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 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 < 𝘙²< 0.99), varying with specific performance indicators. The study results in an automation tool for expediting road performance estimation over multiple years. This tool seamlessly integrates the ANN models, empowering county engineers to make data-driven decisions and optimise resource allocation for effective pavement management, achieving significant cost savings.

• Record URL:
  • https://doi.org/10.1080/14680629.2023.2294468
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14680629
• Supplemental Notes:
  • © 2023 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Citir, Nazik
  • Kaya, Orhan
  • Ceylan, Halil
  • Kim, Sunghwan
  • Waid, Danny
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2001-2035
• Serial:
  • Road Materials and Pavement Design
  • Volume: 25
  • Issue Number: 9
  • Publisher: Taylor & Francis
  • ISSN: 1468-0629
  • EISSN: 2164-7402
  • Serial URL: http://www.tandfonline.com/loi/trmp20

Subject/Index Terms

• TRT Terms: Estimation theory; Neural networks; Pavement distress; Pavement management systems; Pavement performance
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01928004
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 22 2024 3:11PM