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Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Author

Listed:
  • Rita Justo-Silva

    (Research Centre for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

  • Adelino Ferreira

    (Research Centre for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal)

  • Gerardo Flintsch

    (Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute (VTTI), Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0131, USA)

Abstract

Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

Suggested Citation

  • Rita Justo-Silva & Adelino Ferreira & Gerardo Flintsch, 2021. "Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5248-:d:550375
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    References listed on IDEAS

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    Cited by:

    1. Nazmus Sakib Ahmed & Nathan Huynh & Sarah Gassman & Robert Mullen & Charles Pierce & Yuche Chen, 2022. "Predicting Pavement Structural Condition Using Machine Learning Methods," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    2. Nicola Baldo & Matteo Miani & Fabio Rondinella & Clara Celauro, 2021. "A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    3. Cuthbert Ruseruka & Judith Mwakalonge & Gurcan Comert & Saidi Siuhi & Frank Ngeni & Kristin Major, 2023. "Pavement Distress Identification Based on Computer Vision and Controller Area Network (CAN) Sensor Models," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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