Estimating local pavement performance and remaining service interval using neural networks-based models and automation tool

This study introduces an integrated approach to enhance county pavement management, emphasising operational efficiency in determining the Remaining Service Interval (RSI) for rigid and flexible pavements. It establishes a robust methodology for systematically processing raw county road data through...

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Bibliographic Details
Published in:Road materials and pavement design 2024-09, Vol.25 (9), p.2001-2035
Main Authors: Citir, Nazik, Kaya, Orhan, Ceylan, Halil, Kim, Sunghwan, Waid, Danny
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Configuration management
county pavement systems
distress
Estimation
Flexible pavements
Indicators
Pavement management
pavement management systems
Performance evaluation
Performance prediction
remaining service interval
Resource allocation
Rigid pavements
Roads
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Summary:This study introduces an integrated approach to enhance county pavement management, emphasising operational efficiency in determining the Remaining Service Interval (RSI) for rigid and flexible pavements. It establishes a robust methodology for systematically processing raw county road data through dynamic segmentation and summarisation to create a structured pavement database. It also incorporates innovative approaches and input configurations in employing Artificial Neural Networks (ANNs) to predict current and future county pavement performance indicators, including International Roughness Index (IRI), rutting, transverse, and longitudinal cracks, even with limited data. Evaluation of the ANN models on independent county road databases exhibited high prediction accuracies (0.86 
ISSN:1468-0629
2164-7402
DOI:10.1080/14680629.2023.2294468