Spotted
Gum, which in turn appears more durable than Blackbutt. However, all three main timber
types have an average life expectancy greater than 60 years.
Table 6 shows the oldest age group and the percentage of poles in the theoretical population
remaining at the beginning of the last age group. This is to give a better indication of the
scope of the data available.
Table 6: Entire Country Energy network, last age group and % poles failed by start of last
age group.
Pole Type /
Species
Oldest Age Group
(years)
Original Theoretical Pole Population Remaining at Start of Last
Age Group (%)
All Timber
80+
19.1
Concrete
55+
13.1
Steel
70+
71.6
Blackbutt
80+
29.1
Spotted Gum
80+
30.2
Combined
Ironbarks
80+
73.1
To compliment this Table 7 and Table 8 were produced to show a breakdown of the
condemned vs. Inspected poles by age group for each of the pole types across the whole
network. The figures in both Table 7 and Table 8 should be taken into consideration when
comparing the results for different pole materials. For instance, there is comparatively little
data for steel and concrete poles of significant age, which can affect the validity of mortality
rates.
Please note that the yellow highlighted entries were added to allow the analysis to proceed
without errors, as discussed in the Methodology section.
Table 7: Entire Country Energy network, condemned and inspected by material and age
group, timber steel and concrete.
All Timber
Age
Interval Condemned
Inspected
Steel
Concrete
Condemned
Inspected
Condemned
Inspected
5-9
563
61,231
7
10,329
8
15,850
10-14
595
91,966
8
4,295
4
23,699
15-19
747
126,993
8
4,131
7
30,466
20-24
1,076
121,728
6
1,663
14
18,033
25-29
1,173
113,013
2
2,117
13
2,617
30-34
1,603
101,558
2
662
18
206
35-39
2,560
115,114
1
657
37
214
40-44
4,373
124,444
3
711
17
246
45-49
1,832
73,999
3
195
3
265
50-54
1,214
42,552
1
76
3
60
55-59
1,292
29,673
1
114
1
216
60-64
29
1,790
1
110
65-69
62
1,075
2
827
70-74
9
1,019
1
10
75-79
156
4,454
80+
3
100
Table 8: Entire Country Energy network, condemned and inspected by material and age
group, three main timber species.
Blackbutt
Age
Interval Condemned
Inspected
Spotted Gum
Ironbarks
Condemned
Inspected
Condemned
Inspected
10
9,134
5-9
22
19,720
13
24,762
10-14
16
15,651
16
37,699
8
19,414
15-19
28
24,369
28
52,269
25
23,223
20-24
95
28,463
57
48,851
21
18,533
25-29
95
25,816
64
48,607
16
13,074
30-34
111
22,095
70
43,867
16
12,139
35-39
127
15,425
117
38,260
38
18,028
40-44
152
11,556
187
20,637
115
16,603
45-49
41
1,545
11
1,643
43
6,593
50-54
48
673
27
550
18
2,308
55-59
32
758
14
595
7
2,028
60-64
1
81
1
31
2
188
65-69
1
58
1
50
1
152
70-74
1
60
1
16
1
224
75-79
3
136
2
77
16
2,022
80+
1
4
1
5
1
35
From the life table analysis, the survival curves shown in Figure 3 were generated based on
the theoretical initial population of 1 million poles.
1,000,000
900,000
No. Poles Surviving
800,000
700,000
All Timber
600,000
Concrete
500,000
Steel
400,000
Blackbutt
300,000
Spotted Gum
200,000
Combined Ironbarks
100,000
0
10
20
30
40
50
60
70
80
90
Age (Years)
Figure 3: Survival curves across the entire network for the main pole materials.
100
These curves might not look like much, but they actually tell us quite a lot about the
performance of the poles over time, and they are a good method of checking the validity of
the analysis against known phenomenon. The following observations have all been made
using Figure 3 and the known history for the Country Energy network, and they are all
important considerations when planning for future replacement rates.

In almost all cases there is an increase in timber poles condemned after about 40-50
years.

If a timber pole survives beyond about 60 years, the probability that the pole will be
condemned reduces. This supports a theory that both authors postulated – that once a
pole reaches a certain age (50-60 years old), there is a good chance it will last a lot
longer. This can also be seen with the concrete poles older than 40 years, and with
steel poles greater than about 55 years.

The phenomenon of older poles having reduced probability of failure is believed to be
due to two main characteristics of the pole; the inherent durability of the individual
pole, and the environment in which the pole is located. For instance, timber poles
within a species group will have varying durability being a natural material, but their
two main deterioration mechanisms – termites and rot – are highly dependant on the
environment the pole is in. Poles that survive past the 60 year mark, are likely to be of
either high natural durability within their normal species range, or in a low hazard
location, or both.
The same is true for steel and concrete, however both these materials have consistent
rates of decay no matter what environmental conditions they are placed in and their
construction and durability measures (amount of cover concrete, thickness of
galvanising, etc.) will determine the rate of the corrosion. Timber generally has no
discernable rate of degrade unless the environmental conditions required are present
(i.e. a particular moisture content range, or the presence of termites). All materials in
favourable conditions will give excellent service life, and the majority of the older
poles of each type in this study are expected to be in this category.

Up until the 45 year age bracket, the performance of the main pole materials is
extremely similar (steel, Blackbutt, Spotted Gum, Ironbark, and concrete until 30
years). At which point Blackbutt starts to diverge, followed by Spotted Gum at around
50 years.

The dropping away of Blackbutt at a quicker rate than other timber species suggests
that current Blackbutt poles will do the same into the future. However, this gap is
expected to decrease and the slope of the older portion of the curve expected to flatten
out into the future because any timber pole older than 50 years is practically
guaranteed to be untreated, as the first timber treatment plants were installed in
Australia in the late 1950‟s. Treatment also took a number of years to be applied to
every pole that went into Country Energy‟s network as it is now, because at the time
treatment began Country Energy did not exist and NSW was covered by numerous
independent county council regions that did not all start using treated poles at the
same time.

The authors are not sure if the same will hold true for concrete, or if the survival curve
for steel will remain similar to Ironbark due to changes in products over time (i.e. is
the concrete cover on concrete poles more or less than it was 30 years ago? How
many Stobie poles are included in the concrete pole data, which will have different
performance characteristics?). Also affecting the curves for steel and concrete will be
their move into other areas that have different soil conditions, coastal exposure,
different storm conditions, etc. With timber still being present in good number across
the whole network, this is not expected to greatly influence the timber curves into the
future.
Analysis by Region
Table 9 shows the results obtained when using the life table analysis for the region-specific
data. The numbers with a star next to them are considered to have inadequate data to make a
reasonable estimate, mainly because there are generally far less than 10,000 poles in each
data set.
Table 9: WAL expectancies by material and region.
Region
All
Timber
Blackbutt
Spotted
Gum
Combined
Ironbarks
Concrete
Steel
Mid North Coast
53
45
41
41
7*
32*
Far North Coast
51
45
41
41
34*
34*
Northern
74
72
63
78
32*
47*
North Western
49
48
53
53
40
42*
Far West
43
39*
40*
40*
36
37*
Southern
49
52
50
49
34
40
Central Western
58
53
46
55
35*
36*
South Eastern
53
46
46
46
34*
34*
It is quite apparent that the figures for each region separately, tend to be much lower than
those for the network as a whole. This is due to the variability of data quality between
different regions, as well as a result of different hazard levels and different age distributions
of poles between the regions. If the regions were further split into field service centres there
would be an even more pronounced differentiation due to environmental constraints, but
unfortunately there would be a lack of condemned pole data to accurately support the life
table analysis (i.e. avoiding zero condemned poles for an age group) for such a small area.
Table 10 shows the oldest age group and the percentage of the theoretical population
remaining at the start of the oldest age group to assist in understanding the validity of the
data. Once again, the ones with a star next to the age group did not have sufficient data
available to be considered a reasonable representation.
Table 10: Oldest age group and percent of original population remaining at beginning of
oldest age group – by region and material type.
Region
All Timber
Blackbutt
Spotted Gum
Combined
Ironbarks
Concrete
Steel
Mid North Coast
55+ (72%)
45+ (91%)
40+ (98%)
40+ (97%)
35+* (0%)
35+* (14%)
Far North Coast
55+ (57%)
45+ (84%)
40+ (97%)
40+ (93%)
35+* (70%)
35+* (58%)
Northern
80+ (49%)
80+ (45%)
80+ (3%)
80+ (77%)
50+* (2%)
50+* (64%)
North Western
70+ (11%)
55+ (15%)
55+ (58%)
55+ (70%)
50+ (21%)
50+* (13%)
Region
All Timber
Blackbutt
Spotted Gum
Combined
Ironbarks
Concrete
Steel
Far West
50+ (30%)
40+* (64%)
40+* (81%)
40+* (86%)
35+ (90%)
40+* (53%)
Southern
65+ (18%)
55+ (60%)
50+ (83%)
50+ (51%)
45+ (1%)
40+ (88%)
Central Western
70+ (29%)
55+ (42%)
45+ (91%)
55+ (81%)
35+* (70%)
35+* (91%)
South Eastern
55+ (70%)
45+ (97%)
45+ (98%)
45+ (95%)
35+* (38%)
35+* (72%)
It is to be expected that WAL‟s close to the beginning of the oldest age group indicate that
the poles would be likely to have an even longer life expectancy with better data. However,
Table 11 shows another aspect of the data that should be considered when interpreting the
results – the total number of poles inspected and condemned by material type and region
(within the age limits 5-99 years inclusive) – to give an idea of the size of each data set being
drawn upon.
Table 11: Number of poles inspected and condemned for each material by region.
Region
Combined
All Timber
Blackbutt Spotted Gum Ironbarks
Concrete
Steel
(Insp./Cond.) (Insp./Cond.) (Insp./Cond.) (Insp./Cond.) (Insp./Cond.) (Insp./Cond.)
Mid North Coast
100,895 / 617
14,404 / 29
36,712 / 13
18,033 / 12
117 / 1
6,101 / 11
Far North Coast
87,909 / 690
9,538 / 24
51,416 / 29
8,781 / 8
866 / 0
2,671 / 13
Northern
172,312 / 1,385 26,503 / 37
30,818 / 28
46,174 / 89
1,786 / 2
1,572 / 1
North Western
119,781 / 3,684 30,253 / 233
30,503 / 151
14,706 / 94
14,341 / 17
1,913 / 2
11,800 / 58
5,041 / 13
18,171 / 3
1,378 / 3
155,440 / 4,942 22,851 / 161
51,634 / 184
14,008 / 47
42,904 / 96
7,082 / 8
Central Western 180,188 / 3,565 27,675 / 182
48,115 / 122
20,722 / 53
9,943 / 3
2,834 / 0
55,093 / 20
15,545 / 20
3,471 / 1
1,848 / 1
Far West
Southern
South Eastern
33,758 / 706
7,947 / 82
154,511 / 1,078 26,376 / 14
Table 11 shows that once the data is broken down into regions and material types, the amount
of data is highly variable. Interestingly, the mark of the old timber supply industry can be
identified within the regional data. For instance, the far west has quite a low proportion of
Blackbutt and Ironbark poles, as pole sourcing was historically more regional, and these
poles were not in abundance in the far West or New England areas which supplied this
region. Conversely, the Northern region is situated in the middle of the main area for
sourcing the common, high durability Ironbark poles, and these are the most common timber
pole in the analysis for this region. Also, in the Far North Coast region Spotted Gum is a
prevalent species, which is shown in the Country Energy data.
This phenomenon is more related to the period where poles were sourced by county councils
separately, and also the period where there were many more treatment plants around the state
which sourced their own local poles where possible. These days there is one main supplier to
Country Energy, with one main manufacturing plant based in the Far North Coast region and
sourcing from all the main pole supply regions north of Bulahdelah. This plant supplies the
whole network, so each region will get a similar mix of pole species for new poles.
How Can this be Used?
The biggest opportunity for life table analysis of pole records is the ability to use it to predict
replacement rates into the future. This is something that is currently hard to do for utilities
because without good network wide information, the results will be little more than a guess.
By using the probability that poles at the start of a certain age group will reach the next age
group, a prediction of the number of poles to be condemned at any stage in the reasonable
future can be produced.
Table 12 shows the simple calculation that is done to predict replacement rates for the next 5
years. This analysis predicts that Country Energy will condemn over 16,000 timber poles per
year for the next 5 years. With replacement rates at around 6,000 poles per annum, and the
fact that there is around 20% of the current timber pole population ignored in these numbers,
it is obvious that the analysis appears to be conservative.
Table 12: Predicted timber pole replacements for entire Country Energy network.
Age Group
Surviving Timber
Poles
Probability of
condemning
Predicted Number
condemned in 5 years
Average condemned per
year
5–9
61,231
0.045013
2,756
551
10 – 14
91,966
0.031871
2,931
586
15 – 19
126,993
0.029016
3,685
737
20 – 24
121,728
0.043309
5,272
1,054
25 – 29
113,013
0.050674
5,727
1,145
30 – 34
101,558
0.076115
7,730
1,546
35 – 39
115,114
0.105675
12,165
2,433
40 – 44
124,444
0.162166
20,181
4,036
45 – 49
73,999
0.116970
8,656
1,731
50 – 54
42,552
0.133645
5,687
1,137
55 – 59
29,673
0.197165
5,850
1,170
60 – 64
1,790
0.078052
140
28
65 – 69
1,075
0.253024
272
54
70 – 74
1,019
0.043274
44
9
75 – 79
4,454
0.161675
720
144
80+
100
1.000000
100
20
Apart from data quality, other reasons for the high prediction could be;
 Impacts of staking/re-butting not fully recognised.
 Impacts of ground line maintenance techniques not fully recognised.
 Impacts of treatment introduction and later increases in treatment retentions may not
be fully recognised.
 The Greville smoothing techniques are too conservative when determining
probability of poles being condemned before the next age interval.
If a full life table was created with yearly age brackets, the possibility of errors due to the use
of the Greville method would be removed, but the other possible factors mentioned above
could only be accounted for by further breakdown of the data to determine their affects on
service life. Even then, the most important technique for producing more accurate service life
and replacement requirement predictions will be improving the accuracy of the database, and
re-doing the analysis on a regular basis with updated data.
Additional Observations
Of some interest to Country Energy and any other utility is the breakdown of what is causing
the deterioration of their pole assets. Note that in some instances steel and concrete poles
have been entered into the data base with reasons other than that of corrosion, so this will
slightly affect the accuracy of the data.
Table 13: Breakdown of reasons for condemning by region (% of total poles condemned).
Region
Int .Decay
B.G. / A.G.
Ext. Decay
B.G. / A.G.
Termite
Corrosion
Internal Pipe
Undersize
Mid North Coast
17.6 / 19.2
19.6 / 6.6
25.0
7.4
1.9
2.7
Far North Coast
11.5 / 35.0
13.5 / 10.5
22.7
1.9
1.4
3.5
Northern
18.9 / 14.7
10.6 / 5.7
45.3
0.1
2.7
2.0
North Western
22.9 / 10.8
3.7 / 3.1
45.0
0.2
11.9
2.4
Far West*
11.3 / 2.2
3.9 / 0.6
78.7
2.1
0.2
0.9
Southern
34.3 / 7.8
5.5 / 4.5
39.1
0.3
3.5
5.2
Central Western
28.9 / 14.1
4.3 / 8.3
26.7
0.1
15.2
2.5
South Eastern *
48.0 / 14.5
9.2 / 5.3
13.7
0.3
4.2
4.8
Whole Network
24.8 / 15.0
8.6 / 6.2
35.2
1.1
5.9
3.1
* = No entries in database of corrosion cause, steel and concrete entries changed to corroded to better represent
data.
Table 13 indicates that the increase in treatment retentions has effectively solved the soft rot
issues of the 1970‟s and 1980‟s (remembering that there will still be some poles remaining in
the network that are untreated or treated to lower retentions). Not surprisingly, termites are
the main reason for condemning poles in the Far West region, but in most other regions it
appears on par or less than the combined decay issues. It is understood that Country Energy
have done some figures for termites as a cause of condemning poles at the Field Service
Centre level, which further defines the locations of greatest termite hazard.
To put a further perspective on the condemned pole rates, Table 14 gives the percentage of
total poles inspected for each region and reason for condemning. The total timber poles are
used for internal and external decay, termite, internal pipe and undersize, while the combined
steel, concrete, aluminium, stobie and tower poles inspected is used for corrosion.
Table 14: Breakdown of reasons for condemning by region (% of total poles inspected).
Region
Int .Decay
B.G. / A.G.
Ext. Decay
B.G. / A.G.
Termite
Corrosion
Internal Pipe
Undersize
Mid North Coast
0.34 / 0.37
0.38 / 0.13
0.48
1.78
0.04
0.05
Far North Coast
0.28 / 0.87
0.33 / 0.26
0.56
0.72
0.03
0.09
Northern
0.32 / 0.25
0.18 / 0.10
0.77
0.03
0.05
0.03
North Western
0.66 / 0.31
0.11 / 0.09
1.29
0.04
0.34
0.07
Far West*
0.24 / 0.05
0.08 / 0.01
1.71
0.07
0.01
0.02
Southern
1.00 / 0.23
0.16 / 0.13
1.14
0.02
0.10
0.15
Central Western
0.50 / 0.24
0.07 / 0.14
0.46
0.02
0.26
0.04
South Eastern *
0.49 / 0.15
0.09 / 0.05
0.14
0.05
0.04
0.05
Whole Network
0.53 / 0.32
0.18 / 0.13
0.75
0.20
0.13
0.07
* = No entries in database of corrosion cause, steel and concrete entries changed to corroded to better represent
data.
The simple change in perspective between Table 13 and Table 14, has quite a large impact on
the way the issues are viewed. It is obvious that in most cases the percentage of poles being
condemned is extremely small. For the most part the same conclusions reached from Table
13 can be seen in Table 14 also, however in some cases it shows that termites are more of a
problem in the North Western region as opposed to the Northern region, whereas before they
were essentially the same. However, both tables still show that it is really only in the far west
region where termites significantly outweigh decay reasons for condemning poles.
Two main issues should be considered when looking at both these tables;
 Are poles being condemned prematurely, and what cause is being recorded for these
poles?
 What proportion of the timber poles being condemned for each reason are treated or
untreated?
 What proportion of the poles recorded with termites as the cause have decay as a
contributing factor also?
 If there are two apparent causes, which one started first (i.e. see the discussion below
about termites being attracted to decaying timber).
This is extremely useful data as it can provide facts and figures for commercial decisions
related to pole material types used, and for suppliers to improve their products in the most
cost effective manner. However, if the above questions are not properly considered, then the
path chosen by utilities and the suppliers in their R&D may not provide the best solution. It
must also be noted that other forms of the presented data might be more useful, such as
separate percentages for treated and untreated poles, given that there is still such a large
proportion of untreated poles in the system (42% of all timber poles), but these are no longer
purchased.
Another interesting graph to be produced from the Country Energy data is that in Figure 4.
This graph shows the relationship which was previously postulated by the authors between
termite attack and presence of decay. Because the main termites of commercial interest in
NSW require a source of moisture to survive, they are more likely to attack moist timber,
which is also more likely to be decaying. This is commonly recognised by the commercial
termite inspection and pest control industry, but appears to be of little consideration in the
utility industry to date. Generally, termites require moisture to create the right environment
for survival, which is why they are attracted to rotting timber, which also requires moisture to
support decay fungus. However, termites do also forage for food in a random manner, which
will account for a portion of the termite attack also.
A useful addition to the Country Energy data base in the future might be to expand the
reasons for condemning to include a combination of termite & internal decay.
3.00
Percentage of Poles Inspected
2.50
Termites
Decay
2.00
1.50
1.00
0.50
0.00
0
10
20
30
40
50
60
70
80
90
Age (Years)
Figure 4: Poles condemned due to Termites and Combined Decay across the whole network
(% of timber poles inspected).
One other point to consider is the regulatory life assumed for pole materials. For instance,
timber poles in NSW are assumed to depreciate over a period of between 45 and 55 years.
Although we know that on average there should be more than half the poles that last longer
than this, what is the impact of the 30-40% or more of poles that might not reach the assumed
depreciation life? Is it taken into consideration when calculating the depreciation so that it
averages out? This is a complex issue, but something that could be assisted by the analysis of
good data which Country Energy is building towards with their database.
CONCLUSION
Although there are some issues with the accuracy of the data used in this study, there is
enough quality data across the energy network to produce some reasonable service life
estimates, and to obtain some useful information about various performance characteristic of
different pole types and timber species.
This study has shown that a conservative estimate for the average life expectancy of a timber
pole in Country Energy‟s network is 61 years, that Blackbutt (65 years), Spotted Gum (69
years) and Ironbark (77 years) poles all exceed this average life expectancy, and that there is
a slight increase in durability from Blackbutt to Spotted Gum and then to Ironbark as was
expected.
Steel and concrete poles appear to have life expectancies of 67 and 41 years respectively, but
the availability of quality data does not appear to be sufficient to be confident in these values
across the whole network, and if a comparative life cycle cost was to be done between
different pole materials in the future a sensitivity analysis for these pole types is
recommended.
It has also been shown that different regions have different pole life expectancies due to
many possible factors, but the most likely being environmental variations between regions.
This is supported by the variability in types of decay and amount of termite attack causing
poles to be condemned in different regions.
It has also been shown that there appears to be a correlation between incidence of decay and
termite attack, which was to be expected.
All of these results will help Country Energy to manage their network, but it will also help
pole suppliers to better understand the issues contributing to the service life of their products,
which will help steer development of such products into the future.
The other main outcome of this study is the detailing of the abridged life table analysis
procedure that appears to be very useful in predicting service lives and replacement rates for
utility pole populations. It is not considered to be perfect, but with databases improving all
the time and some tweaking of the Greville method used (or the use of full life tables with
one year age groups), the life tables will only get more accurate.
It is hoped that the points made about the accuracy of the tables for this study, along with the
discussion of the results with the history of the evolution of Country Energy‟s pole
population in mind, will provide valuable inspiration for other people wishing to complete a
similar study.
Recommendations for further study would be to repeat this study on a bi-annual basis, to look
at a full life table (1 year age groups), and to possibly look at data from a longer period of
time to improve estimates of central death rates. The beauty of the life table is that data over
many inspection cycles can be grouped together to improve the data base. Since it only
considers the age of the pole at inspection and the age of the pole when condemned, there is
no need to only look at the current population or to normalise all the pole ages based on when
they were inspected in the inspection cycle. However, with an evolving population, going
back too far could begin to introduce more errors due to changes in practices/materials.
BIBLIOGRAPHY
1. Francis, L. and Norton, J. Australian Timber Pole Resources for Energy Networks.
Innovative Forest Products, Horticulture & Forestry Science. Queensland : Department of
Primary Industries & Fisheries, 2006. p. 11.
2. Januba, H., et al. The Life Expectancy of CCA Pressure Impregnated Power Poles in
Tasmania. s.l. : Unpublished, 1981.
3. The Future of the Wood Pole in Overhead Line Construction. Smith, R.G. Sydney :
Electricity Supply Engineers Association of NSW, 1986.
4. Connell Group. Economic Evaluation of Management Strategies for Replacement or
Reinstatement of Defective Poles in the Electricity Distribution System. Sydney : New South
Wales Government Department of Energy, 1988.
5. Probabilistic Derivation of Overstress for Overhead Distribution In-Line Structures.
Stillman, R.H. s.l. : IEEE Transactions on Reliability, 1994. Vol. 43 No.3.
6. Dalta, S V and Pandey, M D. Estimation of Life Expectancy of Engineering Components
from Inspection Data. Ontario : University of Waterloo, 2005. TR-01.
7. Wikipedia. Life Table. Wikipedia. [Online] Wikipedia, 03 05 2009. [Cited: 23 06 2009.]
http://en.wikipedia.org/wiki/Life_table.
8. Siegel, J S and Swanson, D A. The Methods and Materials of Demography. Second
Edition. San Diego : Elsevier Academic Press, 2004. pp. 312-315. 0-12-641955-8.
9. Concrete Structures. s.l. : SAI Global, 2001. AS3600.
10. Smith, R.G. The Future of the Wood Pole in Overhead Line Construction. Sydney :
Connell Group, 1986.
APPENDIX A – Example of Abridge Life Table
This example is for all timber poles across the whole network.
Abridged Life Table
Using the Greville method
Age Interval
5 years
Average Life Expectancy
66
years
Probability of
Condemning Probability
Before next of survival to
Central
age interval,
next age
Age Condemned Surviving Death Rate
Greville
interval,
Interval
Poles
Poles
(5mx)
Method (5qx) Greville (5px)
0 - 4
5 - 9
563
61,231
0.0092
0.045013
0.9550
10 - 14
595
91,966
0.0065
0.031871
0.9681
15 - 19
747
126,993
0.0059
0.029016
0.9710
20 - 24
1,076
121,728
0.0088
0.043309
0.9567
25 - 29
1,173
113,013
0.0104
0.050674
0.9493
30 - 34
1,603
101,558
0.0158
0.076115
0.9239
35 - 39
2,560
115,114
0.0222
0.105675
0.8943
40 - 44
4,373
124,444
0.0351
0.162166
0.8378
45 - 49
1,832
73,999
0.0248
0.116970
0.8830
50 - 54
1,214
42,552
0.0285
0.133645
0.8664
55 - 59
1,292
29,673
0.0435
0.197165
0.8028
60 - 64
29
1,790
0.0162
0.078052
0.9219
65 - 69
62
1,075
0.0577
0.253024
0.7470
70 - 74
9
1,019
0.0088
0.043274
0.9567
75 - 79
156
4,454
0.0350
0.161675
0.8383
80 +
3
100
1.0000
1.000000
0.0000
1.000000
19,153
335,122
Ignored
Approx. Median Life
52
years
Approx. Age at 63.2%
failures
62
years
Weighted average life
expectancy
61.13
Years
Number of
Total pole
Total pole Additional life
poles
Number of poles
years
years survived expectancy at
surviving to
condemned
survived for
above the
the start of
Total Life
start of age during the age
the age
start of the
the age
Expectancy
interval, interval, Greville interval,
age interval
interval,
at each
Greville (5lx)
(5dx)
Greville (5Lx)
(5Tx)
Greville (5ex)
interval
1,000,000
45,816,361
1,000,000
45,013
4,895,515
45,816,361
46
51
954,987
30,437
4,704,458
40,920,847
43
53
924,550
26,827
4,560,666
36,216,389
39
54
897,724
38,879
4,398,399
31,655,723
35
55
858,845
43,522
4,193,091
27,257,324
32
57
815,323
62,058
3,931,711
23,064,232
28
58
753,265
79,601
3,579,386
19,132,522
25
60
673,663
109,245
3,108,827
15,553,136
23
63
564,418
66,020
2,666,702
12,444,309
22
67
498,398
66,608
2,334,695
9,777,606
20
70
431,790
85,134
1,955,242
7,442,912
17
72
346,656
27,057
1,670,080
5,487,670
16
76
319,599
80,866
1,402,118
3,817,590
12
77
238,733
10,331
1,169,691
2,415,472
10
80
228,402
36,927
1,054,306
1,245,781
5
80
191,475
191,475
191,475
191,475
1
81
-
APPENDIX B
Weibull CDF - Northern Region, All Poles
1
Proportion of Poles Failed
0.9
0.8
0.7
0.6
0.5
Actual
0.4
Predicted
0.3
0.2
0.1
0
0
10
20
30
40
50
Age (Years)
R2 ~ 0.85 for the above curve fit.
60
70
80
90
100