01152462

Component

https://doi.org/10.3141/2153-13

http://scholar.google.com...Hall&publication_year=2010

The international roughness index (IRI) is the primary index of the Maryland pavement management system and is the data source for performance trend analysis, budget allocation, and project selection. The Maryland State Highway Administration (MDSHA) makes a significant investment to collect, process, analyze, and store IRI data every year over the entire network of the roads. Such reliance on IRI data and investment instigated the investigation of the confidence level embedded in the data and of devising possible sampling scenarios. To evaluate the confidence level, repeatability errors of the measurements were assessed. IRI data were repeatedly collected over a designated test loop under normal operating conditions to mimic network-level data collection. Sampling scenarios were devised using the Monte Carlo method for network IRI data collected in 2008. The network was stratified on the basis of functional classifications. Sampling errors in estimating the percentage of the roads in each of five IRI categories established by MDSHA were evaluated. The results indicated that the repeatability error of the measurements was less than 7%. The sampling results indicated that the state may survey a third of the network each year and expect estimation errors of less than 0.5% for all IRI categories. As such, MDSHA would not incur a considerable sacrifice in the confidence of network condition evaluation.

01320944

10-3262

English

pp 114-120

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309143004

Print

Figures (4) ; Maps (1) ; References (6) ; Tables (4)

Data collection; Monte Carlo method; Network analysis (Planning); Pavement management systems; Roughness

International Roughness Index

Confidence level; Data sampling; Repeatability

Maryland

Highways; Pavements; I23: Properties of Road Surfaces

TRIS, TRB, ATRI

Jan 25 2010 11:37AM

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• Comparison of Flexible Pavement Distresses Monitored by North Carolina Department of Transportation and Long-Term Pavement Performance Program
• Developing an Overall Combined Condition Index for Pervious Concrete Pavements Using a Specific Panel Rating Method
• Development of a Global Positioning System–Based Vehicle Tracking System for the Minnesota Road Research Facility
• Effect of Frequency of Pavement Condition Data Collection on Performance Prediction
• Evaluation Method for Transverse Cracking in Asphalt Pavements on Freeways
• Index of Foreign Object Damage in Airfield Pavement Management
• Integration of "Mechanistic–Empirical Pavement Design Guide" Distresses with Local Performance Indices
• Necessary Assessment of Use of State Pavement Management System Data in "Mechanistic–Empirical Pavement Design Guide" Calibration Process
• Optical Texture-Based Tools for Monitoring Pavement Surface Wear and Cracks Using Digital Images
• Pavement Performance Modeling Using Piecewise Approximation
• Pavement Rehabilitation Project Ranking Approach Using Probabilistic Long-Term Performance Indicators
• Quantitative Performance Evaluation Algorithms for Pavement Distress Segmentation
• Runway Instrumentation and Response Measurements
• Use of Pavement Management Information System for Verification of "Mechanistic–Empirical Pavement Design Guide" Performance Predictions
• Validation and Implementation of Ontario, Canada, Network-Level Distress Guidelines and Condition Rating