This work introduces the need to develop competitive, low-cost and applicable technologies
to real roads to detect the asphalt condition by means of Machine Learning (ML) algorithms …

This paper conducts a comprehensive review of empirical methods of pavement
performance modeling. The paper firstly analyzes performance measures used in existing …

This paper proposes an ensemble learning model that deploys a Gradient Boosting
Decision Tree (GBDT) to predict two relevant functional indices, the International roughness …

Abstract Pavement Condition Index (PCI) is a numerical assessment of pavement conditions
based on existing distresses. The PCI values are used for pavement management and …

In recent years, different technologies based on Machine Learning (ML) algorithms have
been developed to evaluate road distress quality and help with pavement maintenance …

Significant research efforts have documented the capabilities of machine learning (ML)
algorithms to model pavement performance. Several challenges, however, limit the …

Abstract International Roughness Index (IRI) is an important pavement performance
indicator that is widely used to reflect existing pavement condition and ride quality. Due to …

Pavement roughness in terms of the International Roughness Index (IRI) plays an important
role in determining the riding quality of road networks. Moreover, it is considered as a …

Over time, roads undergo deterioration caused by various factors such as traffic loads,
climate conditions, and material properties. Considering the substantial global investments …

On-time pavement maintenance and rehabilitation are required to maintain or improve
pavement roughness, which is an indicator of pavement performance and road safety. Better …