01475986

Component

https://doi.org/10.3141/2339-14

http://scholar.google.com...alho&publication_year=2013

This investigation assessed the sensitivity of Mechanistic–Empirical Pavement Design Guide (MEPDG) outcomes to normalized axle load spectra representing various loading conditions observed in the Specific Pavement Studies Transportation Pooled Fund Study of the Long-Term Pavement Performance program. The goal was to determine what vehicle classes and axle types with a wide range of axle loading conditions are likely to cause differences in pavement design outcomes when the MEPDG is used. Significant differences found in the MEPDG outcomes support the need for characterization of axle loading beyond a single default value for heavy trucks that dominate vehicle class distributions, especially for Class 9 trucks. The absence of differences for lightweight and underrepresented trucks indicates that load spectra from various sites could be combined to develop a single default for some vehicle classes and axle types. The effect of bias in weigh-in-motion (WIM) axle weight measurements on the normalized axle load spectra estimates and the associated MEPDG outcomes was also investigated. It was found that drift in WIM system calibration leading to a more than 5% bias in mean error between true and WIM-measured axle weight could lead to significant differences in MEPDG design outcomes. These results were used to develop recommendations for creating axle loading defaults for the MEPDG.

01516644

13-2890

English

pp 120–127

2013

Transportation Research Record: Journal of the Transportation Research Board

9780309287098

Print

Figures (1) ; References (4) ; Tables (8)

Axle load force; Bias (Statistics); Load transfer; Mechanistic-empirical pavement design; Sensitivity analysis; Trucks; Vehicle classification; Weigh in motion

Mechanistic-Empirical Pavement Design Guide

Highways; Operations and Traffic Management; Pavements; I22: Design of Pavements, Railways and Guideways

TRIS, TRB, ATRI

Feb 5 2013 12:35PM

• Classification of Bicycle Traffic Patterns in Five North American Cities
• Correlation-Based Clustering of Traffic Data for the Mechanistic–Empirical Pavement Design Guide
• Estimating Annual Average Daily Bicyclists: Error and Accuracy
• Evaluation of Axle-Based and Length-Based Vehicle Classification Stations
• Generating Site-Specific Axle Load Factors for the Mechanistic–Empirical Pavement Design Guide
• Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of the Mechanistic–Empirical Pavement Design Guide
• Length-Based Vehicle Classification Schemes and Length Bin Boundaries
• Locating Traffic Sensors on a Highway Network: Models and Algorithms
• Quality Counts for Pedestrians and Bicyclists: Quality Assurance Procedures for Nonmotorized Traffic Count Data
• Sensor Performance in Measuring Vehicle Length
• Tablet-Based Traffic Counting Application Designed to Minimize Human Error
• Toward a Flexible System for Pedestrian Data Collection with a Microsoft Kinect Motion-Sensing Device
• Use of Reduction-Effectiveness Ratios to Evaluate Reduced Traffic Data Collection Plans