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A review of methods for leakage management in pipe networksR. Puusta; Z. Kapelanb; D. A. Savicb; T. Koppelaa Department of Mechanics, Tallinn University of Technology, Tallinn, Estonia b School of Engineering,Computing and Computer Science, Centre for Water Systems, University of Exeter, Exeter, UK

Online publication date: 24 February 2010

To cite this Article Puust, R. , Kapelan, Z. , Savic, D. A. and Koppel, T.(2010) 'A review of methods for leakagemanagement in pipe networks', Urban Water Journal, 7: 1, 25 45To link to this Article: DOI: 10.1080/15730621003610878URL: http://dx.doi.org/10.1080/15730621003610878

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RESEARCH ARTICLE

A review of methods for leakage management in pipe networks

R. Puusta*, Z. Kapelanb, D.A. Savicb and T. Koppela

aDepartment of Mechanics, Tallinn University of Technology, Ehitajate tee 5, Tallinn, 19086, Estonia; bSchool of Engineering,Computing and Computer Science, Centre for Water Systems, University of Exeter, Harrison Building, North Park Road,

Exeter EX4 4QF, UK

(Received 15 August 2009; nal version received 4 November 2009)

Leakage in water distribution systems is an important issue which is aecting water companies and their customersworldwide. It is therefore no surprise that it has attracted a lot of attention by both practitioners and researchersover the past years. Most of the leakage management related methods developed so far can be broadly classied asfollows: (1) leakage assessment methods which are focusing on quantifying the amount of water lost; (2) leakagedetection methods which are primarily concerned with the detection of leakage hotspots and (3) leakage controlmodels which are focused on the eective control of current and future leakage levels. This paper provides acomprehensive review of the above methods with the objective to identify the current state-of-the-art in the eld andto then make recommendations for future work. The review ends with the main conclusion that despite all theadvancements made in the past, there is still a lot of scope and need for further work, especially in area of real-timemodels 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; waterdistribution systems; leakage model, pressure-dependent leakage

1. Introduction

Leakage occurs in all water distribution systemsnowadays. As noted by William Hope long time ago(1892), there is no water-supply in which someunnecessary waste does not exist and there are fewsupplies, if any, in which the saving of a substantialproportion of that waste would not bring pecuniaryadvantage to the Water Authority.

The amount of water leaked in water distributionsystems varies widely between dierent countries,regions and systems, from as low as 37% ofdistribution input in the well maintained systems inThe Netherlands (Beuken et al. 2006) to 50 percent insome undeveloped countries and less well maintainedsystems (Lambert 2002; Mamlook and Al-Jayyousi2003).

Leakage is not just an economical issue as it is oftenperceived and presented by water companies but it isalso an environmental, sustainability and potentially ahealth and safety issue. As noted by Colombo andKarney (2002), leakages cause inecient energydistribution through the network (thus wasting energyused for pumping the water) and, also, may aect

water quality by introducing infection into waterdistribution networks in low pressure conditions.

A number of past, review type papers exist in theeld of leakage modelling and management. One of theearliest review papers is the paper by Morris (1967)which provided an overview of potential causal factorsleading to water pipe breaks. A report summarisingdierent leakage control policies can be found inGoodwin (1980). Comparisons of the key attributes ofdierent leak detection methods are given by Cist andSchutz (2001). Another review and classication ofleakage detection methods is reported by Liou et al.(2003). A review of calibration methods in waterpipelines (including leaks) can be also found inKapelan (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 byconsidering the overall leakage management process.The objective of this paper is to review the methodsand models developed in the past used in dierentphases of this process, from becoming aware of theleak existence to controlling the level of leakage in the

*Corresponding author. Email: [email protected]

Urban Water Journal

Vol. 7, No. 1, February 2010, 2545

ISSN 1573-062X print/ISSN 1744-9006 online

2010 Taylor & FrancisDOI: 10.1080/15730621003610878

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system. It is hoped that this way the new promisingresearch areas will be found as they often exist alongthe boundaries of current research areas. Morespecically, this review looks into past methods andmodels developed that can be used to either assess,detect or control leakage in distribution (and other)pipe systems. The main objective is to identify theadvantages and disadvantages of all existing ap-proaches and to then use the observations made tosuggest possible future research work in the eld.

The paper is laid out as follows. After thisintroduction, the relevant background information ispresented in section 2. This is followed by the review ofleakage assessment methods in section 3, detectionmethods in section 4 and leakage control methods insection 5. Finally, the main conclusions are drawn insection 6 of the paper.

2. Background

Dierent denitions of leakage in distribution systemsexist. The most frequently used one denes the leakageas (amount of) water which escapes from the pipenetwork by means other than through a controlledaction (Ofwat 2008). Water leakage in distributionsystems is typically classied into background andburst related leakage (ODay 1982). Bursts (i.e. mainbreaks) represent structural pipe failures and back-ground leaks represent the water escaping throughinadequate joints, cracks, etc. Leaks can also exist inservice reservoirs and tanks.

Leakage in distribution systems can be caused by anumber of dierent factors. Some examples includebad pipe connections, internal or external pipe corro-sion or mechanical damage caused by excessive pipeload (e.g. by trac on the road above or by a thirdparty working in the system). Other common factorsthat inuence leakages are ground movement, highsystem pressure, damage due to excavation, pipe age,winter temperature, defects in pipes, ground conditionsand poor quality of workmanship. Therefore, thepresence of leakage may damage the infrastructure andcause third party damage, water and nancial losses,energy losses and health risks.

Leakage is dependent on system pressure. Basi-cally, the higher the pressure, the larger the leak owand vice versa. Initially, an orice type equation(Wiggert, 1968) was used to describe this relationship.Although the orice equation is still widely used inmany research studies, the user must be aware that theequation can lead to misleading results when thepipe in question is not made of a rigid material(Greyvenstein and van Zyl 2005) or when the pressureis negative (Todini 2003). Lately, a more generalisedleak owpressure equation has been adopted which

allows specifying leakage exponent dierent from 0.5(Germanopoulos 1985). It has been shown that thevalue of this exponent depends on the type of leak,pipe material behaviour, soil hydraulics and waterdemand (Cassa et al. 2005; Greyvenstein and Van Zyl2005; Walski et al. 2006; Noack and Ulanicki 2006).For example Van Zyl and Clayton (2005) note thatwhen leakage is analysed as pressure dependent,demand should follow the same procedure. More onleakage as a hole in a pipe and its characteristics can befound in Beck et al. (2005a,b) and Coetzer et al. (2006).Various studies about the pressure dependent leakagemodelling can be found from the literature. Modellingbased on leak discharge coecient and leak area canbe found from the articles by May (1994), Vela et al.(1995), Simpson and Vitkovsky (1997), Vitkovsky andSimpson (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), Vitkovsky et al. (2003a) andVerde (2005). Modelling that also included pipecharacteristics 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 Diaset al. (2005).

3. Leakage assessment methods

The objective of leakage assessment (i.e. water audit) isto estimate the quantity of water lost in the systemanalysed without worrying where the leaks are actuallylocated. The assessment methods developed so far canbe broadly classied into the following two maingroups: (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 ap-proaches is to estimate the leakage in a particularsystem by evaluating dierent components of theoverall water balance, primarily the water consumedfor dierent purposes. The two main approaches usedare the IWA approach (Lambert and Hirner 2000) andthe approach used by the OFWAT in the UK.Although quite similar, there are some dierencesbetween the two approaches due to slightly dierentterminology and denitions used for some waterbalance components.

More information about the general leakageassessment can be found from Stenberg (1982),Thornton (2002), Farley and Trow (2003) and Scottand Barrufet (2003). The latest reports about averagelosses in the UK (based on areas operated by dierent

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water service companies) can be found in (AHL 2006).The latest guidance notes on leak location andrepair are published in Pilcher (2003) and Pilcheret al. (2007).

Despite the simplicity of a top-down type leakassessment, the leakage estimate obtained via thismethod is referred to as a crude estimate. Gatheringsuch information helps to decide what the next step inleakage studies should be for a particular network butit does not help to bound potential leak areas,let al.one locate leaks.

3.2. Bottom-up approaches

Bottom-up type leakage assessment can be consid-ered the second part of the audit process. Thisprocedure is implemented when the company hasconrmed the data used in the top-down portion. Itincludes every area of the companys operation:billing records, distribution system, accounting prin-ciples etc. The audits main purpose is to nd outthe eciency of the water distribution system andthe measures needed to achieve these. Bottom-upaudits require the most accurate and up-to-date datapossible.

Bottom-up real loss assessment can be carried outin two dierent ways: (a) 24 Hour Zone Measurement(HZM) or (b) Minimum Night Flow (MNF) analysis.HZM needs a temporary isolated area of the distribu-tion network that is supplied from one or two inowpoints only. In these areas, 24-h inow measurementsshall always be logged along with pressure measure-ments. MNF in urban situations normally occursduring the early morning period, usually between 02:00and 04:00 h (Liemberger and Farley 2004). Theestimation of the real loss component is carried outby subtracting legitimate night uses from the MNF. Toget a satisfactory estimate of the daily leakage,Stenberg (1982) has found that night leakage owrates should then be multiplied by 20 h. This assump-tion does not take into account that pressure is notconstant over a period of time. Therefore Lambert(2001) suggests using a method called xed and variablearea discharges (FAVAD). This method uses thefollowing equation:

q k Hb 1

Where q volume rate per unit length; k Cdb (2g)b;b leakage exponent; Cd discharge coecient;b width of the slot; g gravitational acceleration;H pressure head. Leak exponents vary, being closeto 0.5 with xed area leakage path (hole in pipe) and1.5 with variable leakage path (crack in pipe). Theincrease or decrease of real losses due to a change

in pressure can then be computed by FAVADconcept as:

L1=L2 H1=H2 b 2

where L1 and L2 are leakage rates and H1, H2 arepressure heads at respective times. Thereafter leakagecan be simulated over the full 24-h period (seeFigure 1).

At the end of the real loss assessment process, theadvantage of the combined top-down, bottom-up andcomponent analysis (Table 1) that were introduced inthe early 1990s (Farley and Trow 2003) becomesobvious. Several countries have had their own mea-sures or indicators. For example the sample measurescould be: percentage of average daily ow (USA,France); m3/km of mains/hours (German, Japan);litres/property/hour (UK) and litres/service connec-tion/hour. The problem is that those indicators do nottake account of component analysis techniques there-fore additional performance measures have to be used.Comparison of some additional performance measurescan be found in Tuhovcak et al. (2005). Performanceindicators should count the possibility of consumptiondecreases seasonally or annually (non-revenue waterdoes not) and also take into account pressure relationsin the pressure zone. Therefore recommended indica-tors should always indicate its robustness. Robustnesscan be dened with a level and a function (Table 2).The most commonly used leak index nowadays isinfrastructure leakage index (ILI) (Lambert 2003;Farley and Trow 2003). The advantages of using ILIare that it can be consistently applied across a range ofutilities and that it is a measure of what can beachieved given the condition of the infrastructure. Itskey disadvantage is that it is not easily understood bynon-technical readers. Additionally it does not takeinto account the relative costs of leakage management

Figure 1. Leakage modelling (24 h) based on minimumnight ow measurement. Adapted from Liemberger andFarley (2004, Figure 2).

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(and other marginal costs, like environmental costs)and it is not able to dene what level of reduction iseconomically feasible. An additional advantage ofcalculating an ILI index is that it can be used tocalculate the leakage exponent (Thornton and Lam-bert 2005):

b 1:5 1 0:65=ILI p100

3

where p the percentage of rigid pipes in network.Using Equation (3) the calculation for dierent

leakage exponents for dierent networks/countries/regions can be found (see Table 3).

Although classical minimum night ow 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 thatcould possibly be used depending on network archi-tecture (see Puust 2007). In addition to water audits the

assessment can be done also using some statisticalanalysis for detecting the magnitude of leaks (Buch-berger and Nadimpalli 2004). This is expected to bemore accurate but with a cost of a need of continuous,high resolution measurements of discharge at one ormore locations within the district metering area(DMA). This can be problematic in some cases becausethe high resolution data measurements are not usedvery often within a DMA and the location of dataacquisition systems must be carefully planned in suchcase studies (Vitkovsky et al. 2003c; Kapelan et al.2003c, 2005; Behzadian et al. 2009).

4. Leakage detection methods

Historically, leakage assessment studies have beencarried out to quantify total losses including, ifpossible, real and apparent losses. This was followedby the development of leakage detection methods withthe aim to detect and locate leaks. Although some

Table 1. IWA standard for international water balance and terminology.

Authorizedconsumption

Billed Billed metered consumption (including water exported) RevenuewaterBilled unmetered consumption

Unbilled Unbilled metered consumption Non-revenuewaterUnbilled unmetered consumption

System inputvolume

Waterlosses

Apparentlosses

Unauthorized consumptionCustomer metering inaccuracies

Reallosses

Leakage on transmission and/or distribution mainsLeakage and overows at utilitys storage tanksLeakage on service connections up to point of customermetering

Table 2. Recommended indicators for real losses and non-revenue water (adapted from Liemberger and Farley (2004,Figure 4)).

Function Level Performance Indicator Comments

Financial: NRWby Volume

1 (Basic) Volume of NRW [% of System InputVolume]

Can be calculated from simple water balance,not too meaningful

Operational:ApparentLosses

1 (Basic) [m3/service connection/year] or: Best of the simple traditional performanceindicators, useful for targer setting, limiteduse for comparisons between systems

[m3/km of mains/year] (only if serviceconnection density is 520/m)

Operational: RealLosses

1 (Basic) [litres/service connection/day] or:[litres/km of mains/day] (only if serviceconnection density is 520/km)


2 (Intermed.) [litres/service connection/day/m pressure]or:

Easy to calculate indicator if the ILI is notknown yet, useful for comparisons betweensystems[litres/km of mains day/m pressure] (only

if service connection density is 520/km)

Financial: NRWby cost

3 (Detailed) Value of NRW [% of annual cost ofrunning system]

Allows dierent unit costs for NRWcomponents, good nancial indicator


3 (Detailed) Infrastructure Leakage Index (ILI) Ratio of Current Annual Real Losses toUnavoidable Annual Real Losses, mostpowerful indicator for comparisons betweensystems

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leakage detection methods have been around for years,because of constant development, they are gettingincreasingly high tech and sophisticated than everbefore. Still, regardless of whether the methods areequipment or non-equipment based, it is commonpractice to use some leak detection method inconjunction with other methods.

4.1. Leakage awareness methods

The term leak awareness is used to explain thediscovery of a leak in a particular area within thenetwork. It does not give any information about itsprecise location. Usually a hydraulic model is neededfor the leakage awareness test. Various hydraulicmodels have been proposed to detect leaks in waterdistribution systems. Those methods usually involvecalibration/optimisation techniques to analyse thedierent areas of the network. The problem isformulated as a constrained optimisation problem ofweighted least- square type to minimise the objectivefunction E:

E XP

i1wh h

mi hi

2 XQ

i1wq q

mi qi

2

XN

k1wp p

mi pi

2 4

where P and Q are the number of pressure, owmeasurement respectively, hmi is the measured head atnode i, hi is the computed head at node i, q

mi is the

measured ow at pipe i, qi is the computed ow at pipei, pmi is the prior estimate (pseudo measurement), pi isthe prior estimate and N is the number of priorestimate, w is a weight factor for pressure/ow andprior estimate part. Prior estimates were introducedinto the minimisation problem by Kapelan (Kapelan2002; Kapelan et al. 2000, 2003a,b, 2004) to avoid theill-posed problem (that is there is no solution, nounique solution or the solution is unstable), to improvethe accuracy of the estimated calibration parameters

and to increase the speed of the convergence process.It should be noted that prior estimates work betterwith pipe friction factors as these are less sensitive thanleak eective areas.

The minimisation of Equation (4) gives the solutionto an inverse problem (Pudar and Liggett 1992; Stathisand Loganathan 1999). Various minimisation algo-rithms have been used to minimise the objectivefunction, Equation (4). When steady state regime isused, both pressure and ow measurements can beused. In a transient ow regime ow measurements aredicult to use because most ow meters do not reactinstantaneously to a change in ow (Chen, 1995). Earlyadoptions of uid transients for leak detection can befound from Wiggert (1968), Nicholas (1990), Liggett(1993), Liggett and Chen (1994).

The use of uid transients for leak detection hasgained popularity over the last decade as a massiveamount of data can be gathered in a very short periodof time therefore ensuring that the inverse problem willalways be over-determined. Another good advantageover steady-state calculation is that pressure waves areless aected by friction than the general ow and thusthe precise friction values become less important to thecalculation. Therefore, using transients, the leakdetection and calibration (friction factors) can bedone simultaneously, thus providing a solution to theproblem of unknown or poorly known friction. Fluidtransients are used to probe the pipeline in much thesame way as radar and sonar are applied to locate andidentify objects. The reason why methods based ontransients are mainly used on single, groundedpipelines is that an uncertainty of the system doesaect the results considerably (pressure wave reec-tions from each feature of the pipe). For under-grounded pipes the systems architecture can be hardlyfollowed thus its applicability in such situations is stillquestionable.

A number of hydraulic transient-based techniquesfor detecting and locating existing leaks are describedin the literature: leak reection method (LRM)(Jonsson 1995, 2003; Brunone 1999, Brunone andFerrante 1999, 2001, 2004; Covas and Ramos 1999),

Table 3. Summary of exponents b derived from eld tests (adapted from Garzon-Contreras and Thornton (2006, Table 1)).

Country Number of zones tested Range of exponents b Average exponent b

United Kingdom (1970s) 17 0.70 to 1.68 1.13Japan (1979) 20 0.63 to 2.12 1.15Brazil (1998) 13 0.52 to 2.79 1.15United Kingdom (2003) 75 0.36 to 2.95 1.01Cyprus (2005) 15 0.64 to 2.83 1.47Brazil (2006) 17 0.73 to 2.42 1.4

Totals 157 0.36 to 2.95 1.14


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inverse transient analysis (ITA) (Liggett and Chen1994; Liou 1994; Vitkovsky et al. 2000; Kapelan et al.2003a,b; Covas et al. 2001, 2003, 2005b; Covas andRamos 2001; Stephens et al. 2004; Wang et al. 2006;Soares et al. 2007), impulse response analysis (IRA)(Liou 1998; Vitkovsky 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,b, 2003a, b; Covas et al.2005a; Ferrante et al. 2005; Lee et al. 2003, 2005a,b,2006; Zecchin et al. 2005, 2006). The main objective ofall transient leak detection methods is the same toextract information about the presence of a leak fromthe measured transient trace. A transient event isgenerated either by system elements (i.e. inline valvesand pumps) or special devices (for example solenoidside discharge valves).

In the leak reection method (LRM), a transientwave is travelling along a pipeline and it is partiallyreected at the leak. The location of the leak can bethen identied from the measured pressure trace(Figure 2). The magnitude from the leak depends onthe ratio between the size of the generated transientwave and the size of the leak orice. LRM methods areso far used only in single pipe case studies inlaboratory conditions.

The inverse transient analysis method (ITA) wasrst introduced by Liggett and Chen (1994). The ITAuses least-squares regression between modelled andmeasured transient pressure traces. The leak is usuallymodelled at network nodes and the minimisation of thedeviation between the measured and calculated pres-sures produces a solution of leak location and size(Figure 3). The ITA method is a well-researched topic

but since its introduction, the main eort has beenfocused on the development of the mathematical partof the technique and not on experimental validation oreld testing. Some limited experiences from laboratoryand elds tests can be found from Vitkovsky et al.(2001), Stephens et al. (2004), Covas et al. (2005b) andSaldarriaga et al. (2006). As with LRM, the tests aremade on single pipeline rather than on a network.Application diculties lie in the fact that ITA needs anaccurate modelling of the transients and boundaryconditions of the pipe system. To address the latter, agreater emphasis should be directed toward analysis oferrors and strategies to deal with the uncertainties ingeneral (Vitkovsky et al. 2007). Model error is the mostlikely limiting factor in successful eld application ofITA and its results should never be presented withoutquantication of their uncertainty.

The impulse response analysis (IRA) is based on thefact that the transient propagation along the pipeline isaected by the friction of the pipe wall and other losselements such as leaks. This eect results in damping ofthe transient wave. A leak can be detected when atransient damping in the same pipeline is comparedwith and without a leak (Figure 4). The lack ofinformation about tests in real pipeline systems wherenoisy data would be used makes it a less importantmethod when compared with LRM and ITA. It hasone advantage when the comparison should be madewith TDM or FRM. Namely, in IRA no discretizationof the pipeline is needed and the shape of the generatedtransient is not important.

In the transient damping method (TDM) it isanalytically derived that friction related transientdamping in a pipeline without a leak is exactlyexponential and the corresponding damping in apipeline containing a leak is approximately exponential(Wang et al. 2002). The rate of the leak-induceddamping depends on leak characteristics, the pressurein the pipe, the location of the transient generationpoint and the shape of the generated transient. Testson a laboratory pipeline showed successful leakdetection (Figure 5) but in a real situation, friction isnot the only cause of transient damping. Transientdamping can be caused by other physical elements likejoints, connections, re hydrants and pipe walldeterioration products. The modelling of these ele-ments can be complicated and in some cases evenimpossible. Therefore it may be dicult to estimate theleak-free damping for a real pipeline.

The frequency response method (FRM) uses theanalysis of transient response in the frequency domain.Fourier transforms are used to transform time-domaindata into the frequency domain. Leak location can beobtained when the dominant frequencies of no-leakand leaking pipelines are compared (Figures 6 and 7).

Figure 2. Pressure time-history at the leak during transientdue to the closure of the end valve. Even though the transientattenuates quickly, the risk of compromising water qualityexists (where pressure falls below tank level, h0 and theambient pressure external to the leak, pext is higher than theinternal pipe pressure at the leak, pe). Adapted from Brunoneand Ferrante (2004, Figure 1).

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Performance of the method is strongly inuenced bythe shape of the transient and the measurementlocation. As with other methods based on transients,only pipeline applications of frequency responseanalysis are presented in the literature. Some of thecase studies that are based on pressure transients aresummarised in Table 4.

There are many other leak awareness methods butonly three of them have been applied to pipe networks(Saldarriaga et al. 2006; Deagle et al. 2007; Wu andSage 2007). It should be noted that most of them arevery rarely used and/or do not have any practical testsmade to support the idea. One of the reasons why these

model based techniques are not so widely used couldbe because of the low ow rates in pipelines thateliminate the possible use of commonly used pressuremeasurement devices that are cheap and easilymanageable, but not eective when used in low owconditions.

When leak awareness methods are under discussionit should be also mentioned that very few of them areprobabilistic ones. In that respect, the Bayesian systemidentication methodology has been used by Poulakiset al. (2003), Rougier (2005) and Puust et al. (2006).The main reason to use a Bayesian interface in leakagestudies is that normally we are dealing with dierent

Figure 4. Impulse response functions for the non-leaking (a) and leaking (b) cases. The rst leak-induced reection for theleaking case determines the correct location and size of the leak. The secondary reection is negligible compared to the main leakreection. Adapted from Vitkovsky et al. (2003b, Figure 4).

Figure 3. Transient analysis for leak detection. Head variations measured at the same nodes for no-leak case studies (a,b) andfor leak case studies (c,d). Considerable head damping can be seen when leaks exist in the system. Adapted from Vitkovsky et al.(2001, Figures 3, 4).


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Figure 5. Fourier series analysis of transients measured from pipeline without leak (a) and with leak (b). By analysing thedamping of harmonic components the leak can be identied. Through the ratios of leak damping rates the leak location can becalculated. Adapted from Wang et al. (2002, Figure 9).

Figure 6. Continuous wavelet transform (CWT) and discrete wavelet transform (DWT) for no-leak (a,b) and for leak (c,d) casestudies. It is possible to show the presence of the leak of diameter equal to 1.49 mm in both cases. It is observed that chains ofmaxima appear in gures (c) and (d) in correspondence with the instants t 1.23 s and t 3.23 s, which are not present in (a)and (b). Adapted from Ferrante et al. (2005, Figures 25).

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kinds of errors that cannot always be included incalculations. Therefore to make more sense, the naldiscrete value is bounded with a certain probabilitythat gives us more information about the resultreliability. The drawback is that usually such proce-dures need a great deal of computer power forcalculations. In general, probabilistic models are usedalso to conduct criticality analysis, where small cracksin pipes might cause the overall failure of much largersystems (like cooling systems in nuclear power plants,Rahman et al. 1997).

Great eort has been made so far in the develop-ment of model based leak detection methodologies.Whilst this development will continue, it is obviousthat some methods will be suitable for application tosimple systems only (e.g., single pipelines). An exampleof this is transient based methodologies. Because oftheir limitations when applied to network systems it isclear that development of transient based methodolo-gies for leakage control will be limited to singlepipelines. For a general reference about leakagecontrol please see section 5.

Table 4. Eciency ranges of various leak detection methodologies using pressure transients.

Transientmethodology Case study

Inspection range(pipeline length)

Detectableleak size

Locationprecision

Transient pressurewave height Reference

LRM Laboratory 135 m 0.04 l/s 1.9 m 44 m Jonsson (2003)ITA Real 5936 m 3 l/s 50 m 13 m Covas et al. (2005b)ITA Real network 1 l/s 4.85%* 7 m Saldarriaga et al. 2006IRA Numerical 20,000 m 10 l/s 2000 m 2 m Liou (1998)TDM Laboratory 37.2 m 0.01 l/s 0.38 m 2 m Wang et al. (2002)FRM Numerical 2,000 m 4.73 l/s 5500 m 26 m Lee et al. (2005a)

*Leak size error.

Note: This table is for general guidance only based on data that is available from given references. It may not reect the best solution for thatparticular technology that is currently available.

Figure 7. Impact of changing leak size and position on the frequency response diagram extracted at the inline valve atdownstream boundary: (a) leak at 700 m, Cdb 0.00014 m2; (b) Cdb 0.00028 m2; (c) leak at 1400 m, Cdb 0.00014 m2; (d)no leak. Adapted from Lee et al. (2005a, Figure 6).


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4.2. Leakage localisation methods

Leak localising is an activity that identies andprioritises the areas of leakage to make pinpointingof leaks easier. Some methods/techniques that belongto this group are: acoustic logging (Moyer 1983; Hough1988; Rajtar and Muthiah 1997; Hessel et al. 1999;Hunaidi and Chu 1999; Miller et al. 1999; Lockwood2003; Shimanskiy 2003; Bracken and Hunaidi 2005;Muggleton et al. 2006), step-testing (Farley and Trow2003; Pilcher et al. 2007), ground motion sensors andground penetrating radars (Hunaidi 1998; Lockwoodet al. 2003; OBrien et al. 2003).

The most well-known and eective leak localisingmethod is step-testing and it has been used by severalwater utility companies for quite some time. Step-testing is an activity whereby the area is subdivided bythe systematic closing of valves during the period ofminimum night ow. Depending on the methodologyused, step-testing may cause backsiphonage, the risk ofinltration of ground water and some parts of thenetwork can be without water for a period of time. Notall networks are planned with the possibility of futurestep-testing in mind and therefore it may be dicult toapply. Because of a need of careful planning, nightwork involving the step-testing has been replaced byacoustic logging during the 1990s (Pilcher et al. 2007).

Acoustic logging (AL) is performed using vibrationsensors or hydrophones, which are temporarily orpermanently attached to the pipe ttings. The distancebetween each other typically varies between 200 and500 m. As with step-testing the data is collected atnight times, usually between 02:00 and 04:00 h.Downloaded data will then be analysed statisticallyfor detection of leak signals (Figure 8). Although awide area may be covered quickly, for a successful leakdetection good skill is required. The fact that quiet

leaks may not be heard and the background noise cantbe ignored makes it dicult to apply in certainsituations.

The application of ground penetrating radar (GPR)for leak location has been given a lot of attentionduring the last few years (Farley 2008). Groundpenetrating radar inspection is a non-destructivegeophysical method that produces a continuouscross-sectional prole or record of subsurface features(Figure 9). Methods like this could be used to locateleaks in water pipes by detecting either undergroundvoids created by leaking water as it circulates near thepipe or by detecting anomalies in the pipe depth asmeasured by radar. GPR has evolved for some yearsnow. It has been previously described as a timeconsuming methodology but recent studies show thatalong transmission main routes it can be carried out at1530 km/h, depending on location and trac. AsGPR technology is similar in principle to seismic andultrasound techniques, the main disadvantage comesfrom the fact that anomalies like metal objects in theground can lead to false conclusions and it might notbe applicable in cold climates. Some developed GPRtechnologies have a penetration capability of up to 2 minto the ground. For example in northern Europeancountries the water pipe bottom should be laid down insome occasions at least 1.8 m deep to avoid waterfreezing. Therefore GPR technology cannot givetrustworthy results on those extreme occasions and insituations where main pipes are excavated even deeperinto the ground. It should be still noted that thismethodology is a good alternative in situations whenlarge diameter or non-metallic pipes need monitoring.

In summary, leakage localisation methods can beused on their own or before/following the applicationof some other method. For example, if a hydraulicmodel of the analysed system is available then some

Figure 8. Results of acoustic noise loggers in two consecutive days (after repairs): line with crosses noise amplitude andline with dots noise dispersion. Leakage situation corresponds to line with crosses above line with dots. Adapted fromCovas et al. (2006b, Figure 8).

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numerical (i.e. inexpensive) leak detection method maybe used before the leak localisation method, to narrowthe area searched for the leak. However, if thehydraulic model is not available (or not updatedregularly) then a leak localisation method could beused on its own.

In general district audits are labour-intensive andcostly, since they are performed at night. A morerecent trend is that permanent ow meters are installedthat are connected telemetrically to a supervisorycontrol and data acquisition (SCADA) system. Thetransmitted ow rate data are automatically analysedto detect unusual increases in ow patterns (Mounceet al. 2009). Based on experience with water system, theincrease in ow rate can be explained by leakage ornot. District audits and step testing help identify areasof the distribution system that have excessive leakage.No information about the exact location of leaks isgiven. When step-testing or SCADA system is notavailable, some other technology is needed that can beused for leak localisation with a reasonable time. Thereasonable time to detect leakage varies depending onthe leak ow rate. Small leaks are more dicult tolocate, especially when using acoustic logging forplastic pipes. As a consequence, the GPR technologywas developed (any pipe material can be surveyed) andvarious studies published demonstrate promisingperformance. GPR is probably one of the keytechnologies studied in Europe currently (WATER-PIPE 2009).

4.3. Leakage pinpointing methods

Leakage pinpointing methods include methodologiesthat are the most accurate in todays leak detectionsurveys. Three main groups described here are based

on (a) leak noise correlators (Grunwell and Ratclie1981; Cascetta and Vigo 1992; Gao et al. 2004, 2005,2006; Hunaidi et al. 2004; Muggleton et al. 2004,Muggleton and Brennan, 2004, 2005); (b) gas injection(Field and Ratclie 1978; Hunaidi et al. 2000; Farleyand Trow 2003); and (c) pig-mounted acoustic sensing(EPSRC 2002; McNulty 2001; Mergelas and Henrich2005). The historical appearance of leakage pinpoint-ing methodologies is given in Figure 10.

Leak noise correlators (LNC) are the most commontechnique for leak location that was rst introducedcommercially into the marketplace in the late 1970s(Thornton 2002). Their technology has been improvedover the last few years quite considerably. The WaterResearch Centre (WRC) in England was one of theleading research institutions to apply the methodologyonto real pipelines. To correlate the sound from a leak,two microphones are located in contact with the pipeor valve stems at the same time, with one microphoneon each side of the leak (Grunwell and Ratclie 1981;Stenberg 1982). The sound is compared in thecorrelator, which is capable of determining thedierence in time for sound to reach the correlator.Knowing the speed of sound in the pipe, it is then easyto calculate the distance to the leak, which will beindependent of the geophone, trac noise, etc. Foraccurate leak localisation the pipe system should beknown precisely as a leak correlating in a branchedsection tend to show a leak on a tee and not at its exactlocation. Such misleading information aects mainlyexcavation costs and man-hours needed for a repair.The latest versions of leak noise correlators canaccurately locate a leak to within 1 metre in mostpipe sizes. The distance between the sensors can be ashigh as 3000 m but it depends highly on pipe material.For plastic pipes this methodology is quite

Figure 9. Left image: Continuous-wave radar principle shown on the left image. Right image: GPR image showing wateraccumulation from a leak. Adapted from Farley (2008, Figure 1). Single frequency f0 transmitted by radar is received back frommoving targets slightly dierent frequency f0 Df. By rejecting f0, only moving objects (such as leaking water) are detected.


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questionable as distance between the sensors should bequite small: 15100 m, making this method very slow.The method works best with clean, small diametermetallic pipes in high water pressure areas where hardpipe backll is used.

In a tracer gas technique (TGT), a non-toxic, water-insoluble and lighter-than-air gas, such as helium orhydrogen, is injected into an isolated section of a waterpipe. This is followed by ground scanning with a highlysensitive gas detector which should identify any tracesof escaped gas from the leak point(s) (Figure 11).Although this method is widely used for machinerytesting, it is normally prohibitive for leak detectionbecause of the high cost. Its eectiveness comes fromaspects that through TGT multiple leak locations canbe found in a single pipe section or at a branched pipesystems where noise correlation techniques usuallyfails or gives misleading results. The main disadvan-tage in addition to high costs are that the gas could betrapped near the ceiling of water-lled pipes and thuscould not escape if leaks were not near the top of thepipe.

The pipe pig-mounted acoustic (PMA) techniquehas also been used for leak detection (McNulty 2001;EPSRC 2002). This technique requires the insertion ofa microphone (or a pair of microphones) underpressure into the main. The velocity of water carriesthe microphone to the leak position whereas the noiseand its position are continuously recorded. Some latesttechnology examples can be found from Chastain-Howley (2005) and Fletcher (2008). Inline pigs are usedto carry dierent kinds of sophisticated measuringdevices such as magnetic ux leakage (Mukhopada-hyay and Srivastava 2000), hydroscopes (Makar andChagon 1999) or ultrasonic tools (Willems andBarbian 1998) along the pipeline. In general thesetools need clean pipes and therefore it is dicult toapply this methodology to old pipes where there maybe heavy corrosion. Access to the inside of the pipelineis also needed. Attention must be paid to the fact that

as pigs are in contact with the pipe inner wall theireect on the water quality must be considered before asurvey is performed.

Leak pinpointing techniques are the most precisetechnologies currently available for leak detection. Itshould be remembered that such a precision comeswith very high costs in terms of equipment owning orrenting and man-hours needed for surveys to becarried out. Considering this and the length of timeneeded for implementation, it is recommended to useleak pinpointing techniques in conjunction with someleakage awareness or localisation method. Table 5brings out some general guidance when leak localisa-tion or pin-pointing technique should be chosen.

5. Leakage control models

Leakage control models can be generally classied intothe following two main groups: (a) passive (reactive)leakage control and (b) active leakage control. Apassive leakage control is a policy of responding only

Figure 10. Several leak detection methods by historical appearance. Adapted from Pilcher et al. (2007, Figure 17).

Figure 11. Recording of a typical leak response when tracergas technique is used Adapted from EPRI (1989, Figure 2).Response time depends on gas that is used and a magnitudeof the response depends on a gas volume that was injected.

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to leaks and bursts reported by the public (in somecases also by a companys own sta). Active leakagecontrol concerns management policies and processesused to locate and repair unreported leaks from thewater company supply system and customer supplypipes.

Many water utilities still take a passive attitude ofwaiting for a problem to arise and repairing it onlywhen leaks become self-evident. For example, theappearance of water on the ground surface followingpipe failure is visually detected by the sta or reportedby customers. Manual location techniques are thenused to identify the actual location of the failure. Thispresents inevitable problems for customers (Ramoset al. 2001). Passive policy is very straightforward andsimple to use but it does not involve any systematicaction. Therefore this kind of acting is reasonable onlyin such water systems where there are very low leakagelevels, the average loss is constantly below 1015%.Even in low loss cases it is advisable to use some moreadvanced technology at the same time (like SCADAsystem) as when using passive policy the overall losscan easily raise to 40%.

Active leakage policy involves the techniques like:active leakage control and active pressure manage-ment. There are also sectorisation and economicintervention but those are not discussed here. Themost appropriate leakage control policy will mainly bedictated by the characteristics of the network and localconditions, which may include nancial constraints onequipment and other resources (Farley and Trow2003). The nal choice of the method is also basedon economic considerations. Term economicallyviable can be dened with an economic curve ofleakage (ELL) analysis that is described in Figure 12.

The most widely used active leakage controlmethodology on single pipelines is based on pressuretransients (Misiunas et al. 2003, 2005a,b, 2006). Thelack of their commercial availability gives them moreattention from the research side and any system-wideconclusions are hard to be made. The main drawbacksusing transients were already discussed in section 4.1.

Additional comment made here is that in on-line leakdetection situations there are always pressure signalfrom normal operations (for example transients causedby pump start-up, valve closures, etc.) and thoseshould be carefully eliminated from automatic reports.A sample of an on-line leak analysis through pressuretraces is given in Figure 13. Active policy applicationson network studies are much more dicult to apply.From recent research papers the more promising arethose that combine hydraulic modelling software, GISand SCADA system into one package (Tabesh andDelavar 2003). With advanced SCADA systems andlarge asset, customer and maintenance databases,water service providers are facing the challenge ofeciently extracting useful information from data.Data mining techniques can be used for dierentpurposes. For example, articial neural network(ANN) models can be used for demand forecasting(Bougadis et al. 2005) and for scanning large amountsof data like operational variable and historical recordsto identify a failure event (Mounce and Machell 2006;

Table 5. Eciency ranges of various leak localisation and pinpointing techniques.

Localisation Inspection range Detectable leak size Precision Reference

Step-test DMA excessive limitedAL 200500 m *0.003 l/s 3075 m* Rajtar and Muthiah (1997)GPR 1 m *0.33 l/s n/a Hunaidi et al. (1998)

PinpointingLNC 152000 m *0.03 l/s 50.6 m Hunaidi et al. (2000)TGT up to 1 m n/a 51 m Hunaidi et al. (2000)PMA up to 2000 m *0.00030.003 l/s 50.5m Mergelas and Henrich (2005)

*Depends on inspection range.

Figure 12. Typical economic level of leakage (ELL)analysis. Economically feasible leakage level compared withnet present value (NPV) costs. There are also some level ofleakages (background losses) that are not possible toeliminate at all. Adapted from Tripartite Group (2002,Figure 4.1).


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Aksela et al. 2009; Mounce et al. 2007, 2008, 2009) orto estimate failure patterns. A sample of other activecontrol techniques applied onto real data is presentedin Table 6. There are many other methodologies foractive leakage control that are not so commonly usedand therefore not discussed here. For a list of dierentactive leakage technologies please see Puust (2007). Ingeneral, available active leakage control techniques areeither expensive (and time consuming) or have a longleak detection and location time. Active leakagecontrol techniques are used regularly to survey thesystem for leaks and hence to reduce the time elapsedbetween the burst occurrence and its repair thusreducing the number of potential customer complaints.The main drawback of the active leakage control isthat it is labour intensive and expensive.

Active pressure management has been called a well-proven method that has an eect on the whole networkor pressure zone. Previously it has been shown thatleakage is tightly coupled with network pressure(Equation 1). Therefore when overall pressure isreduced, the same happens to leakage. One should

still be aware that in such conditions the leak detectionitself is quite challenging because of a reduced leakow. Pressure reduction in water distribution systemsis normally achieved through pressure reducing valves(see Figure 14). The objective of pressure reduction isto ensure the target pressure at any given zone/area/node satises the customers. When pressure reductionis made dynamically over a period of time, somecomputer algorithm/program can denitely make thisstep easier. For example genetic algorithms are usedfor that purpose in Reis et al. (1997). There are manyother optimisation techniques available in the litera-ture that can achieve this but their mathematicaladvances are out of scope in this review.

6. Conclusions and future work recommendations

Leakage assessment, detection and control methodshave come a long way since their introduction in themid 1950s. Based on the review completed andpresented here, the following main conclusions andfuture work recommendations are made.

Table 6. A snapshot of various leakage control techniques applied to real data.

TechniqueHydraulicmodel Network size

Burstdetection

sizeLeak sizeerror

Detectiontime Reference

SE Yes DMA 8.383 l/s n/a n/a Carpentier et al. (1991)GIS Yes DMA (1533 properties) 18.1 l/s 10% n/a Tabesh and Delavar (2003)SA No 200 homes 0.063 l/s n/a n/a Buchberger and Nadimpalli (2004)ANN No DMA 5 l/s 10% 2.5h Mounce et al. (2007)MNFA No DMA 5 l/s 20% n/a

SE state esitmation; SA statistical analysis; MNFA minimum night ow analysis.

Figure 13. On-line leak analysis. Comparison of pressure traces measured with and without leakage (a). The change indierence between the two traces (a) indicates the presence of a leak. The actual dierence between measured pressures can beanalysed to get better resolution as shown in (b). Adapted from Misiunas et al. (2006, Figures 9, 10).

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Bottom-up leakage assessment methods are stillpreferred to top-down approaches despite the fact thatthey are much more data hungry and time consumingto use. The main value of top-down approaches is seenin making fast system-level leakage assessments butalso in verifying/controlling the results obtained byusing bottom-up methods. A certain novel value is seenin integrating these methods, especially bottom-upmethods, with pressure-driven hydraulic models ofthese systems (e.g., see Giustolisi et al. 2008). Finally,both approaches are expected to benet in the futurefrom explicit uncertainty analysis used to characteriseand quantify the major sources of errors involved inthe leakage assessment process. This should be madepossible with the constant increase in better yetcheaper computational power available.

When it comes to leakage detection methods,signicant advances have been made in the past inboth equipment-based and numerical models. Thehardware based methods (e.g., leak noise correlators)still remain superior in terms of detection accuracy butalso remain much more expensive to use than thenumerical models. Further developments of thepromising equipment-based leak detection methodsare envisaged (e.g., pig-mounted acoustic sensingdevices and/or ground penetrating radars).

With regard to the use of various transient basedmethods for leakage detection it should be noted thatthese methods had limited success so far, typically insimpler pipe systems only. It is envisaged that transientsimulation models need to be developed further beforethey can be utilised for leakage detection and assess-ment in more complex pipe systems.

The further development of other numerical (i.e.non-equipment) based methods is envisaged, especiallythe on-line type methods for real-time detection anddiagnosis of leaks caused by pipe bursts in networks.This should be made possible by the latest develop-ments in the pressure and ow sensor technology

which should enable water companies to install largernumber of more accurate and cheaper devices in thenear future. The latest advancements made in thedevelopment of water quality (e.g., turbidity) sensorscould be potentially utilised too, through additionalinformation available (e.g., turbidity tends to increasesignicantly during pipe burst events). The mostpromising techniques in the context of on-line modelsinclude various Articial Intelligence techniques, e.g.,articial neural networks for pressure/ow signalforecasting, wavelets for signal de-noising and fuzzysets and Bayesian networks for improved inferenceanalysis. Note that the successful development of theabove real-time models will enable merging the leakdetection and assessment techniques, pressure-drivenhydraulic solvers and active leakage control methods.

Finally, an integral part of the above should be thedevelopment of novel sampling design methods forlocating pressure and ow sensors in pipe networks sothat better detection and diagnosis results can beobtained for both background and especially burstrelated leaks.

Acknowledgements

The rst author would like to acknowledge the nancialsupport from the Estonian Science Foundation (ETF7646).

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