Infrastructure Investment Management Mike Morrisroe Business Area Manager Water Networks

Knowledge - Planning - Implementation Planning Implement Knowledge Optimise using risk - cost & service constraints Continuous base data improvement Track costs and effects of implementation, improve data Infrastructure Risk Management (IRM) Water & Waste Water Networks Risk based assessment Bottom up approach Investment planned at asset level Transparent process Use of constraints Supports Continuous improvement

UITS Duration Model UITS Probability UITS > 6hrs Probability UITS > 12hrs Probability UITS > 24hrs Dependent Customers Impact Methods xxx + + PROBABILITYCONSEQUENCERISK ×= Burst Model Burst Rate Implement Plan > Serviceability = IRM is a modular approach to optimising investment and serviceability

Burst probability model Sample of predictor variables  Length (offset)  Material  Diameter  Region  Customer density (large variability)  Pressure(AZNP)  Customers/metre (large variability)  Temporal and environmental  Others not selected… we started with ~ 20! PROBABILITYCONSEQUENCERISK ×=

Burst probability model Material and age strongly correlated  Two models were developed –Current state of pipes –Future deterioration PROBABILITYCONSEQUENCERISK ×=

Burst probability model Good correlation with raw burst data PROBABILITYCONSEQUENCERISK ×=

Serviceability UITS Duration Model UITS Probability UITS > 6hrs Probability UITS > 12hrs Probability UITS > 24hrs + + Dependent Customers Impact Methods x IRM is a modular approach to optimising investment and serviceability PROBABILITYCONSEQUENCERISK x= Burst Model Burst Rate =xx

UITS models Servicability score depends on UITS duration  function of: –UITS events > 6 hours –UITS events > 12 hours –UITS events > 24 hours  Requires pipe-level probability of UITS duration to calculate overall Servicability Probability UITS > 6hrs Probability UITS > 12hrs Probability UITS > 24hrs PROBABILITYCONSEQUENCERISK ×=

UITS models Repair durations follow lognormal distribution PROBABILITYCONSEQUENCERISK ×=

UITS models Duration function of material and size PROBABILITYCONSEQUENCERISK ×=

Serviceability UITS Duration Model UITS Probability UITS > 6hrs Probability UITS > 12hrs Probability UITS > 24hrs + + Dependent Customers Impact Methods x IRM is a modular approach to optimising investment and serviceability PROBABILITYCONSEQUENCERISK x= Burst Model Burst Rate =xx

The VCM module automatically assesses the criticality of each asset on the network model during a single batch run MWHSoft Developed The InfoWater Pipe/Valve Criticality Module (VCM) PROBABILITYCONSEQUENCERISK ×=

All Mains / All Valves Model NB hydraulically isolated but not part of pound UPSTREAM POUND DOWNSTREAM POUND COMBINED POUND ASSESSED VALVE Each valve can be operated to shut in an area of the network upstream or downstream of the valve (known as pounds) The upstream or downstream pounds may have a different customer impact If the valve fails to operate then both areas (combined pound) will need to be closed in to isolate the network Pipe/Valve Criticality Theory PROBABILITYCONSEQUENCERISK ×=

Properties Affected By Valve Closures – Isolated, No Water Or Low Pressure Identifies Affected Properties PROBABILITYCONSEQUENCERISK ×=

Assigning impact - five customer types D1 Domestic D2 Commercial D3 Hospital D4 Large D5 Sensitive Five customer types  Geo-referenced  Assigned to mains  Grouped by pound (see later) OPA Properties = D1 + D2 + D3 + D4 + D5 PROBABILITYCONSEQUENCERISK ×=

%1.0%2.0% 3.0% 4.0%5.0% 6.0% 7.0% 8.0% 9.0%10.0% NUMBER OF PROPERTIES AFFECTED PROPORTION OF ALL ASSETS HIGH IMPACT ASSETS AS A % OF ALL ASSETS Sum of Isolated, No Water & Low Pressures Properties Valves With Initial Flow And No Flow All Assets Diameter > 200mm Diameter > 450mm Identify Critical Assets PROBABILITYCONSEQUENCERISK ×=

Serviceability UITS Duration Model UITS Probability UITS > 6hrs Probability UITS > 12hrs Probability UITS > 24hrs + + Dependent Customers Impact Methods x IRM is a modular approach to optimising investment and serviceability PROBABILITYCONSEQUENCERISK x= Burst Model Burst Rate =xx Implement Plan > Serviceability

Many GIS elements make a pipe string  Pipe strings –Same diameter –Same material –Same age –Same contractor –Same ground conditions –Same lifetime pressure –Connected elements  Same deterioration rate?  Same replacement date?  Intelligent cohorts? PROBABILITYCONSEQUENCERISK ×=

Risk at GN pipe level PROBABILITYCONSEQUENCERISK ×=

Selected Pipes (superstrings) PROBABILITYCONSEQUENCERISK ×=

Superstrings are suboptimal… but in reality… PROBABILITYCONSEQUENCERISK ×=

Optimised Capital Expenditure Budget Serviceability

Knowledge - Planning – Implementation Business as Usual Planning Implement Knowledge Infrastructure Risk Management (IRM)

Infrastructure Investment Management Mike Morrisroe Business Area Manager Water Networks

 Scottish Water – Mark Petrie, Greg Hay  Thames Water – Rob Casey  MWH – Tim Watson, Chris Bros, Robert Gaskin  MWHSoft – Paul Boulos Acknowledgements