Hybrid Technique for Calibrating Network-Level Performance Models of Continuously Reinforced Concrete Pavements
Publication: Journal of Transportation Engineering
Volume 139, Issue 12
Abstract
Pavement performance models exist in various forms to cater to the pavement management agencies’ needs and resources. Well-calibrated models are needed to accurately predict future pavement conditions and to forecast and prioritize confidently the future rehabilitation and maintenance expenditures. Statistical tools are commonly used to develop the performance models. These statistical models may be impractical or misleading if they do not consider experts’ opinions. This paper presents a hybrid technique where statistical tools and expert knowledge are combined for the calibration of pavement performance models. This technique was validated using historical pavement condition data for continuously reinforced concrete pavements (CRCP) from the Texas Department of Transportation’s pavement management information system (TxDOT-PMIS). The recalibrated CRCP performance models obtained with the hybrid technique represent an improvement when compared to the current models since they merge expert opinion and statistical analysis, which better reflect field observations regarding distress initiation, distress evolution rate, and maximum allowable amount of distress growth.
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Acknowledgments
The authors wish to thank the Texas Department of Transportation for their continuous support in research project number 0-6386 titled “Evaluation and Development of Pavement Scores, Performance Models and Needs Estimates for the TxDOT Pavement Management Information System.” The expertise provided by TxDOT personnel is greatly appreciated.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 139 • Issue 12 • December 2013
Pages: 1194 - 1200
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Oct 3, 2012
Accepted: May 21, 2013
Published online: May 23, 2013
Discussion open until: Oct 23, 2013
Published in print: Dec 1, 2013
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