1. Risk-Based Coastal Asset Management
Adam Hosking
Louise Trim
Ben Hamer
Richard Harpin
Gulf Coast Hurricane Preparedness, Response, Recovery & Rebuilding Conference.
Mobile, AL. November 11-14, 2008

2. Outline
UK Strategic Risk Analysis
Applications of approach
Asset Management System
Conclusions

3. Strategic Risk Analysis in the UK

4. Risk Management - Background
In UK 5 million people in 2 million homes live in flood risk areas
Over 120,000 residential and commercial properties at erosion risk
Significant geographic variation
Widespread flooding experienced in 1998 and 2000
“Lessons Learned” led to an enhanced emphasis on flood risk management
UK Government Flood & Erosion Risk Management Objective:
In order to ensure the best use of public money, target spending in those areas of greatest flood risk, whilst seeking to maximise the return on investment in terms of reduction of flood risk.
Erosion figures shown are from Defra’s National Appraisal of Assets at Risk Study (2002) which suggested that £7.7b of capital assets were at risk from coastal erosion. That figure was low compared to flood, but nevertheless a substantial financial risk to our nation.

5. Risk Assessment for Strategic Planning (RASP)
Structured approach to the analysis and management of the flooding system
Focus on risk, explicitly recognising that defences and flood plains perform as ‘systems’
(HR Wallingford, 2001)

6. Performance of flood risk structures represented as ‘fragility curves’
Analysis Philosophy
Figure 1:
Halcrow have been involved in two studies related to improving the prediction of the flood pathway within the risk based framework.
The pathway is defined by fragility curves. A fragility curve is used to define the relationship in the framework between the ‘source’ or ‘loading’ and the probability of failure. Where probability of failure is related to the failure to perform a flood risk role ie., breach or overtopping.
Fragility curves are developed for the five key condition grades ranging from very good to very poor
Performance of flood risk structures represented as ‘fragility curves’
Relates loading on an asset to the conditional probability of failure of that asset given that loading.

7. Predicting to flooding pathway
True Fragility Curve
RASP
Figure 2:Fragility curves illustrate the relationship between the loading on an asset and the conditional probability of failure of the asset given that loading.
Based on: field inspections, empirical models, limit state equations, expert judgement
Developed for approx. 60 generic asset types

8. Asset Condition Grades
Fragility curves are developed for the five key condition grades ranging from very good to very poor
Five condition grades
Based on asset performance, not just traditional condition.

9. Receptor The Analytical Approach Pathway Source Pathway
Load
Probability
Source
Damage
Receptor
Depth
Probability of failure
Load
Pathway

10. Performance-based Asset Management System (PAMS)
PAMS project:
Developing performance-based approach to identifying and prioritizing management of flood defences
Ongoing studies, including:
Improved failure mode analysis to determine key performance features, and hence condition grade
Local applications to determine site specific fragility curves
Fragility curves are developed for the five key condition grades ranging from very good to very poor

11. Applications of approach

12. Tiered Approach
(HR Wallingford, 2002)

13. Application of RASP Set-up within GIS National Assessments:
2002, 05, 06 & 08
2004 Foresight Future Flooding
Used to inform policy and decision making
Provide consistent national flood risk mapping (public access)
Local scale assessments:
Project development
Assist in the planning maintenance and replacement of assets

14. Asset Management System

15. Asset Management System (AMS)
Based on existing GIS data platform (SANDS)
Import, visualize and manipulate asset data
Basis for consistent data capture
Record asset types and condition, hence fragility
Identify asset systems
Link assets to risk areas and receptors
Use cost models to optimise investment strategies

16. Key Components of System
Base mapping
Asset inventory
Relationship between loading and asset condition (fragility curve)
Cost models for upgrading from different condition grades (hence improving performance) for a given asset type
Flood extent and receptor data

17. System Components

18. System Components

19. System Components

20. System Components

21. System Components

22. System Components

23. Ongoing Development
Use of optimization engine to automate decision-making
Tool to provide user with top ten outcomes (against multi-criteria objectives), to enable considered selection of preferred option
Possible development for use at higher, regional & national level to inform policy makers and budget setters

24. Conclusions Benefits of Risk-based Asset Management:
provides for systematic and co-ordinated approach to asset management
requires comprehensive asset information (facilitates collection)
focus on asset function and reduction of risk
avoid surprises (physical or financial)
evidence-based decision-making
AMS benefits:
built on established database platform
consistent data storage and analysis
facilitates improved decision-making
maximize risk management benefits from finite budgets

25. THANK YOU! Adam Hosking Tel: (813) 876 6800
Halcrow Inc, Tampa, FL
Tel: (813)