Risk models in Info360 Asset help you determine the likelihood and consequence of failure for your assets, as well as an overall risk score and grade.
Info360 Asset risk models can help you to:
- Accurately analyze risks to predict failure before it occurs and take proactive actions.
- Monitor current asset condition based on latest inspections.
- Optimize and justify decisions on cleaning, repair, rehabilitation, and replacement strategies and further inspections.
- Prioritize assets to focus resources on those that have the highest risk of failure.
When creating risk models, you can leverage data from:
- Current and historical condition inspections
- Work tasks
- Asset attributes
- GIS spatial layers
- Custom tables
- Simulation results (from InfoWorks WS Pro, InfoWorks ICM, or InfoWater Pro)
Structure of risk models
Risk models in Info360 Asset consist of:
- Likelihood of Failure (LoF) categories. For example, current condition or asset attributes (age, material, etc.).
- Consequence of Failure (CoF) categories. For example, environmental impact or transportation impact.
Each category is made up of components. For example:
- Category
- Components
Components and categories are given weightings, depending on their importance in the risk evaluation. The components in a category must add up to 100% and the categories in LoF or CoF must also add up to 100%.
On the Risk Setup tab, you will define the overall weighting of LoF vs CoF for the model.
See Create Risk Model to learn how to set up a risk model.
Results of risk models
Once you have set up and run your risk model you will get results for each asset indicating:
- The overall risk score
- The total LoF score
- The total CoF score
- The risk grade (ranging from Negligible to Extreme)
- The Risk Trend from one run to another.
You can also see the individual LoF and CoF component scores by exporting the results or by going to the individual Asset Details page.
See View Risk Results for more information.
You can query all of these results in a rehabilitation decision tree.