Details:

Creators:
Arhin, Stephen A. ; Noel, Errol C.
Arhin, Stephen A. ; Noel, Errol C. Less -
Corporate Creators:
Howard University. Transportation Research Center
Corporate Contributors:
United States. Federal Highway Administration
Subject/TRT Terms:
[+]
Mathematical Prediction Pavement Distress Pavement Management Systems Regression Analysis Roughness
Publication/ Report Number:
DDOT-RDT-14-03
Resource Type:
Tech Report
Geographical Coverage:
Washington (District of Columbia)
Corporate Publisher:
District of Columbia. Dept. of Transportation
Abstract:
A number of pavement condition indices are used to conduct pavement management assessments, two of which are the

International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specialized

equipment that calculates the smoothness and ride quality of the roadway segment based on established computer

algorithms, while the PCI is based on subjective rating of the number of pavement distress. The literature suggests that

most pavement indices are related, as a result of which several jurisdictions have developed models to predict one index

from the other(s). This study used three (3) years of IRI-PCI data obtained from the District Department of Transportation

to develop models which could potentially predict PCI from IRI by functional classification and by pavement type. The

regression models explored were developed using the ordinary least squares method and were tested on the basis of 5%

level of significance. The IRI-PCI models yielded R2

and adjusted R2

values between 0.008 and 0.0730, indicating that the

models could only explain up to 7.3% of the variations in the data. In addition, the root mean square errors of the models

were all determined to be greater than 1. Even though the results of the ANOVA tests indicated that the coefficients

were generally statistically significant, the low R2

values and high RMSEs indicate that the models do not adequately

predict PCI from IRI, within the margin of error. A more sophisticated prediction tool, such as artificial neural networks,

could be explored to potentially predict PCI from IRI more accurately.


Format:
PDF
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:de2574dc0033da2d7c454fa94c8353108d1251b5bc2adff9f6941b9bf893a3f0
Download URL:
https://rosap.ntl.bts.gov/view/dot/28282/dot_28282_DS1.pdf
File Type:

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