a review of methods for leaked management in pipe networks

*Corresponding author. Tel.: ++37-2620-2552; fax: +37-2620-2550. E-mail address: [email protected] (R. Puust).

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A Review of Methods for Leakage Management in Pipe Networks

R. Puust a,*, Z. Kapelan

b, D.A. Savic

b, T. Koppel

a

a Tallinn University of Technology, Department of Mechanics, Ehitajate tee 5, Tallinn, 19086, Estonia; b University of Exeter, School of Engineering, Computing and Computer Science, Centre for Water Systems, Harrison Building, North Park Road, Exeter EX4 4QF, UK

Abstract

Leakage in water distribution systems is an important issue which is affecting water companies and their

customers worldwide. It is therefore no surprise that it has attracted a lot of attention by both practitioners

and researchers over the past years. Most of the leakage management related methods developed so far

can be broadly classified as follows: (1) leakage assessment methods which are focusing on quantifying the

amount of water lost; (2) leakage detection methods which are primarily concerned with the detection of

leakage hotspots and (3) leakage control models which are focused on the effective control of current and

future leakage levels. This paper provides a comprehensive review of the above methods with the objective

to identify the current state-of-the-art in the field and to then make recommendations for future work. The

review ends with the main conclusion that despite all the advancements made in the past, there is still a lot

of scope and need for further work, especially in area of real-time models for pipe networks which should

enable fusion of leakage detection, assessment and control methods.

Keywords: Distribution system; leakage assessment; leakage control; leakage detection; pipe network;

water distribution systems; leakage model, pressure-dependent leakage.

ManuelResaltado


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1. Introduction

Leakage occurs in all water distribution systems nowadays. As noted by William Hope

long time ago (1892), there is no water-supply in which some unnecessary waste does

not exist and there are few supplies, if any, in which the saving of a substantial

proportion of that waste would not bring pecuniary advantage to the Water Authority.

The amount of water leaked in water distribution systems varies widely between

different countries, regions and systems, from as low as 37% of distribution input in the

well maintained systems in Netherlands (Beuken et al., 2006) to 50+ % in some

undeveloped countries and less well maintained systems (Mamlook and Al-Jayyousi, 2003;

Lambert, 2002).

Leakage is not just an economical issue as it is often perceived and presented by

water companies but it is also an environmental, sustainability and potentially a health

and safety issue. As noted by Colombo and Karney (2002), leakages cause inefficient

energy distribution through the network (thus wasting energy used for pumping the

water) and, also, may affect water quality by introducing infection into water distribution

networks in low pressure conditions.

A number of past, review type papers exist in the field of leakage modelling and

management. One of the earliest review papers is the paper by Morris Jr. (1967) which

provided an overview of potential causal factors leading to water pipe breaks. A report

summarising different leakage control policies can be found in Goodwin (1980).


3

Comparisons of the key attributes of different leak detection methods are given by Cist

and Schutz (2001). Another review and classification of leakage detection methods is

reported by Liou et al. (2003). A review of calibration methods in water pipelines

(including leaks) can be also found in Kapelan (2002) and Savic et al. (2009).

Unlike the existing approaches mentioned above, which are focusing on a

particular leakage issue (usually leakage detection), this paper looks wider by considering

the overall leakage management process. The objective of this paper is to review the

methods and models developed in the past used in different phases of this process, from

becoming aware of the leak existence to controlling the level of leakage in the system. It

is hoped that this way the new promising research areas will be found as they often exist

along the boundaries of current research areas. More specifically, this review looks into

past methods and models developed that can be used to either assess, detect or control

leakage in distribution (and other) pipe systems. The main objective is to identify the

advantages and disadvantages of all existing approaches and to then use the observations

made to suggest possible future research work in the field.

The paper is laid out as follows. After this introduction, the relevant background

information is presented in section 2. This is followed by the review of leakage

assessment methods in section 3, detection methods in section 4 and leakage control

methods in section 5. Finally, the main conclusions are drawn in section 6 of the paper.

2. Background


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Different definitions of leakage in distribution systems exist. The most frequently used

one defines the leakage as (amount of) water which escapes from the pipe network by

means other than through a controlled action (Ofwat, 2008). Water leakage in distribution

systems is typically classified into background and burst related leakage (ODay, 1982).

Bursts (i.e. main breaks) represent structural pipe failures and background leaks represent

the water escaping through inadequate joints, cracks, etc. Leaks can also exist in service

reservoirs and tanks.

Leakage in distribution systems can be caused by a number of different factors.

Some examples include bad pipe connections, internal or external pipe corrosion or

mechanical damage caused by excessive pipe load (e.g. by traffic on the road above or by

a third party working in the system). Other common factors that influence leakages are

ground movement, high system pressure, damage due to excavation, pipe age, winter

temperature, defects in pipes, ground conditions and poor quality of workmanship.

Therefore, the presence of leakage may damage the infrastructure and cause third party

damage, water and financial losses, energy losses and health risks.

Leakage is dependent on system pressure. Basically, the higher the pressure, the

larger the leak flow and vice versa. Initially, an orifice type equation (Wiggert, 1968) was

used to describe this relationship. Although the orifice equation is still widely used in

many research studies, the user must be aware that the equation can lead to misleading

results when the pipe in question is not made of a rigid material (Greyvenstein and van Zyl,


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2005) or when the pressure is negative (Todini, 2003). Lately, a more generalised leak

flow pressure equation has been adopted which allows specifying leakage exponent

different from 0.5 (Germanopoulos, 1985). It has been shown that the value of this

exponent depends on the type of leak, pipe material behaviour, soil hydraulics and water

demand (Cassa et al., 2005; Greyvenstein and Van Zyl, 2005; Walski et al., 2006; Noack and

Ulanicki, 2006). For example Van Zyl and Clayton (2005) note that when leakage is

analysed as pressure dependent, demand should follow the same procedure. More on

leakage as a hole in a pipe and its characteristics can be found in Beck et al., (2005a, b) and

Coetzer et al. (2006). Various studies about the pressure dependent leakage modelling can

be found from the literature. Modelling based on leak discharge coefficient and leak area

can be found from the articles by May (1994); Vela et al. (1995); Simpson and Vitkovsk

(1997); Vitkovsk and Simpson (1997); Dunlop (1999); Hernandez et al. (1999); Stathis

and Loganathan (1999); Alonso et al. (2000); Rossman (2000); Ulanicki et al. (2000);

Ulanicka et al. (2001); Vitkovsk et al. (2003a) and Verde (2005). Modelling that also

included pipe characteristics can be found from Germanopoulos (1985; 1995);

Vairavamoorthy and Lumbers (1998); Martinez et al. (1999); Reis and Chaudry (1999);

Tucciarelli et al. (1999); Ainola et al. (2000) and Dias et al. (2005).

3. Leakage Assessment Methods


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The objective of leakage assessment (i.e. water audit) is to estimate the quantity of water

lost in the system analysed without worrying where the leaks are actually located. The

assessment methods developed so far can be broadly classified into the following two

main groups: (a) top-down leakage assessment methods and (b) bottom-up leakage

assessment methods.

3.1 Top-down approaches

The objective of top-down leakage assessment approaches is to estimate the leakage in a

particular system by evaluating different components of the overall water balance,

primarily the water consumed for different purposes. The two main approaches used are

the IWA approach (Lambert and Hirner, 2000) and the approach used by the OFWAT in

the UK. Although quite similar, there are some differences between the two approaches

due to slightly different terminology and definitions used for some water balance

components.

More information about the general leakage assessment can be found from

Stenberg (1982), Thornton (2002), Farley and Trow (2003), and Scott and Barrufet (2003). The

latest reports about average losses in the UK (based on areas operated by different water

service companies) can be found in (AHL, 2006). The latest guidance notes on leak

location and repair are published in Pilcher (2003) and Pilcher et al. (2007).


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Despite the simplicity of a top-down type leak assessment, the leakage estimate

obtained via this method is referred to as a crude estimate. Gathering such information

helps to decide what the next step in leakage studies should be for a particular network

but it does not help to bound potential leak areas, let alone locate leaks.

3.2. Bottom-up approaches

Bottom-up type leakage assessment can be considered the second part of the audit

process. This procedure is implemented when the company has confirmed the data used

in the top-down portion. It includes every area of the companys operation: billing

records, distribution system, accounting principles etc. The audits main purpose is to

find out the efficiency of the water distribution system and the measures needed to

achieve these. Bottom-up audits require the most accurate and up-to-date data possible.

Bottom-up real loss assessment can be carried out in two different ways: (a) 24

Hour Zone Measurement (HZM) or (b) Minimum Night Flow (MNF) analysis. HZM

needs a temporary isolated area of the distribution network that is supplied from one or

two inflow points only. In these areas, 24 hour inflow measurements shall always be

logged along with pressure measurements. MNF in urban situations normally occurs

during the early morning period, usually between 02:00 and 04:00 hours (Liemberger and

Farley, 2004). The estimation of the real loss component is carried out by subtracting

legitimate night uses from the MNF. To get a satisfactory estimate of the daily leakage,

Stenberg (1982) has found that night leakage flow rates should then be multiplied by 20


8

hours. This assumption does not take into account that pressure is not constant over a

period of time. Therefore Lambert (2001) suggests using a method called fixed and

variable area discharges (FAVAD). This method uses the following equation:

Hkq = (1)

Where q = volume rate per unit length; ( )

gbCk d 2= ; = leakage exponent; dC =

discharge coefficient; b = width of the slot; g = gravitational acceleration; H = pressure

head. Leak exponents vary, being close to 0.5 with fixed area leakage path (hole in pipe)

and 1.5 with variable leakage path (crack in pipe). The increase or decrease of real losses

due to a change in pressure can then be computed by FAVAD concept as:

( )2121 // HHLL = (2)

where L1 and L2 are leakage rates and H1, H2 are pressure heads at respective times.

Thereafter leakage can be simulated over the full 24h period (see Figure 1).

At the end of the real loss assessment process, the advantage of the combined top-

down, bottom-up and component analysis (Table 1) that were introduced in the early

1990s (Farley and Trow, 2003) becomes obvious. Several countries have had their own

measures or indicators. For example the sample measures could be: percentage of average


9

daily flow (USA, France); m3/km of mains/hours (German, Japan); litres/property/hour

(UK) and litres/service connection/hour. The problem is that those indicators do not take

account of component analysis techniques therefore additional performance measures

have to be used. Comparison of some additional performance measures can be found in

Tuhovk et al. (2005). Performance indicators should count the possibility of consumption

decreases seasonally or annually (non-revenue water does not) and also take into account

pressure relations in the pressure zone. Therefore recommended indicators should always

indicate its robustness. Robustness can be defined with a level and a function (Table 2).

The most commonly used leak index nowadays is Infrastructure Leakage Index - ILI

(Lambert, 2003; Farley and Trow, 2003). The advantages of using ILI are that it can be

consistently applied across a range of utilities and that it is a measure of what can be

achieved given the condition of the infrastructure. Its key disadvantage is that it is not

easily understood by non-technical readers. Additionally it does not take into account the

relative costs of leakage management (and other marginal costs, like environmental costs)

and it is not able to define what level of reduction is economically feasible. An additional

advantage of calculating an ILI index is that it can be used to calculate the leakage

exponent (Thornton and Lambert, 2005):

( )100

/65.015.1p

ILI = (3)


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where p = the percentage of rigid pipes in network.

Using Eq.(3) the calculation for different leakage exponents for different

networks/countries/regions can be found (see Table 3).

Although classical minimum night flow analysis (Araujo et al., 2003a; Covas et al.,

2006a; Garcia et al., 2006) can reduce real losses (leakage) considerably, there are many

other methods of leak assessments that could possibly be used depending on network

architecture (see Puust, 2007). In addition to water audits the assessment can be done also

using some statistical analysis for detecting the magnitude of leaks (Buchberger and

Nadimpalli, 2004). This is expected to be more accurate but with a cost of a need of

continuous, high resolution measurements of discharge at one or more locations within

the district metering area (DMA). This can be problematic in some cases because the

high resolution data measurements are not used very often within a DMA and the

location of data acquisition systems must be carefully planned in such case studies

(Vitkovsk, et al. 2003c; Kapelan et al. 2003c, 2005; Behzadian et al. 2009).

4. Leakage Detection Methods

Historically, leakage assessment studies have been carried out to quantify total losses

including, if possible, real and apparent losses. This was followed by the development of

leakage detection methods with the aim to detect and locate leaks. Although some

leakage detection methods have been around for years, because of constant development,


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they are getting increasingly high tech and sophisticated than ever before. Still, regardless

of whether the methods are equipment or non-equipment based, it is common practice to

use some leak detection method in conjunction with other methods.

4.1. Leakage awareness methods

The term leak awareness is used to explain the discovery of a leak in a particular area

within the network. It does not give any information about its precise location. Usually a

hydraulic model is needed for the leakage awareness test. Various hydraulic models have

been proposed to detect leaks in water distribution systems. Those methods usually

involve calibration/optimisation techniques to analyse the different areas of the network.

The problem is formulated as a constrained optimisation problem of weighted least-

square type to minimise the objective function E:

( ) ( ) ( )===

++=N

k

i

m

ip

Q

i

i

m

iq

P

i

i

m

ih ppwqqwhhwE1

2

1

2

1

2

(4)

where P and Q are the number of pressure, flow measurement respectively, m

ih is the

measured head at node i, ih is the computed head at node i , m

iq is the measured flow at

pipe i, iq is the computed flow at pipe i, m

ip is the prior estimate (pseudo measurement),

ip is the prior estimate and N is the number of prior estimate, w is a weight factor for


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pressure/flow and prior estimate part. Prior estimates were introduced into the

minimisation problem by Kapelan (Kapelan, 2002; Kapelan et al., 2000, 2003a, 2003b, 2004)

to avoid the ill-posed problem (that is there is no solution, no unique solution or the

solution is unstable), to improve the accuracy of the estimated calibration parameters and

to increase the speed of the convergence process. It should be noted that prior estimates

work better with pipe friction factors as these are less sensitive than leak effective areas.

The minimisation of Eq. (4) gives the solution to an inverse problem (Pudar and

Liggett, 1992; Stathis and Loganathan, 1999). Various minimisation algorithms have been

used to minimise the objective function, Eq. (4). When steady state regime is used, both

pressure and flow measurements can be used. In a transient flow regime flow

measurements are difficult to use because most flow meters do not react instantaneously

to a change in flow (Chen, 1995). Early adoptions of fluid transients for leak detection can

be found from Wiggert (1968), Nicholas (1990), Liggett (1993), Liggett and Chen (1994).

The use of fluid transients for leak detection has gained popularity over the last

decade as a massive amount of data can be gathered in a very short period of time

therefore ensuring that the inverse problem will always be overdetermined. Another good

advantage over steady state calculation is that pressure waves are less affected by friction

than the general flow and thus the precise friction values become less important to the

calculation. Therefore, using transients, the leak detection and calibration (friction

factors) can be done simultaneously, thus providing a solution to the problem of unknown

or poorly known friction. Fluid transients are used to probe the pipeline in much the same


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way as radar and sonar are applied to locate and identify objects. The reason why

methods based on transients are mainly used on single, grounded pipelines is that an

uncertainty of the system does affect the results considerably (pressure wave reflections

from each feature of the pipe). For undergrounded pipes the systems architecture can be

hardly followed thus its applicability in such situations is still questionable.

A number of hydraulic transient-based techniques for detecting and locating

existing leaks are described in the literature: leak reflection method (LRM) (Jnsson, 1995,

2003; Brunone, 1999, Brunone and Ferrante, 1999, 2001, 2004; Covas and Ramos, 1999),

inverse transient analysis (ITA) (Liggett and Chen, 1994; Liou, 1994; Vitkovsk et al., 2000;

Kapelan et al., 2003a, 2003b; Covas et al., 2001a, 2003, 2005b; Covas and Ramos, 2001b;

Stephens, et al., 2004; Wang et al., 2006; Soares et al., 2007), impulse response analysis

(IRA) (Liou, 1998; Vitkovsk et al., 2003b; Kim, 2005), transient damping method (TDM)

(Wang et al., 2002, 2003), frequency domain response analysis (FRM) (Mpesha et al., 2001,

2002; Stoianov et al., 2001; Ferrante and Brunone, 2001a, 2001b, 2003a, 2003b; Covas et al.,

2005a; Ferrante et al., 2005; Lee et al., 2003; 2005a; 2005b, 2006; Zecchin et al., 2005, 2006).

The main objective of all transient leak detection methods is the same to extract

information about the presence of a leak from the measured transient trace. A transient

event is generated either by system elements (i.e. inline valves and pumps) or special

devices (for example solenoid side discharge valves).

In the leak reflection method (LRM), a transient wave is travelling along a

pipeline and it is partially reflected at the leak. The location of the leak can be then


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identified from the measured pressure trace (Figure 2). The magnitude from the leak

depends on the ratio between the size of the generated transient wave and the size of the

leak orifice. LRM methods are so far used only in single pipe case studies in laboratory

conditions.

The inverse transient analysis method (ITA) was first introduced by Liggett and

Chen (1994). The ITA uses least-squares regression between modelled and measured

transient pressure traces. The leak is usually modelled at network nodes and the

minimisation of the deviation between the measured and calculated pressures produces a

solution of leak location and size (Figure 3). The ITA method is a well-researched topic

but since its introduction, the main effort has been focused on the development of the

mathematical part of the technique and not on experimental validation or field testing.

Some limited experiences from laboratory and fields tests can be found from Vitkovsk et

al., (2001), Stephens et al., (2004), Covas et al. (2005b) and Saldarriaga et al. (2006). As with

LRM, the tests are made on single pipeline rather than on a network. Application

difficulties lie in the fact that ITA needs an accurate modelling of the transients and

boundary conditions of the pipe system. To address the latter, a greater emphasis should

be directed toward analysis of errors and strategies to deal with the uncertainties in

general (Vitkovsk et al., 2007). Model error is the most likely limiting factor in successful

field application of ITA and its results should never be presented without quantification

of their uncertainty.


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The impulse response analysis (IRA) is based on the fact that the transient

propagation along the pipeline is affected by the friction of the pipe wall and other loss

elements such as leaks. This effect results in damping of the transient wave. A leak can

be detected when a transient damping in the same pipeline is compared with and without

a leak (Figure 4). The lack of information about tests in real pipeline systems where noisy

data would be used makes it a less important method when compared with LRM and

ITA. It has one advantage when the comparison should be made with TDM or FRM.

Namely, in IRA no discretization of the pipeline is needed and the shape of the generated

transient is not important.

In the transient damping method (TDM) it is analytically derived that friction

related transient damping in a pipeline without a leak is exactly exponential and the

corresponding damping in a pipeline containing a leak is approximately exponential

(Wang et al., 2002). The rate of the leak-induced damping depends on leak characteristics,

the pressure in the pipe, the location of the transient generation point and the shape of the

generated transient. Tests on a laboratory pipeline showed successful leak detection

(Figure 5) but in a real situation, friction is not the only cause of transient damping.

Transient damping can be caused by other physical elements like joints, connections, fire

hydrants and pipe wall deterioration products. The modelling of these elements can be

complicated and in some cases even impossible. Therefore it may be difficult to estimate

the leak-free damping for a real pipeline.


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The frequency response method (FRM) uses the analysis of transient response in

the frequency domain. Fourier transforms are used to transform time-domain data into the

frequency domain. Leak location can be obtained when the dominant frequencies of no-

leak and leaking pipelines are compared (Figures 6, 7). Performance of the method is

strongly influenced by the shape of the transient and the measurement location. As with

other methods based on transients, only pipeline applications of frequency response

analysis are presented in the literature. Some of the case studies that are based on

pressure transients are summarised in Table 4.

There are many other leak awareness methods but only three of them have been

applied to pipe networks (Saldarriaga et al., 2006; Deagle et al., 2007; Wu and Sage, 2007). It

should be noted that most of them are very rarely used and/or do not have any practical

tests made to support the idea. One of the reasons why these model based techniques are

not so widely used could be because of the low flow rates in pipelines that eliminate the

possible use of commonly used pressure measurement devices that are cheap and easily

manageable, but not effective when used in low flow conditions.

When leak awareness methods are under discussion it should be also mentioned

that very few of them are probabilistic ones. In that respect, the Bayesian system

identification methodology has been used by Poulakis et al. (2003), Rougier (2005) and

Puust et al. (2006). The main reason to use a Bayesian interface in leakage studies is that

normally we are dealing with different kinds of errors that cannot always be included in

calculations. Therefore to make more sense, the final discrete value is bounded with a


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certain probability that gives us more information about the result reliability. The

drawback is that usually such procedures need a great deal of computer power for

calculations. In general, probabilistic models are used also to conduct criticality analysis,

where small cracks in pipes might cause the overall failure of much larger systems (like

cooling systems in nuclear power plants, Rahman et al., 1997).

Great effort has been made so far in the development of model based leak

detection methodologies. Whilst this development will continue, it is obvious that some

methods will be suitable for application to simple systems only (e.g. single pipelines). An

example of this is transient based methodologies. Because of their limitations when

applied to network systems it is clear that development of transient based methodologies

for leakage control will be limited to single pipelines. For a general reference about

leakage control please see section 5.

4.2 Leakage localisation methods

Leak localising is an activity that identifies and prioritises the areas of leakage to make

pinpointing of leaks easier. Some methods/techniques that belong to this group are:

acoustic logging (Moyer, 1983; Hough, 1988; Rajtar and Muthiah, 1997; Hessel et al., 1999;

Hunaidi and Chu, 1999; Miller et al., 1999; Lockwood, 2003; Shimanskiy, 2003; Bracken and

Hunaidi, 2005; Muggleton et al., 2006), step-testing (Farley and Trow, 2003, Pilcher et al.,

2007), ground motion sensors and ground penetrating radars (Hunaidi, 1998; Lockwood et

al., 2003; O'Brien et al., 2003).


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The most well-known and effective leak localising method is step-testing and it

has been used by several water utility companies for quite some time. Step-testing is an

activity whereby the area is subdivided by the systematic closing of valves during the

period of minimum night flow. Depending on the methodology used, step-testing may

cause backsiphonage, the risk of infiltration of ground water and some parts of the

network can be without water for a period of time. Not all networks are planned with the

possibility of future step-testing in mind and therefore it may be difficult to apply.

Because of a need of careful planning, night work involving the step-testing has been

replaced by acoustic logging during the 1990s (Pilcher et al., 2007).

Acoustic logging (AL) is performed using vibration sensors or hydrophones,

which are temporarily or permanently attached to the pipe fittings. The distance between

each other typically varies between 200 to 500 m. As with step-testing the data is

collected at night times, usually between 2 and 4 am. Downloaded data will then be

analysed statistically for detection of leak signals (Figure 8). Although a wide area may

be covered quickly, for a successful leak detection good skill is required. The fact that

quiet leaks may not be heard and the background noise cant be ignored makes it difficult

to apply in certain situations.

The application of ground penetrating radar (GPR) for leak location has been

given a lot of attention during the last few years (Farley, 2008). Ground penetrating radar

inspection is a non-destructive geophysical method that produces a continuous cross-

sectional profile or record of subsurface features (Figure 9). Methods like this could be


19

used to locate leaks in water pipes by detecting either underground voids created by

leaking water as it circulates near the pipe or by detecting anomalies in the pipe depth as

measured by radar. GPR has evolved for some years now. It has been previously

described as a time consuming methodology but recent studies show that along

transmission main routes it can be carried out at 15 30 km per hour, depending on

location and traffic. As GPR technology is similar in principle to seismic and ultrasound

techniques, the main disadvantage comes from the fact that anomalies like metal objects

in the ground can lead to false conclusions and it might not be applicable in cold climates.

Some developed GPR technologies have a penetration capability of up to 2 meters into

the ground. For example in northern European countries the water pipe bottom should be

laid down in some occasions at least 1.8m deep to avoid water freezing. Therefore GPR

technology can not give trustworthy results on those extreme occasions and in situations

where main pipes are excavated even deeper into the ground. It should be still noted that

this methodology is a good alternative in situations when large diameter or non-metallic

pipes need monitoring.

In summary, leakage localisation methods can be used on their own or

before/following the application of some other method. For example, if a hydraulic model

of the analysed system is available then some numerical (i.e., inexpensive) leak detection

method may be used before the leak localisation method, to narrow the area searched for

the leak. However, if the hydraulic model is not available (or not updated regularly) then

a leak localisation method could be used on its own.


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In general district audits are labour-intensive and costly, since they are performed

at night. A more recent trend is that permanent flow meters are installed that are

connected telemetrically to a supervisory control and data acquisition ( SCADA) system.

The transmitted flow rate data are automatically analysed to detect unusual increases in

flow patterns (Mounce et al., 2009). Based on experience with water system, the increase

in flow rate can be explained by leakage or not. District audits and step testing help

identify areas of the distribution system that have excessive leakage. No information

about the exact location of leaks is given. When step-testing or SCADA system is not

available, some other technology is needed that can be used for leak localisation with a

reasonable time. The reasonable time to detect leakage varies depending on the leak flow

rate. Small leaks are more difficult to locate, especially when using acoustic logging for

plastic pipes. As a consequence, the GPR technology was developed (any pipe material

can be surveyed) and various studies published demonstrate promising performance. GPR

is probably one of the key technologies studied in Europe currently (WATERPIPE,

2009).

4.3. Leakage pinpointing methods

Leakage pinpointing methods include methodologies that are the most accurate in today's

leak detection surveys. Three main groups described here are based on (a) leak noise

correlators (Grunwell and Ratcliffe, 1981; Cascetta and Vigo, 1992; Gao et al., 2004, 2005,

2006; Hunaidi et al., 2004; Muggleton et al., 2004, Muggleton and Brennan, 2004, 2005); (b)


21

gas injection (Field and Ratcliffe, 1978, Hunaidi et al., 2000; Farley and Trow, 2003); and (c)

pig-mounted acoustic sensing (EPSRC, 2002; McNulty, 2001; Mergelas and Henrich, 2005; ).

The historical appearance of leakage pinpointing methodologies is given in Figure 10.

Leak noise correlators (LNC) are the most common technique for leak location

that was first introduced commercially into the marketplace in the late 1970s (Thornton,

2002). Their technology has been improved over the last few years quite considerably.

The Water Research Centre (WRC) in England was one of the leading research

institutions to apply the methodology onto real pipelines. To correlate the sound from a

leak, two microphones are located in contact with the pipe or valve stems at the same

time, with one microphone on each side of the leak (Grunwell and Ratcliffe, 1981; Stenberg,

1982). The sound is compared in the correlator, which is capable of determining the

difference in time for sound to reach the correlator. Knowing the speed of sound in the

pipe, it is then easy to calculate the distance to the leak, which will be independent of the

geophone, traffic noise, etc. For accurate leak localisation the pipe system should be

known precisely as a leak correlating in a branched section tend to show a leak on a tee

and not at its exact location. Such misleading information affects mainly excavation costs

and man-hours needed for a repair. The latest versions of leak noise correlators can

accurately locate a leak to within 1 metre in most pipe sizes. The distance between the

sensors can be as high as 3000 m but it depends highly on pipe material. For plastic pipes

this methodology is quite questionable as distance between the sensors should be quite

small 15 to 100 m, making this method very slow. The method works best with clean,


22

small diameter metallic pipes in high water pressure areas where hard pipe backfill is

used.

In a tracer gas technique (TGT), a non-toxic, water-insoluble and lighter-than-air

gas, such as helium or hydrogen, is injected into an isolated section of a water pipe. This

is followed by ground scanning with a highly sensitive gas detector which should identify

any traces of escaped gas from the leak point(s) (Figure 11). Although this method is

widely used for machinery testing, it is normally prohibitive for leak detection because of

the high cost. Its effectiveness comes from aspects that through TGT multiple leak

locations can be found in a single pipe section or at a branched pipe systems where noise

correlation techniques usually fails or gives misleading results. The main disadvantage in

addition to high costs are that the gas could be trapped near the ceiling of water-filled

pipes and thus could not escape if leaks were not near the top of the pipe.

The pipe pig-mounted acoustic (PMA) technique has also been used for leak

detection (EPSRC, 2002; McNulty, 2001). This technique requires the insertion of a

microphone (or a pair of microphones) under pressure into the main. The velocity of

water carries the microphone to the leak position whereas the noise and its position are

continuously recorded. Some latest technology examples can be found from Chastain-

Howley, (2005) and Fletcher, (2008). Inline pigs are used to carry different kinds of

sophisticated measuring devices such as magnetic flux leakage (Mukhopadahyay and

Srivastava, 2000), hydroscopes (Makar and Chagon, 1999) or ultrasonic tools (Willems and

Barbian, 1998) along the pipeline. In general these tools need clean pipes and therefore it


23

is difficult to apply this methodology to old pipes where there may be heavy corrosion.

Access to the inside of the pipeline is also needed. Attention must be paid to the fact that

as pigs are in contact with the pipe inner wall their effect on the water quality must be

considered before a survey is performed.

Leak pinpointing techniques are the most precise technologies currently available

for leak detection. It should be remembered that such a precision comes with very high

costs in terms of equipment owning or renting and man-hours needed for surveys to be

carried out. Considering this and the length of time needed for implementation, it is

recommended to use leak pinpointing techniques in conjunction with some leakage

awareness or localisation method. Table 5 brings out some general guidance when leak

localisation or pin-pointing technique should be chosen.

5. Leakage Control Models

Leakage control models can be generally classified into the following two main groups:

(a) passive (reactive) leakage control and (b) active leakage control. A passive leakage

control is a policy of responding only to leaks and bursts reported by the public (in some

cases also by a company's own staff). Active leakage control concerns management

policies and processes used to locate and repair unreported leaks from the water company

supply system and customer supply pipes.


24

Many water utilities still take a passive attitude of waiting for a problem to arise

and repairing it only when leaks become self-evident. For example, the appearance of

water on the ground surface following pipe failure is visually detected by the staff or

reported by customers. Manual location techniques are then used to identify the actual

location of the failure. This presents inevitable problems for customers (Ramos et al.,

2001). Passive policy is very straightforward and simple to use but it does not involve any

systematic action. Therefore this kind of acting is reasonable only in such water systems

where there are very low leakage levels, the average loss is constantly below 10 15%.

Even in low loss cases it is advisable to use some more advanced technology at the same

time (like SCADA system) as when using passive policy the overall loss can easily raise

to 40%.

Active leakage policy involves the techniques like: active leakage control and

active pressure management. There are also sectorisation and economic intervention but

those are not discussed here. The most appropriate leakage control policy will mainly be

dictated by the characteristics of the network and local conditions, which may include

financial constraints on equipment and other resources (Farley and Trow, 2003). The final

choice of the method is also based on economic considerations. Term economically

viable can be defined with an economic curve of leakage (ELL) analysis that is described

in Figure 12.

The most widely used active leakage control methodology on single pipelines is

based on pressure transients (Misiunas et al., 2003, 2005a, 2005b, 2006). The lack of their


25

commercial availability gives them more attention from the research side and any system-

wide conclusions are hard to be made. The main drawbacks using transients were already

discussed in section 3.2.1. Additional comment made here is that in on-line leak detection

situations there are always pressure signal from normal operations (for example transients

caused by pump start-up, valve closures etc.) and those should be carefully eliminated

from automatic reports. A sample of an on-line leak analysis through pressure traces is

given in Figure 13. Active policy applications on network studies are much more difficult

to apply. From recent research papers the more promising are those that combine

hydraulic modelling software, GIS and SCADA system into one package (Tabesh and

Delavar, 2003). With advanced SCADA systems and large asset, customer and

maintenance databases, water service providers are facing the challenge of efficiently

extracting useful information from data. Data mining techniques can be used for different

purposes. For example, artificial neural network (ANN) models can be used for demand

forecasting (Bougadis et al., 2005) and for scanning large amounts of data like operational

variable and historical records to identify a failure event (Mounce and Machell, 2006;

Aksela et al., 2009; Mounce et al., 2007; 2008; 2009) or to estimate failure patterns. A

sample of other active control techniques applied onto real data is presented in Table 6.

There are many other methodologies for active leakage control that are not so commonly

used and therefore not discussed here. For a list of different active leakage technologies

please see Puust (2007). In general, available active leakage control techniques are either

expensive (and time consuming) or have a long leak detection and location time. Active


26

leakage control techniques are used regularly to survey the system for leaks and hence to

reduce the time elapsed between the burst occurrence and its repair thus reducing the

number of potential customer complaints. The main drawback of the active leakage

control is that it is labour intensive and expensive.

Active pressure management has been called a well-proven method that has an

effect on the whole network or pressure zone. Previously it has been shown that leakage

is tightly coupled with network pressure (Eq. 1). Therefore when overall pressure is

reduced, the same happens to leakage. One should still be aware that in such conditions

the leak detection itself is quite challenging because of a reduced leak flow. Pressure

reduction in water distribution systems is normally achieved through pressure reducing

valves (see Figure 14). The objective of pressure reduction is to ensure the target pressure

at any given zone/area/node satisfies the customers. When pressure reduction is made

dynamically over a period of time, some computer algorithm/program can definitely

make this step easier. For example genetic algorithms are used for that purpose in Reis et

al. (1997). There are many other optimisation techniques available in the literature that

can achieve this but their mathematical advances are out of scope in this review.

6. Conclusions and Future Work Recommendations


27

Leakage assessment, detection and control methods have come a long way since their

introduction in the mid 1950s. Based on the review completed and presented here, the

following main conclusions and future work recommendations are made.

Bottom-up leakage assessment methods are still preferred to top-down approaches

despite the fact that they are much more data hungry and time consuming to use. The

main value of top-down approaches is seen in making fast system-level leakage

assessments but also in verifying/controlling the results obtained by using bottom-up

methods. A certain novel value is seen in integrating these methods, especially bottom-up

methods, with pressure-driven hydraulic models of these systems (e.g., see Giustolisi et

al. 2008). Finally, both approaches are expected to benefit in the future from explicit

uncertainty analysis used to characterise and quantify the major sources of errors

involved in the leakage assessment process. This should be made possible with the

constant increase in better yet cheaper computational power available.

When it comes to leakage detection methods, significant advances have been

made in the past in both equipment-based and numerical models. The hardware based

methods (e.g. leak noise correlators) still remain superior in terms of detection accuracy

but also remain much more expensive to use than the numerical models. Further

developments of the promising equipment-based leak detection methods are envisaged

(e.g. pig-mounted acoustic sensing devices and/or ground penetrating radars).

With regard to the use of various transient based methods for leakage detection it

should be noted that these methods had limited success so far, typically in simpler pipe


28

systems only. It is envisaged that transient simulation models need to be developed

further before they can be utilised for leakage detection and assessment in more complex

pipe systems.

The further development of other numerical (i.e. non-equipment) based methods

is envisaged, especially the on-line type methods for real-time detection and diagnosis of

leaks caused by pipe bursts in networks. This should be made possible by the latest

developments in the pressure and flow sensor technology which should enable water

companies to install larger number of more accurate and cheaper devices in the near

future. The latest advancements made in the development of water quality (e.g. turbidity)

sensors could be potentially utilised too, through additional information available (e.g.

turbidity tends to increase significantly during pipe burst events). The most promising

techniques in the context of on-line models include various Artificial Intelligence

techniques, e.g. artificial neural networks for pressure/flow signal forecasting, wavelets

for signal de-noising and fuzzy sets and Bayesian networks for improved inference

analysis. Note that the successful development of the above real-time models will enable

merging the leak detection and assessment techniques, pressure-driven hydraulic solvers

and active leakage control methods.

Finally, an integral part of the above should be the development of novel

sampling design methods for locating pressure and flow sensors in pipe networks so that

better detection and diagnosis results can be obtained for both background and especially

burst related leaks.


29

Acknowledgements

The first author would like to acknowledge the financial support from the Estonian Science Foundation

(ETF7646).

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