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© 2014 Water Research Foundation. ALL RIGHTS RESERVED. © 2014 Water Research Foundation. ALL RIGHTS RESERVED.

Water Research Foundation

Webcast

Benefits and disadvantages of

using ‘no disruption’ repair

techniques

June 3, 2014

© 2014 Water Research Foundation. ALL RIGHTS RESERVED.

Partnership Projects with UKWIR

Long-term Partnership

• Benefits and disadvantages of using ‘no disruption’

repair techniques (#4513)

• Guidance and Strategies for Determining When it is

Cost Effective to Use Condition Assessment

Technologies on High Consequence Water Mains (#4553)

• Renewable energy in the water/wastewater industries

from current lessons to future contributions(#4577)

• Remote sensing for catchment management Phase 2

(#4576)


Benefits and Disadvantages of ‘No Disruption Repair’ Methods

Introduction

Jo Parker, Project Manager


UKWIR manages

collaborative research

on behalf of the UK water

service industry

• All water undertakings in the UK

• Core staff in London

• Active participation by water companies

• Projects managed by water companies or

independent consultants


Context for this research project

• UK Water Industry is highly regulated

• Economic Regulator ‘Ofwat’ monitors financial and customer service

performance

• Water companies have to submit data on the number of customers

without water for more than 3 hours

• This pushes for repair methods which do not require shutting down the

network

• In addition new measures, the ‘Service Incentive Mechanism’ (SIM) push

for better customer service

• Qualitative and Quantitative Measures e.g. Customer surveys, number

of contacts

• Performance affects the level of profit a water company can make in

the following 5 years

© 2014 Water Research Foundation. ALL RIGHTS RESERVED. © 2014 Water Research Foundation. ALL RIGHTS RESERVED.

Project 4513

Benefits and disadvantages of using

‘no disruption’ repair techniques

Paul Conroy

Sue De Rosa

CH2M HILL


Content 1. Background

2. Project Aims and Objectives

3. Approach and methodology 3.1) Information Review

3.2) Data Availability

3.3) Data Collection

3.4) Analysis

4. Decision Support Tool

5. Conclusions

6. Recommendations

‘No Disruption’ Repair Techniques


1. Background: the project need

• Increasing focus on the quality of service

• Increasing pressure to prevent or minimise interruptions to water

supplies

• ‘No disruption’ repair techniques ‘offer’ advantages

However, potential undesirable outcomes of network repair:

1. Disruptive repair activity short term interruptions

2. Non-disruptive repair activity longer term failure recurrence


2. Project Aims and Objectives

• Assess water network repair techniques

in terms of the impact on short- and

long-term network performance

• Provide guidance to improve confidence

in method selection


Risk factors

Asset failure

Network configuration

and operational

response

Customer experience

Impact on system

performance

Probability of failure

Consequences of failure Failure

Risk to performance and service

Need to consider a complex chain of events…



Terminology – in the context of this study

Repairs are classified as ‘no-disruption’ repairs (or non-disruptive

repairs) where the network remains live (although possibly at

reduced pressure) during the repair (e.g. repair clamps)

Repairs are classified as ‘disruptive’ where a network shut occurs.

(e.g. pipe cut-outs)

Repairs included in the analysis were planned and unplanned types



‘No disruption’ techniques - examples:

Repair clamp

Encapsulating collar (for pipe joints)

Valve repacking (by injection)

Repair using line-stopping (by hot-tapping)

Repair using line-stopping (using bag stops)

Repair using line-stopping (by pipe freezing)

Repair using pipe squeezing

Hydrant replacement (in chamber, without network shut-down)

Temporary overlands to effect repair


3. Approach and Methodology

3.1 Information review

3.2 Prepare Data Specification

3.3 Collect and prepare data

3.4 Analytical methods (developed,

tested and deployed)


3.1 Information Review

• Previous UKWIR research

— Techniques for Preventing Interruptions to Customer Water Supplies

12/WM/04/9, 2012)

• Industry journals and magazines:

— Virtually all of the articles and features related to industry service

suppliers’ or manufacturers’ reports

— Consequently bias towards service/product capabilities and a focus on

positive results, rather than disadvantages

• Company reported experience:

— Broad range of methods used

– Some methods only used in a limited way e.g. ‘5 times in last 5-years’

– Data repair type may be available (generic level)

– Some case studies were available


Data availability

• Based on water Industry questionnaire

— Issued to 12 companies

— Enquiry on:

▪ Experience of using a range of repair methods

▪ Information on trials undertaken and case studies

▪ Availability of repair data (accuracy and completeness)

• Summary of repair methods

— Traditional techniques - pipe section replacement

— ‘No disruption’ techniques - repair clamps, repairs using line-stopping

(by pipe freezing, bag stops or hot-tapping)


Data type

Data

availability/

quality

Significance

score Comments

X, Y coordinates Good 3 Essential for ‘cluster’ analysis

Pipe material Good 3

Pipe material may affect:

• Condition of pipe

• Ease/difficulty of repair

• Type of repair method available

Pipe diameter Good 3

Pipe diameter may affect:

• Ease/difficulty of repair

• Type of repair method available

Generic repair

method

Reasonable to

Good 3

Ideally, an actual repair method is required to enable

comparative performance to be evaluated. Generic

method will; however, permit an general assessment

Significance score

1 – non-essential information

2 – can add value to the analysis

3 – essential information

Data availability: Summary of repair data I

Continued…


Data type

Data

availability/

quality

Significance

score Comments

Actual time of

failure Very Poor 1

Not essential to the analysis. Date work order

raised adequate alternative

Time taken to

repair

Reasonable to

Good 2

This is a useful parameter for comparative

assessment between methods

Time network out

of service (if

applicable)?

Reasonable to

Good 3

Needed to assess likelihood of interruptions of a

certain duration

Impact on service

(e.g. specific

type of service

impact, numbers

of customers

affected)?

Good 3 This is essential information for impact

assessment

Valve operations

undertaken as

part of repair?

Variable – Poor to

Good (depending

upon company)

2 This is necessary to evaluate the impact of

valve operations

Data availability: Summary of repair data II


Data availability key issues

• Data Specification prepared and data availability reviewed

• ‘Essential’ data types either reasonable or good

— X and Y coordinates

— Pipe material

— Pipe diameter

— Generic repair method

— Time out of service

— Impact of event

• Actual repair method - not generally available

Conclusion: Information on repair type not ideal, but considered adequate

to commence data analysis


3.2 Data Specification

• 5 short-listed companies (those with greatest potential

for suitable data)

• Data specification developed to support analysis

• Data requested through Data Specification

— Repair activity data (minimum of 5 years of data)

— GIS asset data (mains, communication pipes etc.)

— Levels of service indicators - low pressure, supply interruptions

— Zonal leakage

— Customer contacts data (water quality issues)


Data specification I

Continued…

Data type Data required Inclusions and data range

Repair data

– spatial and

temporal

Time series of network activities:

• System unique ID

• Event type

• Date/time measures

• Spatial location

• Link to asset

• Information on repair technique (e.g.

descriptions/activity codes)

Active and visible events/bursts

(breaks) repairs

All available records (at least the

last 5 years)

Network

asset

inventory

Pipe network assets details (GIS format):

• Unique ID

• Pipe diameter/material/length

• Pipe date laid

• Pipe ground type (aggressivity) (if available)

• Pipe cohort (if applicable)

• Surface type

• Network zonal polygon boundaries

Selected sample of pipes and

DMAs as described above


Data specification II

Continued…


Interruption

to supply

data

Data on incidents of planned and unplanned

interruptions to supply:

• Date/time

• Location XY

• Duration

• Cause

Customer contacts – no water – date/time and location

Planned and unplanned

interruptions and associated

customer contacts

(Same time period as repair

events)

Low

pressure

incident

data

Data on incidents of low pressure (arising from planned

and unplanned events):

• Date/time

• Location and/ or link to address point

• Duration

• Cause

Customer contacts – low pressure – date/time and

location

Planned and unplanned low

pressure incidents and

associated customer contacts


events)


Data specification III


DMA level

leakage data

Leakage metrics for each DMA where available:

• Nightline

• Natural rate of rise

• Average incremental costs

• ALC details (hours)

Zone leakage management – pressure management?

Leakage metrics and activity

Water

quality

incidents

reported by

customers -

spatial and

temporal

• Customer contacts for discoloured water

• Customer contacts for milky water (or equivalent)

• Customer contacts (others relating to aesthetic water

quality – to be discussed)

• For all – date/time and location

Customer contacts relevant to

aesthetic water quality


events)


3.3 Data Collation

• Data collected from 5 water companies

Note: 2 of these covered the whole of the company supply area, 2 covered extensive regions of the company supply area and 1 covered 3 DMA* only

• Data used for various stages in the process

— Data reviewed for suitability (all datasets)

— Data used for method development (1 dataset)

— Data included in detailed analysis (4 datasets)

*DMA: District Metered Area – network zone managed as an independent area of the network


Data received I

• Data from 5 companies (mixture of data types received)

• Repair activity data

— Good coverage (up to 12 years of data) and geocoded (primarily to

property address)

— Generic repair method (‘disruptive’ and ‘non-disruptive’) identifiable

from activity codes

— Lack of data on specific repair techniques used

— Poor link to asset inventory data

• Asset data

— GIS format (ArcGIS, MapInfo), asset attributes (material, diameter)

well populated

— Poor links to repair activity data and asset inventory data


Data received II

• Levels of service data (low pressure, interruptions to

supply)

— Spreadsheet/database format with reasonable geo-coding (to

property address)

— Poor links to repair activity data and asset inventory data

• DMA leakage data

— MNF (minimum night flow) as a surrogate

— Suitable granularity (weekly or monthly time series)


Data preparation I

• Asset data

— GIS processing to determine polyline feature lengths

— Review of attribute data – asset length by material, diameter, era

laid


— Removal of duplicate work orders

— Gap-filling of missing coordinate data (using property address)

— GIS spatial join to link repair activities to nearest assets


Data preparation II

• Repair classification

— ‘Disruptive’ and ‘non-disruptive’ techniques based on activity

codes for main repairs using cut-outs or repair clamps

respectively

Or, to fill data gaps

— High-level assumptions based on operating practice e.g. asset

material and failure type

▪ ‘Disruptive’ – repairs on PVC, AC, GRP mains or those with

split/longitudinal fractures

▪ ‘Non-disruptive’ – repairs on all other mains


3.4 Analytical Methods (developed, tested and deployed)

1. Burst (break)/repair interval analysis within bounded

network areas (in the UK – District Meter Areas (DMAs))

2. Cluster analysis & DMA analysis

3. Consequence analysis

4. Leakage analysis

5. Forensic analysis – a detailed look at a small number of

specific events e.g. valve operations


Classification of Repairs

• Disruptive repairs – where the repair was undertaken using a

‘disruptive’ method

• Non-disruptive repairs – where the repair was undertaken using a

‘non-disruptive’ method

• Non-classified repairs – where the repair method could not be

established because of a lack of recorded information preventing

inference of repair type e.g. no material type


3.4.1 Burst (break) Interval Analysis within bounded

network areas (DMAs)

• Assessing short- to medium-term effects

— For all classified burst/break repairs estimate time to next

burst/repair in DMA (any repair method)

— For the two types of repair separately:

▪ tabulate each burst (break) interval (I) and number of occurrences

(repeats) (R)

▪ determine weighted average time to next burst (break)/repair


Burst/Break interval analysis within DMAs – Example data Chart of weighted average time to next burst (break)/repair by repair

technique

Disruptive 49% of repairs, Non-disruptive 35% of repairs (+16% unclassified)


3.4.2 Cluster and bounded network area (DMA) Analysis

• Identifies hotspots

• Assessing longer-term effects


Cluster Tool Overview

• GIS-based technique for

analysing hotspots of failure

activity on network assets and

how these evolve over time

• Constructs ‘buffers’ or ‘hoops’

around events according to user-

defined input parameters to give

optimum groupings of

bursts/breaks and mains

• Generates spatial outputs or

‘cluster statistics’ e.g. failure

rate (breaks/km/year)


Time Series Classification of Network Events

Dataset separated into an ‘observation’ period and a ‘test’ period to

give as far as possible equal periods of observation and test

Observation period Test period

Short term

Longer term

Medium term


Cluster/DMA Analysis

• Classify clusters/DMAs

— Disruptive repair clusters/DMAs – where all repairs during the

‘observation’ period were undertaken using a ‘disruptive’ method

— Non-disruptive repair clusters/DMAs – where all repairs during

the ‘observation’ period were undertaken using a ‘non-disruptive’

method

— Non-classified repair clusters/DMAs – where repairs during the

‘observation period’ were undertaken using a mixture of

‘disruptive’ and ‘non-disruptive’ methods or where repairs could

not be categorised according to repair type.

Note: ‘Non-classified’ repair clusters/DMAs excluded from further analysis


Cluster/DMA Analysis

• For some datasets, to avoid all clusters/DMAs being ‘non-classified’,

it was necessary to adjust the definitions

• Bounded areas of the ‘disruptive’ and, separately, the ‘non-

disruptive’ clusters/DMAs used as the spatial subjects for the ‘test’

period

Company Classification Definition

A

For company A, there were insufficient ‘disruptive’ repairs to enable ‘disruptive’

clusters to be constructed. Cluster analysis was not possible on this dataset.

Similarly for DMAs

C ‘Disruptive’: Clusters or DMAs with 100% ‘disruptive’ repairs

‘Non-disruptive’: Clusters or DMAs with 100% ‘non-disruptive’ repairs

D ‘Disruptive’: Clusters or DMAs with 60% or more ‘disruptive’ repairs

‘Non-disruptive’: Clusters or DMAs with 60% or more ‘non-disruptive’ repairs

E ‘Disruptive’: Clusters or DMAs with 75% or more ‘disruptive’ repairs

‘Non-disruptive’: Clusters or DMAs with 75% or more ‘non-disruptive’ repairs


Disruptive and non-disruptive cluster burst/break rates during test period

Clusters DMAs 54 Disruptive and 13 Non-disruptive 78 Disruptive and 32 Non-disruptive

Example Data


Cluster and DMA Analysis – Observations • Variable post repair performance

* Note small sample size

Dataset Analysis Unit Outcome – D v. NonD No. D No. NonD

Company C

Clusters No statistical difference 54 13

DMAs Disruptive > Non-disruptive 78 32

Company D


DMAs No statistical difference 10 474

Company E


DMAs Non-disruptive > Disruptive 10 195

Company A

Clusters No analysis possible 0 397

DMAs No analysis possible 0 999


Cluster and DMA Analysis – Conclusion

• The evidence obtained supports the conclusion that the

type of repair technique (‘disruptive’ vs ‘non-disruptive’)

does not markedly or consistently influence the post-

repair network performance, other things being equal

Differences D v. NonD:

• Not marked

• Not consistent in direction

• In general, not statistically significant and where they are they

act in contrary directions


3.4.3 Consequence Analysis

Analysis aimed at quantifying serviceability and customer

impacts:

• Unplanned interruptions to supply

• Customer service contacts

• Leakage

Note:

78 ‘disruptive DMAs

32 non-disruptive DMAs


Consequence Analysis – interruptions to supply

• Distance constraint (within same bounded network area) 100m to

500m

• Time constraint of repair date of +/- 3 days


Consequence Analysis – Discoloured Water

• Distance constraint (within same bounded network area) 100m to

500m

• Time constraint of repair date of +/- 3 days


Consequence Analysis – summary findings

• Interruptions to supply: ‘Disruptive’ DMAs slightly more interruptions

to supply than ‘Non-disruptive’. Effect of valve operations?

• Discoloured water: ‘Disruptive’ DMAs no discoloured water

complaints. Customers notified of activity, therefore less likely to

complain

Note: It has not been possible to separate the consequences of

original failure from the consequences of repair


3.3.4 Leakage analysis

Example Data

Dataset No DMAs

Disruptive 32

Non-Disruptive 35

Unclassified 714


Leakage analysis - summary findings

• Findings suggest that ‘Disruptive’ DMAs are performing differently to the

‘Non-disruptive’ DMAs – data reveal different trends over analysis period

• Possible reasons for this were explored through consideration of the DMAs

constituting the sub-set, but no obvious explanations could be found

Potential factors which could impact are:

— Mains material profile

— Mains rehabilitation activity

— Scale of Active Leakage Control

— History of zone maintenance, e.g. pressure management

• The limited scale of this analysis did not permit isolation of the impact of

each of these factors on leakage and hence the effect of repair technique (if

any) could not be established


3.4.5 Forensic Analysis

• Used for valve operations

• Information available from risk assessment forms – permits to work,

safe operation assessments and similar

• Process

— Identify date of valve operation

— Identity valve operation locations (from GIS)

— Identify number of valve operations

— Interrogate repair, contacts and interruptions to supply datasets to

establish whether an ‘incident’ (e.g. interruptions, discoloured water

complaint) has occurred on the day of the valve operations or during the

following 7 days

— If incident recorded, locate incident in relation to valve operation


Forensic analysis - observations

Forensic Analysis

Valve

Operation

Reference

Number of

valves operated

No of additional

breaks/repairs

over next 7

days

No of interruption

incidents over next

7 days

No of discoloured water contacts

over next 7 days

1 4 0 0

3

(2 on day of operation and1 on

next day – up to 60m away

from closest valve activity)

2 4 0 0 0

3 1 0 0 0

4 1 0 0 0

5

1

0 0 0


4. Decision Support Tool (DST) I

• DST Mk1 developed in Part 1 of Project

• MS Excel environment

• Provides a framework for the decision making process of

repair technique selection

• Supports decisions on the technical applicability and

cost-benefit of ‘under pressure’ techniques

— Most suitable techniques for a given situation (based on input

parameters e.g. pipe material, size, operating pressure)

— Comparative costs (repair and supply interruptions) against

equivalent ‘traditional’ techniques


4. Decision Support Tool (DST) II

• DST Mk2 by CH2M HILL

— Improved user interface (data entry form)

— Improved functionality (overwrite default cost data with latest

available cost data)

— Improved visualization of results (charts to compare ‘under

pressure’ vs ‘traditional’ techniques)


5. Overview of Findings

1. Analytical methods suitable for quantifying effects

2. No consistent or marked difference evident between post failure performance for ‘disruptive’ vs ‘non-disruptive’

3. Greater precision in the analysis would be possible with improved ‘tagging’ of technique to repairs

4. Forensic analysis was found to be too ‘site-specific’ and was considered unsuitable for reaching broad conclusions based on the number of investigations feasible under the scope of this study

5. A limited assessment of the impact of repair method on leakage performance revealed some interesting differences between the two zone classifications (D v. NonD) but other explanatory variables may be operating and their effects could not be isolated

6. We believe that network conditions are very important in terms of whether or not activity will produce adverse effects, therefore companies are advised to examine their own data sets

http://www.google.co.uk/imgres?imgurl=http://www.2lbin.com/images/copperpipelinestoppingtappingsaddles.jpg&imgrefurl=http://www.2lbin.com/tappingsaddles499copperpipelinestopservicesaddles.shtml&usg=__ygkdImOsngfj26uwBpAbwtX5c0o=&h=862&w=800&sz=72&hl=en&start=74&um=1&itbs=1&tbnid=9P27ekWy-EvzfM:&tbnh=145&tbnw=135&prev=/images?q=line+stopping&start=60&um=1&hl=en&sa=N&ndsp=20&tbs=isch:1


6. Recommendations (I)

• Asset data — Gap-filling of missing attribute data (material, diameter, install

date)

— Confidence grades for attribute data

— Standardisation of units of measurement (e.g. pipe diameters)

• Repair activity data — Transfer of existing records to GIS format

— Geocoding of new repairs (physical location or nearest feature)

— Gap-filling of missing coordinate data based on available data (property address)

— Confidence grades for geocoding

— Link repair to asset (asset ID)

— Gap-filling of link to asset using spatial analysis in GIS


Recommendations (II)


— Activity codes for specific repair techniques

— Supporting data for inferring repair classification e.g. mains

shutdown, asset failure type

• Levels of service data

— Transfer of existing records to GIS format

— Geocoding of new incidents (full address)

— Gap-filling of missing coordinate data based on available data

(property address)

— Link incident to asset (asset ID) or repair activity (job number)


Thank you

Any Questions?

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Water Research Foundation · PDF fileWater Research Foundation Webcast ... Risk factors Asset failure Network ... (ArcGIS, MapInfo), asset attributes (material,