R-PMS System Use Cases : Model . R-PMS System - EPMS/KANPAV : System
Use Case - KANPAV-03 Estimate Remaining Life
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| Properties |
| Name | Value | ||||||||||||||
| Description |
This Use Case is used for the Calculation of the Estimated Remaining Life of each segment per the current Segment State Vector |
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| Id | UC30 | ||||||||||||||
| Abstract | false | ||||||||||||||
| Leaf | false | ||||||||||||||
| Root | false | ||||||||||||||
| Stereotypes | UseCase, Modeling | ||||||||||||||
| Justification | A primary requirement for optimization of the system is to do an initial estimation of the remaining life of each segment or pavement category in the Network | ||||||||||||||
| Business Model | false | ||||||||||||||
| Primary Actors | Actor - KDOT PMS Modeler | ||||||||||||||
| Status | Identify | ||||||||||||||
| Rank | Unspecified | ||||||||||||||
| Relationships Summary |
| Use Case Note |
| ■ Inputs and Prior Actions | |||
| ■ Actions | |||
| • for each segment | |||
| ♦ Grab the Current Segment State Vector: | |||
| ♦ Current Condition, | |||
| ♦ Last Major Action(LMA) | |||
| ♦ Predicted Future Conditions under Nominal Actions, | |||
| ♦ Thresholds for good to fair and fair to poor | |||
| ♦ Traffic Vector | |||
| ♦ Prediction process (Monte Carlo or ML) | |||
| ♦ Now | |||
| ♦ Next cycle | |||
| ♦ Threshold crossings for each variable | |||
| ♦ Earliest crossing determines remaining life | |||
| ♦ Utilize ML Algorithms in concert with or separately to Monte Carlo Calculations on existing sampled data | |||
| ♦ Result is a weighting on Remaining life before doing something either light or heavy | |||
| ■ Outputs | |||
| • Remaining life prior to actions | |||
| Actions |
| Steps | |||
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1. |
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| 2. SYSTEM for each segment | |||
| 2.1. Grab the Current Segment State Vector: | |||
| 2.1.1. Current Condition, | |||
| 2.1.2. Last Major Action(LMA) | |||
| 2.1.3. Predicted Future Conditions under Nominal Actions, | |||
| 2.1.4. Thresholds for good to fair and fair to poor | |||
| 2.1.5. Traffic Vector | |||
| 2.1.6. Predicted Future Conditions | |||
| 2.2. Prediction process | |||
| 2.2.1. Now (Steady State for Baseline) | |||
| 2.2.2. Next cycle | |||
| 2.2.3. Threshold crossings for each variable | |||
| 2.2.4. Earliest crossing determines remaining life | |||
| 2.2.4.1. Utilize ML Algorithms in concert with or separately to Monte Carlo Calculations on existing sampled data | |||
| 2.3. Merge Remaining life prior to actions into current Segment State Vector | |||
| Details |
| Name | Value | ||
| Level | Subfunction | ||
| Complexity | High | ||
| Use Case Status | Initial | ||
| Implementation Status | Scheduled | ||
| Preconditions | |||
| Post-conditions | |||
| Author | Rick Miller and Jerry W. Manweiler, Ph.D. | ||
| Assumptions | |||
| Tagged Values |
| Name | Type | Value | |||
| Level | Ad Hoc Enumeration | ||||
| Complexity | Ad Hoc Enumeration | ||||
| Use Case Status | Ad Hoc Enumeration | ||||
| Implementation Status | Ad Hoc Enumeration | ||||
| Preconditions | Multi-line Text | ||||
| Post-conditions | Multi-line Text | ||||
| Author | Text | ||||
| Assumptions | Multi-line Text | ||||
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| File Name | Description | ||
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| Abstract | false | ||||||||||||||||||||||||||||||
| Final Specialization | false | ||||||||||||||||||||||||||||||
| Leaf | false | ||||||||||||||||||||||||||||||
| Visibility | Unspecified | ||||||||||||||||||||||||||||||
| Derived | false | ||||||||||||||||||||||||||||||
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| Stereotypes | Include | ||||||||||
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