CHAPTER 2 LITERATURE REVIEW
2.4 FACTORS AFFECTING THE PERFORMACE OF A WATERMAIN
2.4.2 FAULT ANALYSIS
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lfm Mains Infrastructure Serviceability Factor Lm Length of mains (km)
c The average contribution to leaks from communication pipes and fittings (l/connection/hr) to NRR
Lfc Communication pipe and supply pipe serviceability factor Nc Number of connections
Df Network disturbance factor
Thornton et al. (2008) recommends that careful analysis is carried out to determine where the problems with the water mains lie and that the Utility should be able to determine if the losses are predominantly on the water mains, the connections, the connection pipes, on the consumer’s premises or are in fact associated with meter tampering. This analysis will ensure that the proposed solution will correct the problem and that this solution is implemented at minimum cost.
Deb et al. (2002) has made recommendations for 45 data items to be captured for each main break which illustrates the complexity of the problem.
UKWIR (2002b) lists, after close collaboration with 23 water utilities in the UK (with 362 000km of water mains worth an estimated £30bn) a number of standards that can be used for the recording of faults. The benefits for adopting this robust approach will provide a better understanding of the trends of failures, will inform the need for spending, allows the utilities to compare themselves both internally and externally, enables better customer service and provides feedback to those specifying pipe and related material. To allow utilities to participate at various levels and not be too prescriptive, the data has been broken up into three categories that are deemed Essential, Preferable and Desirable. The standardised list of failures is detailed below:
Failure Types:
o Joint (JO) o Ferrule (FE)
o Circumferential (CI) o Longitudinal (LO) o Pin Hole (PH) o Gasket/Bolts (GB) o Electro-fusion (EF) o No Failure (NF) o Other (OT) o Unknown (UN).
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UKWIR (2002b) admit that it is a laudable aim to ascribe the cause of failure to each fault but agree that in many cases analysis would be required by an expert in order to be fully confident of being correct. The causes of failure listed are the following: Pressure, corrosion, faulty product, point load, ground movement, 3rd party damage, poor bedding erosion, aging rubber seal, grit in seal, surge event, and poor installation. The user is cautioned when using these causes of failure as there is a need to be cognisant of who has recorded the data, what training they have received and what data validation has taken place.
Surface Classifications:
o Field (FD) o Footpath (FP) o Verge (VE)
o Highway – Heavy Traffic (HH) o Highway – Light Traffic (HL) o Free Standing Within Duct (DU) o Unknown (UN).
Soil Types:
o Clay (CL) o Gravel (GR) o Peat/Loam (PT) o Rock (RK) o Sand (SA) o Chalk (CH)
o Made Ground (MG) o Other (OT)
o Unknown (UN).
Detection Methods:
o Reported / Reactive (C)
o Unreported/Proactive Detection (L).
Troterotot (2009) recommends that the storage and management of adequate real time data through the life of the assets regarding the operations, the failures and their impacts will pay back over time for taking operational as well as planning decisions.
Following the work of Ellison (2001) and Deb, Hasit and Grablutz (1998), proposed that the
20 following infrastructure indicators are monitored:
• Physical
• Repair History
• Leaks
• Inoperability
• Unaccounted for Water.
Knowledge of this data will allow the Utility to asses “Infrastructure readiness” and the likelihood that the system will fail and also the rate of degradation. AwwaRF (2005) further recommend that the following Operating indicators are monitored:
• Flow
• Water quality
• Pressure
• Customer data
• Interruptions
• Energy use
Knowledge of this data will allow the Utility to asses “Operational readiness” and whether the system is providing the desired quality and quantity of water. It is difficult to assemble all this data and have confidence in its integrity. Ellison’s (2001) report explains how this process can be simplified in the first instance and system capacity and reliability can be evaluated. He admits that no single variable can measure system readiness and suggests that the following be initially measured. He further reports that of all the potentially available data, the burst history report is the most valuable.
Table 2.2: Suggested Initial Variables for System Condition Assessments (Ellison, 2001)
A composite condition index much like a report card could be compiled for each DMA. This simplistic method would not suffice however where multi-attribute measurements are required for system assessments as there is no single best approach to compile such an index. In fact, it is believed that there are similar concerns with seemingly random weightings that some users apply to decide on which mains will be replaced.
VARIABLE Break and leak frequency Non-Revenue Water Renewal Rates Renewal Costs
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AwwaRF (2005) recommend that the indicators of the component and system conditions shown in Table 2.3 are collected to assist with the prediction of failures.
Table 2.3: Variables that predict pipe failure (AwwaRF, 2005)
Wood and Lence (2009) reported that the amount of water main fault data needed for extensive analysis is generally not available in the majority of water utilities. Most municipalities only have limited fault data and this is often difficult to analyse. (Pelletier et al. 2003) Furthermore, unless rigorous training and quality control has been undertaken, the accuracy of the data is also often questionable. Wood and Lence (2009) report however that often utilities can analyse data from other sources such as archives and models. The typical data that is used by models to predict mains failures and their surrogates are listed in Table 2.4. Wood and Lence state that “If a utility has sufficient data and the requisite skill and ability, it may be able to apply such knowledge discovery techniques to detect patterns, although for most utilities, rudimentary statistical analyses by technical staff are sufficient for detecting patterns in their data.
Additional information may often be obtained efficiently at the time of the break repair by revising forms to collect more information, such as bedding or backfill material”
MATERIAL AND INSTALLATION
ENVIRONMENTAL CONDITIONS
SERVICE
CONDITIONS VULNERABILITY
Poor original mate rials Corrosive soils Corrosive water Excavations
De fects Ground move ment Wate r te mpe rature Trenchless intrusions
Age Fre ezing Traffic loads Disasters
Type of mate rial Stray currents System pre ssures Cross connections
Poor be dding Wate r hammer Nearby loadings
Weak joints Se rvice history
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Table 2.4. Data Used in Models and Surrogate Factors (Wood et al., 2009)
Surrogate Factor
Age Method of pipe manufacture, construction standards, deterioration over time
Pipe material Construction practices, method of manufacture, failure mechanisms and causes, joint failures
Pipe diameter Wall thickness and resistance to beam loading, pipe use, method of pipe manufacture, construction standards
Type of pipe lining Method of pipe manufacture, resistance to corrosion Bedding and backfill
material
Physical stress on pipes caused by construction practices, structural resistance, soil type, fines migration
Pipe protection wrapped/anodes
Structural resistance, life expectancy, construction practices, method of pipe manufacture
Pipe condition Remaining life
Soil type Soil corrosivity, physical loading on the pipe such as swelling and frost, level of pipe protection, ground water effects, construction practices, bedding and/or backfill material
Under a roadway Physical loading from surface loads such as traffic, road salt effects Depth of cover Physical loading on the pipe from the weight of soil
Surface material/type Physical loading from surface use Normal operating
pressure
Internal pressure on pipe structure Typical flow in area of
break
Physical impact from factors such as accelerated internal corrosion from low flow mains, water hammer effects
Traffic classification Physical loading from surface loads such as traffic volumes and wheel loads
Dandy et al. (2001) state that the water main replacement strategy can be driven either by economic, reliability, or water quality factors. A genetic algorithm can be developed to multiple criteria problems but examining the economic criterion alone ensures that the distribution system is efficiently run, that capital costs are optimised and operational and maintenance costs are minimised. In order for this analysis to be reliably conducted, all costs borne directly by the water authority and as well as those costs borne indirectly by the community need to be accurately known.
23 2.4.3 FAILURE MECHANICS
O’Day et al. (1986) reports that it is important to understand the causes of failure in a system because all systems are unique and a solution that is applicable in one system will not necessarily transfer to another system. Pipes will fail by different mechanisms, failures can occur singly or in multiples, and failures can be linked to a number of circumstances including human error, construction activities, routine service conditions, installation conditions and manufacturing defects.
Ellison (2001) states that pipes can be divided into critical and non-critical groups by using a weighted score approach. The weak point of this approach is that the underlying data is usually inadequate to yield high confidence in the results. Typical attributes that are tracked are leak history, importance of the pipe in the system, diameter, material type, soil corrosivity, damage potential, and pipe age.