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Using data analytics to
prioritise high risk critical pipe failure
National ICT Australia and Sydney Water
have developed a conceptual model for
Sydney Water that predicts high risk
failures on critical water pipes. This model
out performs the current water industry
practices.
Context
NICTA (National ICT Australia) is Australia’s
Information Communications Technology (ICT)
Research Centre of Excellence and the
nation’s largest organisation dedicated to ICT
research. NICTA’s primary goal is to pursue
high-impact research excellence and, by
applying this research, create national benefit
and wealth for Australia. NICTA’s machine
learning group rates among the top five
machine learning groups in the world.
Sydney Water manages 21,000 km of water
and 24,000 km of wastewater pipes. These
include 5,000 km of critical water pipes and
900 km of large concrete wastewater pipes.
Sydney Water’s investment on renewals of
critical water pipes is about $32 million a year
and for smaller water pipes another $30 million
a year. The yearly investment on large
concrete wastewater pipes is $40 million a
year. Targeting better condition assessment
will improve the efficiency of the investment in
pipe renewals.
Nature of collaboration
The collaboration between Sydney Water and
NICTA is based on a partnership arrangement,
with NICTA providing data analytics research
expertise and Sydney Water providing data
and system knowledge.
Governance arrangements are in place to
ensure that the partnership will deliver value to
Sydney Water and its customers.
Current success in predicting failure in
large critical water pipes
Successful collaboration between both parties
during the last three years has resulted in a
conceptual model to prioritise high risk pipes
with a higher level of confidence. This is world
leading research that out performs significantly
the current water industry practice.
Machine learning –the key data analytics
technique
This is a metaphor – treating the machine as a
child and teaching it to use observations
(features or attributes) as inputs and
corresponding labels as outputs. The
combination of observations and labels is
called training data.
Figure 1- Blind folded men and an elephant: machine
learning concept
We take ’blind folded men and an elephant‘,
(Figure 1). The shapes of the ears, teeth, legs,
and other body parts are the observations and
the label is ’elephant‘. Machine learning aims
Using data analytics to priotorise high risk cricial pipe failure
SW149 12/14- © Sydney Water. All rights reserved.
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to create a rule which is able to bundle all the
observations together to the label, avoiding
biased predictions.
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With better targeting of high risks pipes for
critical water main renewals Sydney Water
should reduce cost by several million dollars
over a four year price determination period
and minimise inconvenience to customers
from main breaks.
Detected
failures
Benefits and value to customers
Data analytics method and outcome
Inspected length (km)
Figure 3 - Sydney Water System total pipe analysis
NICTA concept (green) vs water industry practice (red)
We analysed a number of parameters (figure
2) and considered three levels of pipes to
include, elements (pipes), shut down blocks
and mains. We also used Sydney Water’s
economic model and level of constraints
considered for risk modelling for the analysis
and validated the outcome using 2012 data.
NICTA’s concept on critical
pipes 100% more efficient to
identify failures
Further collaboration in smaller water pipes
Currently international water industry practice
is to run to failure for the smaller pipes. We are
planning to collaborate further to extend this
research effort to smaller pipes (<300 mm)
identifying the high risk pipes and prioritising
areas for active leak detection, as there is
significant investment on replacing smaller
pipes and considering the impact to the
customers when breaks occur.
NICTA
Dr Fang Chen
Research Group Manager, Machine Learning
[email protected]
Figure 2 – We analysed a number of parameters using
the total critical water pipe failures from1999 – 2011
within Sydney Water.
The data validation demonstrated that the
NICTA conceptual model for the total Sydney
Water system can identify 100% more failures
for the same effort of inspection.
Carly Perry
Business manager
Infrastructure, Transport & Logistics
[email protected]
Sydney Water
Dammika Vitanage
Asset & Infrastructure Research Coordinator
[email protected]
David Zhang
Strategist Servicing & Asset Strategy Liveable City Solutions
Sydney Water
[email protected]
Using data analytics to priotorise high risk cricial pipe failure
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