Abstract
The collection of sufficient, accurate, and consistent pavement condition data is essential to an effective pavement management system. Condition data drive a variety of pavement management tasks such as:
• Predicting future pavement performance
• Identifying current and future maintenance and rehabilitation needs
• Estimating budget needs and requirements
• Reporting to decision makers
• Selecting appropriate pavement management tools
Pavement condition data are represented at either the distress level or overall condition level. Common indices representing overall pavement condition include:
• Pavement Condition Index (PCI)
• Present Serviceability Index (PSI)
• International Roughness Index (IRI)
• Pavement Surface and Evaluation Rating (PASER)
Session Title
Student Poster Session
Location
Purdue Memorial Union
Date of Version
March 2018
Recommended Citation
Montgomery, Sharlan R. and Haddock, John E., "Factors Affecting Pavement Surface and Evaluation Rating Accuracy and Variability" (2018). Purdue Road School. 3.
https://docs.lib.purdue.edu/roadschool/2018/posters/3
Start Date
3-6-2018 11:00 AM
End Date
3-6-2018 1:50 PM
Factors Affecting Pavement Surface and Evaluation Rating Accuracy and Variability
Purdue Memorial Union
The collection of sufficient, accurate, and consistent pavement condition data is essential to an effective pavement management system. Condition data drive a variety of pavement management tasks such as:
• Predicting future pavement performance
• Identifying current and future maintenance and rehabilitation needs
• Estimating budget needs and requirements
• Reporting to decision makers
• Selecting appropriate pavement management tools
Pavement condition data are represented at either the distress level or overall condition level. Common indices representing overall pavement condition include:
• Pavement Condition Index (PCI)
• Present Serviceability Index (PSI)
• International Roughness Index (IRI)
• Pavement Surface and Evaluation Rating (PASER)
