01550347

Component

https://doi.org/10.3141/2525-08

Data collected by inertial profilers on new asphalt pavements in Colorado in 2012 were used to analyze the effectiveness of the localized roughness specification in Colorado. For the analyzed projects, data were collected before any corrections were made as well as after diamond grinding had been performed to remove areas of localized roughness. The data indicated that localized roughness features having a half-car roughness index (HRI) lower than 175 in./mi were rarely addressed during correction. However, about half the localized roughness features that had an HRI of 175 to 200 in./mi were successfully addressed during correction. Localized roughness features having an HRI greater than 200 in./mi appeared to be successfully addressed during correction. The analysis indicated a significant difference in the localized roughness locations identified by AASHTO R 54 and the Colorado Department of Transportation (DOT) method of detecting localized roughness. The Colorado DOT procedure specifies a minimum length for a roughness feature that is to be corrected, but AASHTO R 54 does not. This paper shows that collecting accurate profile data and analyzing the data to determine localized roughness locations are not enough. The identified locations must be correctly marked on the pavement in the field so that the feature does not cause localized roughness. This paper presents a procedure not only for collecting accurate data but also for accurately marking the roughness features in the field. It is shown that it is possible to locate and correct localized roughness accurately to the current thresholds as set by AASHTO R 54.

01592675

15-2854

English

pp 71–78

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369497

Print

Figures (3) ; References (3) ; Tables (4)

Asphalt pavements; Data collection; Paving; Quality control; Roughness; Smoothness; Specifications

Colorado

Construction; Highways; Pavements; I52: Construction of Pavements and Surfacings

PRP, TRIS, TRB, ATRI

Dec 30 2014 12:58PM

• Adaptive Spike Removal Method for High-Speed Pavement Macrotexture Measurements by Controlling the False Discovery Rate
• Backcalculation with an Implanted Inertial Sensor
• Dynamic Viscoelastic Analysis of Falling Weight Deflectometer Deflections for Rigid and Flexible Pavements
• Evaluation and Validation of a Model for Predicting Pavement Structural Number with Rolling Wheel Deflectometer Data
• Importance of Rubber Characteristics in the Frictional Response of Asphalt Concrete Surfaces
• Measurement of Pavement Treatment Macrotexture and Its Effect on Bicycle Ride Quality
• Monitoring Pavement Surface Macrotexture and Friction: Case Study
• Peak Friction Prediction Model Based on Surface Texture Characteristics
• Performance Evaluations of Unbound Aggregate Permanent Deformation Models for Various Aggregate Physical Properties
• Roughness-Induced Pavement–Vehicle Interactions: Key Parameters and Impact on Vehicle Fuel Consumption
• Study of Influence of Operating Parameters on Braking Friction and Rolling Resistance