Dynamic Prediction Model of As-Built Roughness in Asphaltic Concrete Pavement Construction
Publication: Journal of Transportation Engineering
Volume 133, Issue 2
Abstract
This paper develops a dynamic prediction model of a highway pavement contractor’s quality-based performance using a panel (longitudinal) data analysis. This panel data modeling uses as-built roughness measurements and pavement and contractor’s characteristics for reconstructed, replaced, and resurfaced pavement projects in Wisconsin from 1998 through 2002. Several random effects models were first developed in in-sample specification, and their modeling performances were measured by Akaike’s information criteria, which combines goodness of fit and model complexity. Out-of-sample specifications validated the developed random effects models by comparing out-of-sample forecasting accuracies. The results show that the best model has approximately a 16% mean absolute percentage error. The results finally show that asphaltic concrete pavement quality of construction can be predicted based on the contractor’s past quality-based performance and other construction parameters. Therefore, the dynamic prediction model developed in this study could be implemented in the contractor’s prequalifications required for advanced contracting methods.
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References
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 133 • Issue 2 • February 2007
Pages: 90 - 95
Copyright
© 2007 ASCE.
History
Received: Aug 1, 2005
Accepted: Dec 20, 2005
Published online: Feb 1, 2007
Published in print: Feb 2007
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