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.

The swift advancement in monitoring and evaluation systems for road pavement conditions highlights the crucial role that this field plays in ensuring the sustainability of roads. This, in turn, contributes to the growth and prosperity of nations and enables users to enjoy the highest levels of luxury and comfort. Despite numerous studies and ongoing research, finding the most precise and efficient monitoring systems to determine the type and severity of road defects, their causes, and appropriate treatments remains a challenge. This study proposes a system that employs a camera to create an application capable of evaluating road conditions with ease by taking images while driving over the road. Based on the results, the application was accurate in identifying road defects of different severity within the same category. The proposed method was compared to the Pavement Condition Index (PCI) method, and a significant match was found in determining the type and severity of each defect on the selected road sections. More clearly, the overall accuracy of detecting and classifying block cracks, alligator cracks, longitudinal cracks, and potholes was significant for detecting and classifying the patches.

Aims: This research investigates the effect of adding natural Jordanian Zeolitic tuffs to the SUPERPAVE asphalt mixture at the dynamic creep and the indirect tensile performance. Background: Rutting and fatigue are considered the most common types of distress that affect pavements all over the world. Many factors can affect these properties, such as different aggregate properties, asphalt grades, and properties, as well as the type and amount of the mineral filler. Mineral fillers play an important role in enhancing the asphalt mixtures' performance by filling the voids in the mixture besides improving the cohesion of the binder as it has a small weight in addition to a very large surface area. Generally, the powder of limestone is the most used type of filler. However, studies showed that many materials could also be used as mineral fillers successfully. The focus of this study is using natural Jordanian Zeolitic tuff as a mineral filler. Jordanian zeolitic tuff mainly consists of Phillipsite, chabazite, and faujasite, which are considered the most abundant Zeolitic tuff minerals. The pyroclastic material is widely distributed in the Badia region of northeast Jordan. Zeolitic tuffs are located in Jabal Aritayn (30km northeast of Azraq), Tlul AlShahba (20 km east of Al Safawi), Tal Al-Rimah (35 km northeast of Al Mafraq), and other small deposits in central and south Jordan. Objectives: The aim of this research was to study the impact of Zeolitic tuffs on overall Superpave asphalt mixture performance. Methods: The specimens were prepared using the optimum asphalt content obtained by the SUPERPAVE method. Dynamic Testing System DTS-16 was used to apply both dynamic creep and indirect tensile tests that give a good idea about the rutting and fatigue behavior of asphalt mixtures. Both tests were conducted at 25 °C. Results: The Zeolitic tuffs modified asphalt mixtures have lower accumulated strains and higher creep stiffness compared to the control mixture. Subsequently, the indirect tensile test results showed that the modified asphalt mixtures have a higher resilient modulus than the control asphalt mix. Generally, using Zeolitic tuffs as a modifier of asphalt mixtures enhanced the rutting and fatigue resistance. The results showed that asphalt mixtures fortified with 25% Zeolitic tuffs emerged as the best contenders for rutting resistance. Also, mixtures enriched with 50% Zeolitic tuffs stood out in fatigue resistance performance. Conclusion: The best dynamic creep performance was by adding 25% Zeolitic tuffs by the mineral filler mass, while the best resilient modulus was by adding 50% Zeolitic tuffs by the mineral filler mass. Other: The overarching goals encompass understanding the dynamic creep behavior of Zeolitic tuff-infused asphalt blends, deciphering its indirect tensile performance, pinpointing the ideal replacement ratio for mineral fillers, and drawing a performance comparison between WMA and conventional Hot Mix Asphalt (HMA) compositions.