Evaluation of Concrete Pavement Performance Model Considering Inherent Bias in Performance Data
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 150, Issue 1
Abstract
Concrete pavement performance models are evaluated, calibrated, and validated using field data from databases like the Long-Term Pavement Program. Conceptually, performance model predictions should match field data considering a reliability of 50%; that is, there is a 50% probability that the predictions of a certain distress indicator are higher or lower than the field data. However, modern pavements are designed for higher levels of reliability (usually 90% to 95%). Local performance model evaluation for higher levels of reliability requires a high amount of field data that traditional databases lack. Pavement management system (PMS) databases can be a useful resource for high reliability model analysis because of the large amount of data collected locally and regularly. However, when selecting and filtering field databases (of any source), the effect of censored performance data due to rehabilitation, removal from service, or modification of pavement sections is usually ignored. This paper proposes an approach for the use of PMS databases accounting for censored performance data to evaluate the accuracy of performance models’ high reliability predictions. The approach is exemplified using a PMS transverse joint faulting database. Results show that by addressing the “survival issue,” i.e., accounting for the censored performance data, the resulting PMS-based reliability model improves the faulting model accuracy in matching the field data for high reliability levels.
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Data Availability Statement
All the raw PMS data used during the study were provided by PennDOT. Direct request for these materials may be made to the provider as indicated in the Acknowledgments. All models generated or used during the study appear in the published article.
Acknowledgments
This work was supported by PennDOT contract 4400018535, WO1 and by the University of Pittsburgh Anthony Gill Chair. The authors would like to thank Lydia Peddicord, Joshua Freeman, and Shelley Scott from PennDOT for providing information on the collected data, as well as Haoran Li from the University of Pittsburgh for the initial Pavement ME simulations.
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© 2023 American Society of Civil Engineers.
History
Received: Jun 6, 2023
Accepted: Sep 26, 2023
Published online: Nov 25, 2023
Published in print: Mar 1, 2024
Discussion open until: Apr 25, 2024
ASCE Technical Topics:
- Computing in civil engineering
- Concrete pavements
- Databases
- Design (by type)
- Engineering fundamentals
- Field tests
- Highway and road design
- Information Technology (IT)
- Infrastructure
- Model accuracy
- Models (by type)
- Pavement design
- Pavements
- Sight distances
- System reliability
- Systems engineering
- Systems management
- Tests (by type)
- Transportation engineering
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