Open Access
MATEC Web Conf.
Volume 195, 2018
The 4th International Conference on Rehabilitation and Maintenance in Civil Engineering (ICRMCE 2018)
Article Number 04019
Number of page(s) 9
Section Transportation Engineering
Published online 22 August 2018
  1. M. Berawi, T. Zagloel, P. Miraj, & H. Mulyanto, Producing Alternative Concept for the Trans-Sumatera Toll Road Project Development using Location Quotient Method. Procedia Engineering, 171, 265-273, (2017) [Google Scholar]
  2. A. Ansar, B. Flyvbjerg, A. Budzier, & D. Lunn, Does infrastructure investment lead to economic growth or economic fragility? Evidence from China. Oxford Review of Economic Policy, 32(3), 360-390, (2016) [CrossRef] [Google Scholar]
  3. N. Pradhananga, & J. Teizer, Cell-based construction site simulation model for earthmoving operations using real-time equipment location data. Visualization in Engineering, 3 (1), 12, (2015) [CrossRef] [Google Scholar]
  4. A. Rashidi, H. Nejad, & M. Maghiar, Productivity estimation of bulldozers using generalized linear mixed models. KSCE Journal of Civil Engineering, 18(6), 1580-1589, (2014). [CrossRef] [Google Scholar]
  5. C. B. Tatum, Construction engineering research: Integration and innovation. Journal of Construction Engineering and Management, 144(3), 04018005, (2018). [CrossRef] [Google Scholar]
  6. P. Cortez, Data mining with neural networks and support vector machines using the r/rminer tool. Advances in Data Mining: Applications and Theoretical Aspects, 10th Industrial Conference on Data Mining, 83, Berlin, Germany: J In P. Pemer, editor, (2010) [Google Scholar]
  7. M. Parente, A. G. Correia, & P. Cortez, Artificial Neural Networks Applied to an Earthwork Construction Database. In: Toll D, Zhu H, Osman A, et al (eds) Second Int.Conf. Inf. Technol. Geo-Engineering. IOS Press, Durham, UK, 200-205, (2014) [Google Scholar]
  8. S. S. Lee, S. I. Park, & J. Seo, Utilization analysis methodology for fleet telematics of heavy earthwork equipment. Automation in Construction, 92, 59-67, (2018) [Google Scholar]
  9. P. Saha, & K. Ksaibati, A risk-based optimisation methodology for pavement management system of county roads. International Journal of Pavement Engineering, 1-11, (2015) [Google Scholar]
  10. A. Alshibani, & O. Moselhi, Productivity based method for forecasting cost & time of earthmoving operations using sampling GPS data. Journal of Information Technology in Construction (ITcon), 21(3), 39-56, (2016) [Google Scholar]
  11. F. Vahdatikhaki, & A. Hammad, Framework for near real-time simulation of earthmoving projects using location tracking technologies. Automation in Construction, 42, 50-67, (2014) [CrossRef] [Google Scholar]
  12. A. Sheikh, M. Lakshmipath, and A. Prakash, Application of Queuing Theory for Effective Equipment Utilization and Maximization of Productivity in Construction Management. International Journal of Applied Engineering Research, 11(8), 5664-5672, (2016) [Google Scholar]
  13. A. A. Tsehayae, & A. R. Fayek, Developing and Optimizing Context-Specific Fuzzy Inference System-Based Construction Labor Productivity Models. Journal of Construction Engineering and Management, 142(7), 04016017, (2016) [Google Scholar]
  14. C. Koo, T. Hong, & S. Kim, An integrated multi-objective optimization model for solving the construction time-cost trade-off problem. Journal of Civil Engineering and Management, 21(3), 323-333, (2015) [CrossRef] [Google Scholar]
  15. Y. Pan, & L. Hou, Lifting and parallel lifting optimization by using sensitivity and fuzzy set for an earthmoving mechanism. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 0954407016660454, (2016) [Google Scholar]
  16. J. -S. Chou, Applying AHP-Based CBR to Estimate Pavement Maintenance Cost. Tsinghua Science and Technology, 114-120, (2008) [CrossRef] [Google Scholar]
  17. S. Ahn, P. Dunston, A. Kandil, & J. Martinez, Process Mining Technique for Automated Simulation Model Generation Using Activity Log Data. In Computing in Civil Engineering, 636-643, (2015) [Google Scholar]
  18. T. C. Fu, A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164-181, (2011) [Google Scholar]
  19. A. A. Freitas, Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media, (2013) [Google Scholar]
  20. X. Wu, X. Zhu, G. Q. Wu, & W. Ding, Data mining with big data. Knowledge and Data Engineering., IEEE Transactions on, 26(1), 97-107, (2014) [Google Scholar]
  21. 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 5:751-76, (2015) [CrossRef] [Google Scholar]

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