Creating a Likelihood and Consequence Model to Analyse Rising

Main Bursts

Robert Spivey and Sivaraj Valappil

Thames Water Utilities Ltd, Innovation Department, Reading, 2017, U.K.

Keywords: Geographical Information System (GIS), Risk Analysis, Spatial Analysis, Spatial Modelling, Data

Interpretation, Data Visualisation.

Abstract: A model was created that analysed the likelihood and consequence of a sewage rising main bursting at any

given time. Likelihood of failure was analysed through factor analysis using GIS data and historical rising

main bursts data. Consequence was analysed through spatial analysis on GIS using multiple spatial joins,

property density and a cost of tankering model that was created using data from GIS. This analysis created a

likelihood and consequence score for each section of rising main to then create a combined overall risk

score. These outputs were then used to develop a rising main planning tool in the data presentation

programme Tableau to identify the high risk sites and target asset maintenance and rehab works. This paper

will explain how the tool was created and the benefits of the final outputs.

1 INTRODUCTION

The waste water network is made up of various

types of sewer pipes. One of these pipes is known

as a rising main.

Figure 1: Rising main diagram (Sharkawi farm, 1999).

As shown from figure 1, gravity sewers take

sewage from houses and connect them to a

pumping station. This is then pumped up a rising

main to the start of another length of gravity sewer.

This process is then continued until the pipe

reaches a sewage treatment works. Due to the

increased pressure from the pumping station there

is a risk that the rising main can burst.

Previously, 3 rising mains models have been

created by the company:

 2002/03 Spreadsheet Risk Model

 2007/08 Probability of Failure x Rolling

Ball model

 20012/13 Updated Probability of Failure

x Rolling Ball model

The most recent model is different to previous

models because it has split the rising mains into

smaller sections and observed other burst factors

such as rising mains located under rail/roads, ‘soft’

land or ‘urban’ land to attempt to identify

additional factors that could affect the likelihood of

bursting. It also improves the consequence aspect

of the risk model whereas previous models had less

robust consequence models.

A sewage rising main bursting can cause a

serious issue for the company. This is due to the

cost of repair, cost of tankering and pollution and

flooding fines. To address this issue investment is

made into regular replacement of rising main

pipes. To identify which areas need the greatest

investment a model was created to identify the

areas of rising main that pose the largest risk.

Company datasets relating to the sewer network

were regularly used throughout this project.

Spivey, R. and Valappil, S.

Creating a Likelihood and Consequence Model to Analyse Rising Main Bursts.

DOI: 10.5220/0006669601670172

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 167-172

ISBN: 978-989-758-294-3

167

2 CREATING THE MODEL

2.1 Likelihood

To begin creating this model, historical burst data

was obtained which contained a list of every

recorded burst since 1994.

Burst data is updated regularly every time new

bursts are recorded. Bursts are recorded by an

eastings and northings coordinate system in a

simple Excel spreadsheet. This is then plotted into

GIS using the display X/Y feature. When each new

burst point is plotted it is then saved as a shape file

then spatially joined to sections of rising main.

Based on the distance between the new burst points

and the sections of rising main we can work out

which section the new bursts is referring to.

Figure 2: Diagram of burst identification issue.

As shown from figure 2, allocating a burst to a

specific section of rising can prove difficult at

times as burst coordinates do not always match up

exactly with sections of rising main. This can lead

to a burst not being added to the model as the data

is not specific enough be sure which section of

rising main has burst. However, this is only the

case for a small percentage of the burst data.

Figure 3: Burst map (Thames Water Utilities Ltd, 2017).

Figure 3 above shows the map of bursts from

1994 up until 2017 across the Thames Valley area.

Once we know what section of rising main that

burst we can add this to the overall burst database

which the model is based upon. This data enabled

us to identify certain factors that contributed to a

rising main bursting. These factors included:

1. Material

2. Age

3. Ground type

4. Diameter

5. Soil corrosivity

Information regarding material, age, ground

type and diameter were accessible through the

company records however soil corrosivity was

identified from using the soil map from Cranfield

University to show which areas of land are most

corrosive.

Below shows the breakdowns of each factor

based on the length in kilometres. Using historical

burst data each category was given a burst rate of

number of bursts per kilometre of pipe.

Material

 Plastic

 Iron

 Concrete

 Other

 Unknown

Diameter

 Small (225mm and below)

 Medium (226-600mm)

 Large (above 600m)

 Unknown

Age

 1900 or earlier

 1901-1959

 1960 or later

 Unknown

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Ground Type

 Traffic (rail or road)

 Soft land

 Urban land

Soil Corrosivity

 0 (Very low corrosivity)

 1 (Low corrosivity)

 2 (Low-medium corrosivity)

 3 (Medium corrosivity)

 4 (High corrosivity)

 6 (Very high corrosivity)

Figure 4: Soil corrosivity map (Cranfield University,

2017).

Figure 4 shows the areas of land that are most

corrosive within the Thames Valley area. For the

purposes of the model water was assumed to have

a soil corrosivity of 0. GIS was used to create this

map by adding soil data to GIS then colour coding

based on soil corrosivity score.

Each category of burst was then given a burst

rate score then matched up with the sections of

rising main associated with each category. Some

information for the rising mains is unknown due to

a lack of information in some of the company

records. Any category that had an unknown factor

was taken out of final outputs as it is not an

accurate measure. The category with the greatest

bursts per km was other material, medium

diameter, 1900 or earlier, soft ground type and soil

corrosivity 6.

Figure 5: Tableau table of burst rate categories.

The table above shows the top categories of

bursts per km.

2.2 Consequence

Consequence was then added to the model through

3 factors including:

1. Distance to specific locations

2. Property density

3. Tankering cost

Specific locations were identified by the

consequence to the business and society of

flooding. Distances considered include:

 Hospitals

 Schools

 Roads (Motorways, A-Roads and B-

Roads)

 Water

 Sites of Special Scientific Interest

(SSSI’s)

 Bio habitats

 Underground Stations

 Railways

The distances to each of these points of interest

were analysed through spatial joins in GIS by

combining shape files of rising main locations and

spatial locations of all the areas listed above. Shape

files of all these points of interest were created by

obtaining easting and northing positions for each

location and importing this data into GIS from

Excel spreadsheets using these easting and

northing positions.

Creating a Likelihood and Consequence Model to Analyse Rising Main Bursts

169

Figure 6: Rising main map (Thames Water Utilities Ltd,

2017).

Figure 6 shows a map of rising mains and their

location across the Thames Valley area. To analyse

distances to various points of interest other spatial

data is added to the map then spatially joined from

the rising main data. For example Figure 7 shows

the rising main data combined with motorway data

across the Thames Valley area.

Figure 7: Map of rising mains and motorways (Thames

Water Utilities Ltd, 2017).

After the distance to each of these points of

interest had been analysed for each section of

rising main they were combined into an overall

distance ratio by taking an average of all the

distances. This allows the model to take into

account sections of rising main that are close to

more than one point of interest rather than just how

close it is to an individual location.

After spatial distance data had been analysed

we then looked at the property density that each

section of rising main falls into. To add this we

combined a square kilometre grid across the whole

of the Thames Valley area with property data. This

allowed us to create a count of properties per each

square kilometre. Rising main location data was

then added to this grid count to analyse which

property grid square each section of rising main

was in. This allowed us to allocate a number of

properties per section of rising main.

Figure 8: Property density heat map.

Figure 8 shows a heat map of property density

across the Thames Valley area. The colour scale

ranges from green to red with red being highest

number of properties. As expected the highest

number of properties are located in and around the

London area.

After property density had been considered we

added tankering cost to the consequence model.

Tankering is the process of providing tankers to the

location of the burst in order for the waste water to

fill into the tankers rather than flood across the

burst area.

In order to add this, a separate model was

created to analyse tankering cost. This model was

created by combining 5 separate factors to create

a tankering cost per section of rising main. These

factors include:

1. Distance from pumping stations to tanker

depots

2. Distance from pumping stations to sewage

treatment works

3. Flow data in the rising mains

4. Diameter of rising main

5. Length of rising main

Flow data, diameter and length were accessible

through company records however the distances

were created by spatially joining locations of

pumping stations to tanker depots and sewage

treatment works in GIS.

At this point in the model intervention data was

added to the outputs. Intervention data is data

regarding what lengths of rising main have been

recently replaced. This is then removed from the

outputs as it is assumed that if the pipe has recently

been replaced then it reduces the risk of bursting

again.

The 3 consequence factors were then combined

to create an overall consequence of failure score

for each section of rising main. Likelihood and

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consequence scores were rated between 0 and 10

with 10 being the highest and 0 the lowest. By

taking the average of these scores an overall risk

score was created in order to rank each section of

rising main on its risk priority.

Figure 9: Likelihood consequence plot.

Figure 9 shows the overall plot of the

likelihood and consequence scores for each section

of rising main. To identify the top sites that need

attention the top 10 sections of rising main with the

highest risk score were observed.

When observing the highest risk sections we

observed sites that have a consequence score over

5.5 and a likelihood score of over 5. The sum of

the length of rising main that were incorporated in

this category came to 7km. Hence, if 7km of rising

main were replaced it would remove all of the high

risk sections from the model. 7km may seem like a

large amount of pipe however the overall length of

rising main that was incorporated in the model is

2109km. Therefore, only 0.33% falls in the high

risk area of this risk plot.

Figure 10: Map of high risk sites.

Figure 10 shows the locations of the sites that

based on the model created have the greatest risk

score.

3 PLANNING TOOL

After this model had been created it was then

adapted into a user friendly planning tool. This was

created within the data visualisation programme

Tableau. Tableau was chosen for this planning tool

as it allows spatial and other data files to be

combined into one, user-friendly, interactive

dashboard. The planning tool contains data relating

to each section of rising main. For example, the

region that the rising main falls within and the

contact details of the operational staff member

responsible for the rising main section. The region

was identified through spatially joining the rising

main file to the operational regional boundary file

in GIS. This is very useful as if there is an issue

with a certain section of rising main the member of

staff responsible can be quickly contacted in order

to resolve this issue.

The planning tool will be used by many

members of staff across the business. Therefore,

the planning tool will need to be user friendly in

order for staff members from a non-analytical

background to use it effectively. This is achieved

through easy access information dashboards that

can be filtered through drop down menus relevant

to the maps or graphs. Updating the model is also

extremely user friendly. New bursts data is added

to the original burst spreadsheet and Tableau will

update all of the models and dashboards based on

this new data. This allows for the model to stay

updated therefore reducing the need for a new

model to be created when the current data set is

outdated. The planning tool will be distributed

across the business in the form of a packaged

workbook file in Tableau reader. This allows

access for all staff across the business without

them being able to edit the original file. Due to this

only one Tableau server license is needed to share

this tool with the rest of the business.

4 CONCLUSIONS

To conclude, this paper has shown how GIS spatial

analysis and modelling is used by the water

industry to analyse the impact of a rising main

bursting. This model will provide a direction for

rising main replacement investment. It allows the

business to efficiently replace the minimum

amount of rising main pipe based on how

detrimental a burst would be in that section,

therefore maximising the operational cost saving.

Creating a Likelihood and Consequence Model to Analyse Rising Main Bursts

171

Without the use of the tools within GIS this model

would have been a lot more difficult to create.

Simple tools on GIS such as spatial joining were

influential in the making of this model. The

outputs from this project include a list of all rising

main sections with its associated risk score and a

user friendly planning tool to be used across the

business.

This model has areas for improvement using

further applications in GIS and other programmes.

The model could be improved by adding in lidar

data to the consequence modelling in order to

analyse the heights of all the sites listed within the

distance factor of consequence. This will give a

better insight into the flow of the flooding out of a

burst rising main. For example, if a school is

downhill from a rising main burst it is more likely

to flood towards the school compared to if the

school was higher than the burst. This model will

be further improved by adding in a more detailed

likelihood model based on further analysis that

looks to identify which likelihood factors are

greater linked to a burst. This is likely to be

modelled within the statistical programme R using

logistical regression.

REFERENCES

Sharkawi farm, (1999), Rising main diagram (ONLINE).

Available at: http://www.sharkawifarm.com/lift/lift-

station-pump-wiring-diagram (Accessed 27

September 2017).

Cranfield University (2017) Soil corrosivity dataset,

(Accessed 5

September 2017).

Thames Water Utilities Ltd (2017) Sewer network

datasets (Accessed 10

August 2017).

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