Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Nader Karballaeezadeh, Farah Zaremotekhases, Shahaboddin Shamshirband, Amir Mosavi, Narjes Nabipour, Peter Csiba and Annamária R. Várkonyi-Kóczy
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Nader Karballaeezadeh: Faculty of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran
Farah Zaremotekhases: Department of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USA
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Amir Mosavi: Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany
Narjes Nabipour: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Peter Csiba: Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
Annamária R. Várkonyi-Kóczy: Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia

Energies, 2020, vol. 13, issue 7, 1-22

Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.

Keywords: transportation; mobility; prediction model; machine learning; pavement management; pavement condition index; highway; structural health monitoring; falling weight deflectometer; multilayer perceptron; radial basis function; artificial neural network; intelligent machine system committee (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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