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.
Highlights
Road transportation has always been inherent in the development of societies, impacting between 10–20% of Gross Domestic Product (GDP)
The AASHTO road test occurred in the USA between 1958 and1961, and it remains the foundation for the development of many Pavement performance prediction models (PPPMs) in various countries
Artificial neural networks (ANNs) are computational systems inspired by biological and psychological insight composed of processing elements, called “neurons.” Neurons are linked to each other, establishing a network
Summary
Road transportation has always been inherent in the development of societies, impacting between 10–20% of Gross Domestic Product (GDP). Road maintenance is vital since the need for maintenance increases as road infrastructure ages. It is based on sustainability, meaning that spending money saves much more in the future. There is no doubt about the importance of PPPMs for efficient management in PMSs, these methods are not yet being used by most Portuguese road agencies or municipalities. A key component in pavement management is assessing the condition of the road network to predict future conditions. Mathematical functions, known as pavement performance prediction models (PPPMs), are used to perform this task. PPPMs are used to predict the future class condition of the roads that comprise the network. According to [2], there are two types of models for pavement performance prediction: Static models (or absolute models); and Dynamic models (or relative models)
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