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Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

Author

Listed:
  • Sami Abdullah Osman

    (Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia)

  • Meshal Almoshaogeh

    (Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia)

  • Arshad Jamal

    (Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia)

  • Fawaz Alharbi

    (Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 51452, Saudi Arabia)

  • Abdulhamid Al Mojil

    (Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia)

  • Muhammad Abubakar Dalhat

    (Transportation and Traffic Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31451, Saudi Arabia)

Abstract

The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.

Suggested Citation

  • Sami Abdullah Osman & Meshal Almoshaogeh & Arshad Jamal & Fawaz Alharbi & Abdulhamid Al Mojil & Muhammad Abubakar Dalhat, 2022. "Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-30, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:561-:d:1018366
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    References listed on IDEAS

    as
    1. Arshad Jamal & Waleed Umer, 2020. "Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network," IJERPH, MDPI, vol. 17(20), pages 1-22, October.
    2. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
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