Next Article in Journal
A Non-Newtonian Magnetohydrodynamics (MHD) Nanofluid Flow and Heat Transfer with Nonlinear Slip and Temperature Jump
Previous Article in Journal
Trigonometrically-Fitted Methods: A Review
Previous Article in Special Issue
Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network
Open AccessArticle

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

1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Department of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
3
Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
4
Queensland University of Technology, 130 Victoria Park Road, Queensland 4059, Australia
5
Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
6
Department of Elite Relations with Industries, Khorasan Construction Engineering Organization, Mashhad 9185816744, Iran
7
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
8
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Mathematics 2019, 7(12), 1198; https://doi.org/10.3390/math7121198
Received: 19 October 2019 / Revised: 30 November 2019 / Accepted: 2 December 2019 / Published: 6 December 2019
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. View Full-Text
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) machine learning; flexible pavement; remaining service life prediction; pavement condition index; support vector regression (SVR); fruit fly optimization algorithm (FOA); gene expression programming (GEP)
Show Figures

Figure 1

MDPI and ACS Style

Nabipour, N.; Karballaeezadeh, N.; Dineva, A.; Mosavi, A.; Mohammadzadeh S., D.; Shamshirband, S. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics 2019, 7, 1198.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop