R-PMS System Use Cases : Model . R-PMS System - Q3 : System
Use Case - T08-02 Run PMS for 1R Candidates link
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Name | Value | ||||||||
Description |
Do the full modeling activities for the 1R processes. - See AECOM epmsHelpPart20220119100157200 for more information |
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Id | UC81 | ||||||||
Abstract | false | ||||||||
Leaf | false | ||||||||
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Stereotypes | 1R Processes | ||||||||
Business Model | false | ||||||||
Status | Identify | ||||||||
Rank | Unspecified |
Relationships Summary |
Name | Begin | End | |||
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PMS Current |
Steps | |||
1. ![]() |
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2. SYSTEM Executes the AECom modeling based upon the input parameter space | |||
3. ![]() |
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4. SYSTEM Executes the AECom modeling based upon the input parameter space | |||
4.1. Convert Optimized Projects by Road Category Scopes into Projects by Project Scopes | |||
4.2. Generates modeling output data for use in T10-5 (list of project locations and scopes w/ year recommendations (2,3,4)) | |||
4.3. JWM - See Powerpoint for data elements generated |
R-PMS Desired Opt A |
Steps | |||
1. ![]() |
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1.1. SYSTEM Executes the KanPav/Genetic modeling and measures based upon the input parameter space | |||
1.1.1. Alternative data items used in modeling include: | |||
1.1.1.1. Federal Surface Condition definitions | |||
1.1.1.2. Transverse Cracking | |||
1.1.1.3. Joint Distress | |||
1.1.2. Alternative models for the above three elements with a baseline of AECom | |||
1.1.2.1. Genetic Machine Learning Algorithm | |||
1.1.2.2. TBD Algorithm | |||
1.1.3. Alternative Action Groups | |||
1.1.3.1. Actions with respect to Light, Medium, Heavy | |||
1.1.3.2. Actions with respect to Cost | |||
1.1.4. Triggers for Actions | |||
1.1.4.1. Time and/or condition based | |||
1.2. Generates Overall Network Condition and Cost with breakout of Interstate and non-Interstate | |||
1.3. Does not generate Candidate Project List (CPL) |
R-PMS Desired Opt B |
Steps | |||
1. ![]() |
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1.1. SYSTEM Executes the KanPav/SimPav modeling and measures based upon the input parameter space | |||
1.1.1. Any Sufficiently advanced technology is indistinguishable from magic | |||
1.1.2. JWM | |||
1.1.3. Utilization of all historical data per segment per project as a simulation of the Kansas Highway Network | |||
1.1.4. Solving the PDE's recognizing the district and local dependencies | |||
1.2. Generates Overall Network Condition and Cost with breakout of Interstate and non-Interstate | |||
1.3. Generates Candidate Project List (CPL) |
R-PMS Opt C |
Steps | |||
1. Vender defined Trigger based selection for Candidate Project List generation |
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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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