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

Narjes Nabipour, Nader Karballaeezadeh, Adrienn Dineva, Amir Mosavi, Danial Mohammadzadeh S. and Shahaboddin Shamshirband
Additional contact information
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
Danial Mohammadzadeh S.: Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Mathematics, 2019, vol. 7, issue 12, 1-22

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.

Keywords: machine learning; flexible pavement; remaining service life prediction; pavement condition index; support vector regression (SVR); fruit fly optimization algorithm (FOA); gene expression programming (GEP) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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