Open Access
MATEC Web Conf.
Volume 195, 2018
The 4th International Conference on Rehabilitation and Maintenance in Civil Engineering (ICRMCE 2018)
Article Number 04007
Number of page(s) 9
Section Transportation Engineering
Published online 22 August 2018
  1. T. Ding, L. Sun, & J. Chen, Optimal Strategy of Pavement Preventive Maintenance Considering Life-Cycle Cost Analysis. Social and Behavioral Sciences, 96, 1679-1685, (2013) [Google Scholar]
  2. R. R. Almuhanna, H. A. Ewadh, & S. J. Alasadi, Using PAVER 6.5. 7 and GIS program for pavement maintenance management for selected roads in Kerbala city. Case Studies in Construction Materials, 8, 323-332, (2018) [CrossRef] [Google Scholar]
  3. P. Y. Wang, Y. Wen, K. Zhao, D. Chong, & A. S. Wong, Evolution and locational variation of asphalt binder aging in long-life hot-mix asphalt pavements. Construction and Building Materials, 68, 172-182, (2014) [CrossRef] [Google Scholar]
  4. R. Hass, Good Technical Foundations Are Essential for Successful Pavement Management. Maintenance and Rehabilitation of Pavements and Technology Control. Guimaraes: Universidade do Minho-Escola de Engenharia. pp. 3-28, (2003) [Google Scholar]
  5. M. Varela-González, M. Solla, J. Martínez-Sánchez, & P. Arias, A semi-automatic processing, and visualization tool for ground-penetrating radar pavement thickness data. Automation in Construction, 45, 42-49, (2014) [CrossRef] [Google Scholar]
  6. A. I. Rifai, S. P. Hadiwardoyo, A. G. Correia, P. Pereira, & P. Cortez, Data Mining Applied for The Prediction of Highway Roughness under Overloaded Traffic. International Journal of Technology, 5:751-761, (2015) [CrossRef] [Google Scholar]
  7. A. Rifai, S. P. Hadiwardoyo, A. G. Correia, & P. Pereira, Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway. Applied Mechanics and Materials ISSN: 1662-7482, 845, Trans Tech Publications, Switzerland, pp 369-378, (2016) [CrossRef] [Google Scholar]
  8. J. Coutinho-Rodrigues, A. Simão, & C. H. Antunes, A GIS-based multicriteria spatial decision support system for planning urban infrastructures. Decision Support Systems, 51(3), 720-726, (2011) [CrossRef] [Google Scholar]
  9. S. M. Bazlamit, H. S. Ahmad, & T. I. Al-Suleiman, Pavement Maintenance Applications using Geographic Information Systems. Procedia Engineering, 182, 83-90, (2017) [CrossRef] [Google Scholar]
  10. F. Wang, Z. Zhang, & R. Machemeh, Decision-making problem for managing pavement maintenance and rehabilitation projects. Transportation Research Record: Journal of the Transportation Research Board, 1853, 21-28, (2003) [CrossRef] [Google Scholar]
  11. M. Di Sivo, L. Daniela, Decision-support tools for municipal infrastructure maintenance management. Procedia Computer Science, 3, 36-41, (2011) [CrossRef] [Google Scholar]
  12. Z. Turskis, G. Ambrasas, D. Kalibatas, & A. Barvidas, Multiple criteria decision support system model for construction works technological cards designing. In The 9th International Conference “Modern Building Materials, Structures and Techniques, 1, p. 3 (2007) [Google Scholar]
  13. E. Cascetta, A. Carteni, F. Pagliara, & M. Montanino, A new look at planning and designing transportation systems: A decision-making model based on cognitive rationality, stakeholder engagement, and quantitative methods. Transport policy, 38, 27-39, (2015) [CrossRef] [Google Scholar]
  14. H. Li, F. Ni, Q. Dong, & Y. Zhu, Application of analytic hierarchy process in network level pavement maintenance decision-making. International Journal of Pavement Research and Technology, (2017) [Google Scholar]
  15. Q. Wen, M. Qiang, & P. Gloor, Speeding up decision-making in project environment: The effects of decision makers’ collaboration network dynamics. International Journal of Project Management, (2018) [Google Scholar]
  16. S. Khademolqorani, & A. Z. Hamadani, An adjusted decision support system through data mining and multiple criteria decision making. Procedia-Social and Behavioral Sciences, 73, 388-395, (2013) [CrossRef] [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.