Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads
Year: 2025
Type: article
Abstract: The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly and time-consuming. Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. The distresses for 15,000 pavement segments in Egypt were investigated through a desk study and field data collection. To this end, several machine learning (ML) and deep learning approaches were developed. The ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). The proposed techniques provide precise PCI estimates and can be seamlessly integrated with PMCs using ubiquitous spreadsheet programs. The results have shown excellent predictions of the ANN model, as demonstrated in the high coefficient of determination (R2 = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). This study sets out to provide a reliable and affordable alternative to specialized tools like MicroPAVER. The ANN model exhibited greater prediction accuracy than the other developed models and can also reliably forecast PCI values by using only measured distress data.
Source: Eng—Advances in Engineering
DOI: 10.3390/eng6060114
Authors: Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W4410826746