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Tennessee’s Asset Management Approach for Pavements

Tennessee’s Asset Management Approach for Pavements. Donald Reid DESIGN DIVISION As revised 03-08-01. GASB 34 Requirements. Inventory of Pavement Assets Condition Assessments Management Strategies. Inventory of Pavement Assets. Interstate System in Tennessee 1073 miles

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Tennessee’s Asset Management Approach for Pavements

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  1. Tennessee’s Asset Management Approach for Pavements Donald Reid DESIGN DIVISION As revised 03-08-01

  2. GASB 34 Requirements • Inventory of Pavement Assets • Condition Assessments • Management Strategies

  3. Inventory of Pavement Assets Interstate System in Tennessee • 1073 miles State Route System (non interstate routes) • Region 1: 2437 miles 154 miles divided • Region 2: 2376 miles 83 miles divided • Region 3: 3185 miles 118 miles divided • Region 4: 2687 miles 106 miles divided Continuous Inventory Updates with TRIMS Transportation Roadway Information Management System

  4. Condition Assessments Data Collection: • Infrastructure Management Services (IMS) • Data Collected: • Roughness, Rutting, Distress • Frequency: • Collect Condition Data on Interstate Every Year • Collect Condition Data on NHS Every Year • Collect Condition Data on STP 1 Region Per Year • Pavement Condition Rating: • PSI-Pavement Roughness Index • PDI-Pavement Distress Index • PQI-Pavement Quality Index

  5. Management Strategies

  6. Management Strategies Goals and Objectives • Interstate System • 90 % of all interstate highways will be in good or very good condition • No more than 10 % of all interstate highways will be in a poor or very poor condition • The current goal is an 8 year resurfacing cycle • State Route System • 85 % of all state routes will be in good or very good condition • No more than 15 % of all state routes will be in a poor or very poor condition • The current goal is a 12 year resurfacing cycle

  7. PavementQuality Report

  8. What’s Next? • Performance prediction models • Structural design optimization • Pavement decision tree validation • Future pavement needs prediction models

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