Sigmoidal Models for Predicting Pavement Performance Conditions
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 4
Abstract
This study presents an approach to develop sigmoidal family pavement performance models (pavement performance ratings versus pavement age) for a pavement management system (PMS). Pavement condition data collected from windshield surveys oftentimes suffer quality issues stemming from human subjectivity, and pavement age sometimes not being properly reset after a treatment. These issues can be systematically addressed by the proposed approach, and nonlinear sigmoidal family performance models can then be developed using the cleaned condition data. In a case study, this approach was successfully applied to a sample data set extracted from the North Carolina Department of Transportation (NCDOT) PMS. Contour plots developed for the raw data and the cleaned data showed that the data cleansing process was effective. Goodness-of-Fit indicators and cross-validation suggest that the resulting nonlinear sigmoidal models fit the condition data well.
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Acknowledgments
The authors gratefully acknowledge the support and assistant from the Pavement Management Unit (PMU) at the NCDOT. This research was supported by NCDOT; however, any opinions, findings, and conclusions presented in this paper are those of the authors and do not necessarily reflect the official views of the sponsor.
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Information & Authors
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Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 4 • August 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Aug 20, 2014
Accepted: Jul 29, 2015
Published online: Sep 21, 2015
Discussion open until: Feb 21, 2016
Published in print: Aug 1, 2016
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