Risk-based, data-driven sewer management
How can sewer managers apply fit-for-purpose asset management techniques to implement risk-based, data-driven, efficient and effective sewer management?
Context and problem:
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Aging infrastructure: Many sections of the sewer systems are aging, leading to concern that they may malfunction and need repairs.
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Rising costs: Repairing and upgrading aging sewer infrastructure requires substantial financial investments, straining municipal budgets.
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How to set priorities?: Deciding which projects to prioritize requires a better understanding of the risks.
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Staffing and knowledge shortages: Many municipalities face a lack of knowlegeable professionals with the expertise to manage and modernize sewer systems effectively. Succession planning is needed, but knowledge transfer from experienced professionals adds to the demands on their time.
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Integrated work: Coordinating sewer upgrades with road maintenance, green infrastructure and other utilities can save costs and improve urban sustainability.
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Climate effects: Extreme weather events like droughts and heavy storms stress sewer systems, requiring resilient designs to handle fluctuating water volumes.
Basis for a Solution
Develop a risk-based, data-driven framework to support the sewer manager, providing:
- an objective, uniform approach to prioritisation and cooperation with other municipalites
- structured analyses and presentation of the data, aligned with the core values of the municipality
- knowledge capture to enable transfer to new sewer managers
Risk-based evaluation using FMEA and extensive data.
Risk = Consequence of malfunction × Probability of malfunction
Core values of municipalities
- Safety
- Health/Environment
- Accessibility
- Availability
- Living quality
- Costs
- Image
Consequence per core value, based on proxies
- Pipeline type
- Location
- Diameter
- Depth
- Road classification
- Company
- Railway
- Harbour quay
-Material
-…..
Probability of malfunction
When is there a higher chance of malfunction?
- Inspection data
- Norm NEN-EN 13508-2
- Inspection observations
- Quality classes
- Experieince of experts
--> Probability levels
The Risk Matrix
Per sewer section, determine the consequences of flow, stability and containment malfunctions and the likelihood of malfunction based on the quality observations from inspections.
Based on 6 levels of consequences and 6 levels of probability, the risk ranges between 1 and 36.
Consequences
.....proxies ........................
Likelihood of Malfunction
Inspection data..........
Geographic Information System (GIS) representation of results
Results are exported from the analyses scripts in GIS format to capture all data on a map of the area. GIS enables a multitude of operations on the data, to view the output variables at various levels of detail.
