An Overview of Pavement Degradation Prediction Models

Abstract

Pavement management systems (PMSs) have a primary role in determining pavement condition monitoring and maintenance strategies. Moreover, many researchers have focused on pavement condition evaluation tools, starting with data collection, followed by processing, analyzing, and ultimately reaching practical conclusions regarding pavement condition. The analysis step is considered an essential part of the pavement condition evaluation process, as it focuses on the tools used to find the most accurate results. On the other hand, prediction models are important tools used in pavement condition evaluation to determine the current and future performance of the road pavement. Therefore, pavement condition prediction has an effective and significant role in identifying the appropriate maintenance techniques and treatment processes. Moreover, pavement performance indices are commonly used as key indicators to describe the condition of pavement surfaces and the level of pavement degradation. This paper systematically summarizes the existing performance prediction models conducted to predict the condition of asphalt pavement degradation using pavement condition indexes (PCI) and the international roughness index (IRI). These performance indices are commonly used in pavement monitoring to accurately evaluate the health status of pavement. The paper also identifies and summarizes the most influencing parameters in road pavement condition prediction models and presents the strength and weaknesses of each prediction model. The findings show that most previous studies preferred machine learning approaches and artificial neural networks forecasting and estimating the road pavement conditions because of their ability to deal with massive data, their higher accuracy, and them being worthwhile in solving time-series problems.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

“…The IRI can be measured by calculating the distortion of the pavement surface along the longitudinal profile of the vehicle wheel path [18]. Developing IRI-based models for performance prediction is common as pavements deteriorate [3,19,20]. Increasing fuel consumption, greenhouse gas emissions, decreasing vehicle efficiency, and causing traffic accidents are well-correlated with increasing pavement roughness, which collectively costs USD one million every year [18,[21][22][23].…”

Dynamic civil facility degradation prediction for rare defects under imperfect maintenance

“…In earlier research, various statistical and AI techniques have been employed to address prediction issues, such as regressions, Markov chain and the variants, neural networks, machine learning, simulation, etc. (Chen et al, 2019;Yu et al, 2013;Sheng et al, 2019;Shtayat et al, 2022;Wang et al, 2017;Zemni et al, 2020;Zhang et al, 2015b;Zhou et al, 2020). Chen et al (2019) adopted the grey neural network model to build the degradation model of fuel cells, in which the particle swarm optimization was used to enhance the operation performance.…”

“…Zhou et al (2020) incorporated Convolutional Neural Network (CNN) and long short-term memory techniques to model the highly nonlinear dynamics and estimate degradation tendency. Shtayat et al (2022) reviewed and discussed the methodologies used for pavement degradation prediction.…”

Effect of asphalt pavement construction on the environment of Ethiopia

“…Among those studies, it has been focused on the environmental impact of asphalt pavement [3], it has been studied to compare the environmental impact of different pavements [4,5], it has been explanted about the traffic fuel consumption on a different surface of the layers during the use phase or on maintenance strategies [6] and it has been studied about a review on existing of life cycle assessment [7]. Several researchers have studied the life cycle of road pavement and divided it into five categories [8,9]. They are extraction of raw materials, construction of road, using of road and end of its life [10].…”