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Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement
Nabipour, N.; Karballaeezadeh, N.; Dineva, A.; Mosavi, A.; Mohammadzadeh, D.S.; Shamshirband, S. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics2019, 7, 1198.
Nabipour, N.; Karballaeezadeh, N.; Dineva, A.; Mosavi, A.; Mohammadzadeh, D.S.; Shamshirband, S. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics 2019, 7, 1198.
Nabipour, N.; Karballaeezadeh, N.; Dineva, A.; Mosavi, A.; Mohammadzadeh, D.S.; Shamshirband, S. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics2019, 7, 1198.
Nabipour, N.; Karballaeezadeh, N.; Dineva, A.; Mosavi, A.; Mohammadzadeh, D.S.; Shamshirband, S. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics 2019, 7, 1198.
Abstract
Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and 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 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
hybrid machine learning model; transportation infrastructure; flexible pavement; remaining service life prediction; pavement condition index; support vector regression; fruit fly optimization algorithm (foa); gene expression programming (gep); svr-foa
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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