Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Rita Justo-Silva, Adelino Ferreira and Gerardo Flintsch
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Rita Justo-Silva: Research Centre for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
Adelino Ferreira: Research Centre for Territory, Transports and Environment, Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra, Portugal
Gerardo Flintsch: Center for Sustainable Transportation Infrastructure, Virginia Tech Transportation Institute (VTTI), Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0131, USA

Sustainability, 2021, vol. 13, issue 9, 1-27

Abstract: Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

Keywords: pavement performance prediction models; modeling techniques; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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