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IJSCER 2023 Vol.12(4): 154-159
doi: 10.18178/ijscer.12.4.154-159

Using Machine Learning for Road Performance Modelling and Influential Factors Investigation

Ali Fares 1*, Eslam Mohammed Abdelkader 1,2, Nour Faris 1, and Tarek Zayed 1
1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
2. Structural Engineering Department, Cairo Univerisity, Giza, Egypt
*Corresponding author: ali-i.fares@connect.polyu.hk (A.F.)

Manuscript received July 8, 2023; revised August 28, 2023; accepted October 2, 2023.

Abstract—Healthy road networks are essential to facilitate economic and social development. Sustaining the integrity of road pavement necessitates having reliable performance models. Such performance models can facilitate evaluating the effect of different physical, environmental, and operational factors on road pavement performance. Hence, this research adopts multiple Machine Learning (ML) algorithms to model the impact of these factors on a composite Pavement Condition Rating (PCR). The PCR is developed using three indicators, namely cracking, rutting, and the International Roughness Index (IRI). This study investigates the implementation of some widely acknowledged ML algorithms, including Artificial Neural Networks, Support Vector Machines, and Bagged Regression Trees to model road pavement performance. Thus, the models are developed and tested using a data set of 302 road sections managed by the Nebraska Department of Roads (NDOR). Also, the deterioration factors are ranked based on their influence on the PCR. Based on the developed models, annual daily traffic (ADT), base layer thickness, and age affect the pavement condition most.

Keywords—performance modeling, pavement, machine learning, influential factors investigation

Cite: Ali Fares, Eslam Mohammed Abdelkader, Nour Faris, and Tarek Zayed, "Using Machine Learning for Road Performance Modelling and Influential Factors Investigation," International Journal of Structural and Civil Engineering Research, Vol. 12, No. 4, pp.154-159, November 2023. doi: 10.18178/ijscer.12.4.154-159

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.