EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 676 (2025) E3S Web Conf., 676 (2025) 01003 References
Open Access
Issue
E3S Web Conf.
Volume 676, 2025
Second Edition International Congress Geomatics in the Service of Land Use Planning (GéoSAT’25)
Article Number 01003
Number of page(s) 9
Section Advanced Geomatics at the Heart of Smart and Sustainable Cities
DOI https://doi.org/10.1051/e3sconf/202567601003
Published online 12 December 2025
  1. C. Zhang, E. Nateghinia, L. F. Miranda-Moreno, et L. Sun, « Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada », International Journal of Transportation Science and Technology, vol. 11, no 2, p. 298-309, juin 2022, doi: 10.1016/j.ijtst.2021.04.008. [Google Scholar]
  2. Al-Mansour et A.H. Al-Qaili, « An Application of Android Sensors and Google Earth in Pavement Maintenance Management Systems for Developing Countries », Applied Sciences, vol. 12, no 11, p. 5636, juin 2022, doi: 10.3390/app12115636. [Google Scholar]
  3. N. S. P. Peraka et K.P. Biligiri, « Pavement asset management systems and technologies: A review », Automation in Construction, vol. 119, p. 103336, nov. 2020, doi: 10.1016/j.autcon.2020.103336. [Google Scholar]
  4. L. Blanari, « Automated Road Analyser (ARAN) LRMS ». [Google Scholar]
  5. P. R.W, B. C, et K. S.D, Investigation of Development of Pavement Roughness, vol. FHWA-RD-97-147. Federal Highway Administration, Virginia, 1998. [En ligne]. Disponible sur: https://www.fhwa.dot.gov/publications/research/infrastructure/ [Google Scholar]
  6. P. Li et al., « CNN-based pavement defects detection using grey and depth images », Automation in Construction, vol. 158, p. 105192, févr. 2024, doi: 10.1016/j.autcon.2023.105192. [Google Scholar]
  7. Y. Que et al., « Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model », Engineering Structures, vol. 277, p. 115406, févr. 2023, doi: 10.1016/j.engstruct.2022.115406. [Google Scholar]
  8. J. K. Lee, B. K. Kim, H. Choi, et S.I. Chang, « Road-pavement classification by artificial neural network model based on tire-pavement noise and road-surface image », Applied Acoustics, vol. 225, p. 110194, nov. 2024, doi: 10.1016/j.apacoust.2024.110194. [Google Scholar]
  9. Ragnoli, M. R. De Blasiis, et A. Di Benedetto, « Pavement Distress Detection Methods: A Review », Infrastructures, vol. 3, no 4, Art. no 4, déc. 2018, doi: 10.3390/infrastructures3040058. [Google Scholar]
  10. Y. Aouni, S. El Moudni El Alam, et A. Toumi, « Application of GIS Tools to analyze changes in road network parameters: The case of Berkane province », E3S Web Conf., vol. 607, p. 03002, 2025, doi: 10.1051/e3sconf/202560703002. [Google Scholar]
  11. M. A. Mehdi, T. Cherradi, A. Bouyahyaoui, S. El Karkouri, et A. Qachar, « Evolution of a flexible pavement deterioration, analyzing the road inspections results », Materials Today: Proceedings, vol. 58, p. 1222-1228, 2022, doi: 10.1016/j.matpr.2022.01.452. [CrossRef] [Google Scholar]
  12. J. Ma, Y. Sang, Y. Xu, et B. Wang, « A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points », Algorithms, vol. 18, no 6, p. 372, juin 2025, doi: 10.3390/a18060372. [Google Scholar]
  13. W. Yan, W. Peng, et X. Huaiyu, « Research of Pavement Performance Evaluation and Prediction System of Highway Based on Linear Regression Method », in 2012 International Conference on Communication Systems and Network Technologies, Rajkot, Gujarat, India: IEEE, mai 2012, p. 982-986. doi: 10.1109/CSNT.2012.209. [Google Scholar]
  14. T. Tamagusko, M. Gomes Correia, et A. Ferreira, « Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions », Infrastructures, vol. 9, no 12, p. 213, nov. 2024, doi: 10.3390/infrastructures9120213. [Google Scholar]
  15. M. Zhang, J. Zhong, C. Zhou, X. Jia, X. Zhu, et B. Huang, « Deep learning-driven pavement crack analysis: Autoencoder-enhanced crack feature extraction and structure classification », Engineering Applications of Artificial Intelligence, vol. 132, p. 107949, juin 2024, doi: 10.1016/j.engappai.2024.107949. [Google Scholar]
  16. D. Li, Z. Duan, X. Hu, D. Zhang, et Y. Zhang, « Automated classification and detection of multiple pavement distress images based on deep learning », Journal of Traffic and Transportation Engineering (English Edition), vol. 10, no 2, p. 276-290, avr. 2023, doi: 10.1016/jjtte.2021.04.008. [Google Scholar]
  17. A. Ali, A. Milad, A. Hussein, N. I. Md Yusoff, et U. Heneash, « Predicting pavement condition index based on the utilization of machine learning techniques: A case study », Journal of Road Engineering, vol. 3, no 3, p. 266-278, sept. 2023, doi: 10.1016/j.jreng.2023.04.002. [Google Scholar]
  18. Direction des Etudes, du Développement et de la Recherche Routière, « Recueil Du Trafic Routier 2023 », Direction Générale des Routes et du Transport Terrestre, 2023. [En ligne]. Disponible sur: https://www.equipement.gov.ma/AR/Infrastructures-routieres/Reseau-Routier-du-Maroc/Documents/Recueil%202019.pdf [Google Scholar]
  19. F. Salim, F. Saeed, S. Basurra, S. N. Qasem, et T. Al-Hadhrami, « DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition », Electronics, vol. 12, no 14, p. 3132, juill. 2023, doi: 10.3390/electronics12143132. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.