scholarly journals Program to Determine the Terrain Roughness Index using Path Profile Data Sampled at Different Moving Window Sizes

2018 ◽  
Vol 182 (34) ◽  
pp. 34-41
Author(s):  
Simeon Ozuomba ◽  
Henry Johnson ◽  
Constance Kalu
Author(s):  
Gary J. Higgins

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.


2000 ◽  
Vol 1699 (1) ◽  
pp. 121-126 ◽  
Author(s):  
Emmanuel G. Fernando

The relationship between the profilograph profile index (PI) and the international roughness index (IRI) is evaluated. To accomplish this evaluation, profile data taken on 48 overlaid test sections were used in profilograph simulations to predict the profilograph response to the measured profiles. The PIs determined were then correlated with IRIs computed from the profile data to evaluate relationships between these roughness statistics. The results show that the PI based on the null blanking band is more strongly related to the IRI than the corresponding index determined using the 5-mm blanking band. In view of the general acceptance of the IRI as a statistic for establishing surface smoothness based on profiles, the results suggest that a profilograph specification based on the null blanking band is preferable to a similar specification based on the 5-mm blanking band, which may mask certain components of roughness that are otherwise picked up if no blanking band is used.


Author(s):  
Shubham Rawool ◽  
Emmanuel G. Fernando

Pavement smoothness has become a standard measure of pavement quality. Transportation agencies strive to build and maintain smoother pavements. Road users generally perceive the quality of a pavement on the basis of how well it rides, which is severely affected by the presence of defects (bumps or dips) in the pavement profile. Defects are corrected according to the smoothness specifications prescribed by respective agencies. The effectiveness of any method used to identify defect locations depends on the decrease in roughness obtained on correction of the defects. Following this line of thought, this paper presents a method for the detection of defects based on a comparison of the original profile with a target or a desired profile. The proposed methodology is based on the international roughness index (IRI) gain function for the identification of defect locations to improve smoothness in pavements. This method uses the discrete Fourier transform to help identify defect locations on the basis of deviations of the original profile from the target or the smoothened profile. Areas with defects have a higher deviation from the smoothened profile than areas without defects. This method also estimates the contribution of each defect to roughness. Roughness statistics, such as the IRI and the present serviceability index, are used in the proposed approach to determine the severity of each defect. In addition, the use of a quarter-truck transfer function instead of the IRI gain function is demonstrated to illustrate consideration of dynamic load criteria for the detection of defects. The approach is illustrated through the use of profile data collected for in-service pavement sections.


Data Series ◽  
10.3133/ds724 ◽  
2012 ◽  
Author(s):  
Arnell S. Forde ◽  
Shawn V. Dadisman ◽  
Jack G. Kindinger ◽  
Jennifer L. Miselis ◽  
Dana S. Wiese ◽  
...  

Data Series ◽  
10.3133/ds611 ◽  
2011 ◽  
Author(s):  
Arnell S. Forde ◽  
Shawn V. Dadisman ◽  
James G. Flocks ◽  
Dana S. Wiese ◽  
Nancy T. DeWitt ◽  
...  

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