An Overview of Pavement Degradation Prediction Models

Amir Shtayat,Shahriar Rumi,Sara Moridpour,Berthold Best,Seyed Ali Ghahari

doi:10.1155/2022/7783588

Amir Shtayat, Shahriar Rumi + Show 3 more

Open Access

https://doi.org/10.1155/2022/7783588

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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.

Highlights

Road infrastructure facilities have essential and active roles in the advancement of cities and communities
After monitoring the pavement condition, pavement assessment strategies should be applied, and field surveys should be conducted for data collection to evaluate pavement infrastructure. en, a decision will be made based on the relevant information of pavement conditions, and pavement maintenance procedures will be carried out based on the condition of the paving surfaces and expectations of pavement performance [1,2,3]
Performance models must have the growth of pavement degradation, distresses, and damages, or pavement performance indexes such as roughness index, serviceability index, and pavement condition rating [4]

Summary

Introduction

Road infrastructure facilities have essential and active roles in the advancement of cities and communities. Is paper is structured as follows: the subsequent section provides a general layout of the paper It is followed by a general overview of data sources, while Section 4 reviews the existing pavement performance prediction models, depending on PCI and IRI. Some of the past research papers have focused on using the results of pavement performance indices as a database to build their prediction models, while others focused on using filed measurements or other intelligent techniques, such as image processing and vibration data, to collect appropriate databases. Many studies have been conducted to investigate the status and the level of pavement degradation using pavement performance indexes, including PCI, IRI, pavement serviceability index (PSI), and pavement condition rating (PCR). To achieve the optimal goals of the high-precision rating system, IRI and PCI indices are used as main variables in developing pavement performance prediction models.

Applied Methodology

Discussion and Research

Findings

Future Directions