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Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

Author

Listed:
  • Narjes Nabipour

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Nader Karballaeezadeh

    (Department of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Adrienn Dineva

    (Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary)

  • Amir Mosavi

    (Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Queensland University of Technology, 130 Victoria Park Road, Queensland 4059, Australia)

  • Danial Mohammadzadeh S.

    (Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
    Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad 9185816744, Iran)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

Abstract

Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

Suggested Citation

  • Narjes Nabipour & Nader Karballaeezadeh & Adrienn Dineva & Amir Mosavi & Danial Mohammadzadeh S. & Shahaboddin Shamshirband, 2019. "Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement," Mathematics, MDPI, vol. 7(12), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1198-:d:295136
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    References listed on IDEAS

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    2. Prozzi, Jorge A, 2001. "Modeling Pavement Performance by Combining Field and Experimental Data," University of California Transportation Center, Working Papers qt1gx2425x, University of California Transportation Center.
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