EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 393 (2024) MATEC Web Conf., 393 (2024) 02005 References
Open Access
Issue
MATEC Web Conf.
Volume 393, 2024
2nd International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR-2023)
Article Number 02005
Number of page(s) 9
Section Design, Development, and Optimization
DOI https://doi.org/10.1051/matecconf/202439302005
Published online 13 March 2024
  1. Kulkarni, R. B., & Miller, R. W. (2003). Pavement management systems: Past, present, and future. Transportation Research Record, 1853(1), 65–64. https://doi.org/10.3141/1853-08 [CrossRef] [Google Scholar]
  2. Newman, P., Hargroves, K. C., Desha, C., Whistler, L., Farr, A., Wilson, K., & Surawski, L. (2012). Reducing the environmental impact of road construction. https://eprints.qut.edu.au/85967/ [Google Scholar]
  3. Ivanova, E., & Masarova, J. (2013). Importance of road infrastructure in the economic development and competitiveness. Economics and management, 18(2), 263–264. https://doi.Org/10.5755/j01.em.18.2.4253 [CrossRef] [Google Scholar]
  4. He, Z., & Haasis, H. D. (2020). A theoretical research framework of future sustainable urban freight transport for smart cities. Sustainability, 12(5), 1975. https://doi.org/10.3390/su12051975 [CrossRef] [Google Scholar]
  5. Rasol, M., Pais, J. C., Pérez-Gracia, V., Solla, M., Fernandes, F. M., Fontul, S., & Assadollahi, H. (2022). GPR monitoring for road transport infrastructure: A systematic review and machine learning insights. Construction and Building Materials, 324, 126686. https://doi.org/10.1016/j.conbuildmat.2022.126686 [CrossRef] [Google Scholar]
  6. Elhadi, H. M. A. (2009). GIS, a tool for pavement management. https://www.diva-portal.org/smash/get/diva2:1064849/FULLTEXT01.pdf [Google Scholar]
  7. Congress, S. S. C., Puppala, A. J., Khan, M. A., Biswas, N., & Kumar, P. (2020). Application of unmanned aerial technologies for inspecting pavement and bridge infrastructure assets conditions. Transportation Research Record, 03611981221105273, https://doi.org/10.1177/03611981221105273 [Google Scholar]
  8. Outay, F., Mengash, H. A., & Adnan, M. (2020). Applications of unmanned aerial vehicle (UAV) in road safety, traffic, and highway infrastructure management: Recent advances and challenges. Transportation research part A: policy and practice, 141, 116–113. https://doi.org/10.1016/j.tra.2020.09.018 [CrossRef] [Google Scholar]
  9. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road damage detection and classification using deep neural networks with smartphone images. Computer-Aided Civil and Infrastructure Engineering, 33(12), 1127–1124. https://doi.org/10.1111/mice.12387 [CrossRef] [Google Scholar]
  10. Elghaish, F., Talebi, S., Abdellatef, E., Matarneh, S. T., Hosseini, M. R., Wu, S., & Nguyen, T. Q. (2022). Developing a new deep learning CNN model to detect and classify highway cracks. Journal of Engineering, Design and Technology, 20(4), 993–994. https://doi.org/10.1108/JEDT-04-2021-0192 [CrossRef] [Google Scholar]
  11. Tillson, G.W. (1900). “Street Pavements and Paving Materials.” John Wiley & Sons. [Google Scholar]
  12. Collins and Hart. (1936). “The King’s Highway.” Macmillan & Co. [Google Scholar]
  13. Baker, I.O. (1903). “A Treatise on Roads and Pavements.” John Wiley & Sons. [Google Scholar]
  14. Smiles, S. (1904). “Lives of the Engineers: The Locomotive. George and Robert Stephenson.” John Murray. [Google Scholar]
  15. Gillette, H.P. (1906). “Handbook of Cost Data for Contractors and Engineers.” Myron C. Clark Publishing Co. [Google Scholar]
  16. Li, Z., Cheng, C., Kwan, M.-P., Tong, X., & Tian, S. (2023). Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. Peking University, Beijing, China. [Google Scholar]
  17. Cheng, D., & Lane, L. (2023). Pavement Condition Survey using Drone Technology. Mineta Transportation Institute [Google Scholar]
  18. Astor, Y., Nabesima, Y., Utami, R., Rouly Sihombing, A. V., Adli, M., & Firdaus, M. R. (2023). Unmanned aerial vehicle implementation for pavement condition survey. Transportation Engineering, 12, 100168. [CrossRef] [Google Scholar]
  19. Phusakulkajorn, W., et al. (2023). Artificial intelligence in railway infrastructure: current research, challenges, and future opportunities. Intelligent Transportation Infrastructure, Oxford Academic. [Google Scholar]
  20. Li, J., Yin, G., Wang, X., & Yan, W. (2022). Automated decision making in highway pavement preventive maintenance based on deep learning. Automation in Construction, 104111. https://doi.org/10.1016Zj.autcon.2021.104111 [CrossRef] [Google Scholar]
  21. Chandrakasu Makendran, Murugasan Rajiah, Velmurugan Senathipathi, and Ch. Ravi Sekhar. 2021. “Comparative Evaluation of Distress Prediction Modeling of Village Roads in India Using Regression and ANN Techniques.” Journal of Transportation Engineering, September. https:ZZdoi.org/10.1061Zjpeodx.0000287. [Google Scholar]
  22. Chandrakasu, M.: Statistical modelling of rutting for low-volume roads in India, GRAĐEVINAR, pp. 379–387, doi: https://doi.org/10.14256/JCE.3486.2022 [Google Scholar]
  23. C. Makendran, R. Murugasan and S. Velmurugan, “Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis,” Journals of Applied Mathematics, Volume 2015, Article no. 192485, pp. 01–07, 2015, http://dx.doi.org/10.1155/2015/192485. [CrossRef] [Google Scholar]
  24. Makendran, C., M. Vignesh Kumar, B. Mahalingam, and S. Packialakshmi. “Roughness prediction models based on variable distress parameters using neural network and MLRA for PMGSY roads.” Int. J. Adv. Sci. Technol 29, no. 7 (2020): 2208–2218. [Google Scholar]
  25. Makendran C., Performance modelling of low volume village roads in India, http://hdl.handle.net/10603/258801, 2018 [Google Scholar]
  26. Chandrakasu, M.: Statistical modelling of rutting for low-volume roads in India, GRAĐEVINAR, 75 (2023) 4, pp. 379–387, doi: https://doi.org/10.14256/JCE.3486.2022 [Google Scholar]
  27. Makendran C., Karunanidhi S., Shiferaw Garoma, S. Balaguru 2022 ‘Laboratory Study on the Water-soluble Polymer as a Self-curing Compound for Cement Concrete Roads in Ethiopia’, Technologies, 10 (4), 80. https://doi.org/10.3390/technologies10040080 [CrossRef] [Google Scholar]
  28. Balaguru, S., Venkataramana, J. (2020). Experimental Study on Tyre Dynamics and Properties of Heavy Load Transporting Vehicle. In: Yang, L.J., Haq, A., Nagarajan, L. (eds) Proceedings of ICDMC 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-3631-117 [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.