514
Views
30
CrossRef citations to date
0
Altmetric
Original Articles

Backpropagation Neural Network to estimate pavement performance: dealing with measurement errors

&
Pages 1218-1238 | Received 03 Jan 2016, Accepted 09 Jun 2016, Published online: 01 Jul 2016

References

  • AASHTO. (1986). AASHTO guide for the design of pavement structures (appendix EE). Washington, DC: American Association of State Highway and Transportation Officials.
  • AASHTO. (1993). AASHTO guide for the design of pavement structures. Washington, DC: American Association of State Highway and Transportation Officials.
  • Abaza, K., Ashur, S., Abu-Eisheh, S., & Rabay’a, A. (2001). Macroscopic optimum system for management of pavement rehabilitation. Journal of Transportation Engineering, 127(6), 493–500. doi:http://dx.doi.org/10.1061/(ASCE)0733-947X(2001)127:6(493)
  • Amin, M. S. R. (2015). The pavement performance modeling: Deterministic vs stochastic approaches. In S. Kadry & A. El Hami (Eds.), Numerical methods for multiscale and multiphysics in reliability and safety (pp. 179–196). Cham, Switzerland: Springer.
  • Amin, M. S. R., & Amador, L. E. (2016). Pavement management with dynamic traffic and artificial neural network: A case study of Montreal. Canadian Journal of Civil Engineering, 43(3), 241–251. doi: 10.1139/cjce-2015-0299
  • Attoh-Okine, N. O. (1994). Predicting roughness progression in flexible pavements using artificial neural networks. Paper presented at the Third International Conference on Managing Pavements, Transportation Research Board, National Research Council, San Antonio, TX, 55–62.
  • Attoh-Okine, N. O. (1999). Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance. Advances in Engineering Software, 30, 291–302. doi:10.1016/S0965-9978(98)00071-4
  • Ben-Akiva, M., Humplick, F., Madanat, S., & Ramaswamy, R. (1993). Infrastructure management under uncertainty: The latent performance approach. Journal of Transportation Engineering, 119(1), 43–58. doi:http://dx.doi.org/10.1061/(ASCE)0733-947X(1993)119:1(43)
  • Cement Association of Canada. (2012). Methodology for the development of equivalent pavement structural design matrix for municipal roadways – Montréal and Québec City: Including maintenance & rehabilitation schedules and life cycle cost analysis. Final Report. Ottawa: Applied Research Associates Inc.
  • Durango-Cohen, P. L. (2007). An optimal estimation and control framework for the management of infrastructure facilities. In C. Karlsson, W. P. Anderson, B. Johansson, & K. Kobayashi (Eds.), The management and measurement of infrastructure: Performance, efficiency and innovation (pp. 177–197). Cheltenham: Edward Elgar.
  • El-Basyouny, M., & Jeong, M. G. (2010). Probabilistic performance-related specifications methodology based on mechanistic-empirical pavement design guide. Journal of the Transportation Research Board, 2151, 93–102. doi:10.3141/2151-12
  • Freeman, J. A., & Skapura, D. M. (1991). Neural networks. Algorithms, applications, and programming techniques. Menlo Park, CA: Addison-Wesley Publishing Company.
  • George, K. P., Rajagopal, A. S., & Lim, L. K. (1989). Models for predicting pavement deterioration. Transportation Research Record: Journal of the Transportation Research Board, 1215, 1–7.
  • IBM. (2010). IBM SPSS neural networks 19. Received January 10, 2012, from http://www.sussex.ac.uk/its/pdfs/SPSS_Neural_Network_19.pdf
  • Jansen, J. M., & Schmidt, B. (1994). Performance models and prediction of increase overlay need in Danish state highway pavement management system, BELMA. Paper presented at the Third International Conference on Managing Pavements, San Antonio, TX, 74–84.
  • Johnson, K. D., & Cation, K. A. (1992). Performance prediction development using three indexes for North Dakota pavement management system. Transportation Research Record: Journal of the Transportation Research Board, 1344, 22–30.
  • Kulkarni, R. B., & Miller, R. W. (2002). Pavement management systems past, present, and future. Transportation Research Record: Journal of the Transportation Research Board, 1853, 65–71. doi:http://dx.doi.org/10.3141/1853-08
  • Li, N., Haas, R., & Xie, W.-C. (1997). Investigation of relationship between deterministic and probabilistic prediction models in pavement management. Transportation Research Record: Journal of the Transportation Research Board, 1592, 70–79. doi:http://dx.doi.org/10.3141/1592-09
  • Li, N., Xie, W.-C., & Haas, R. (1996). Reliability-based processing of Markov chains for modeling pavement network deterioration. Transportation Research Record: Journal of the Transportation Research Board, 1524, 203–213. doi:http://dx.doi.org/10.3141/1524-24
  • Li, Y., Cheetham, A., Zaghloul, S., Helali, K., & Bekheet, W. (2006). Enhancement of Arizona pavement management system for construction and maintenance activities. Transportation Research Record: Journal of the Transportation Research Board, 1974, 26–36. doi:http://dx.doi.org/10.3141/1974-06
  • Liebman, J. (1985). Optimization tools for pavement management. Paper presented at the North American Pavement Management Conference, Ontario Ministry of Transportation and Communication, U.S. Federal Highway Administration, Washington, DC, 6.6–6.15.
  • Liu, L., & Gharaibeh, N. G. (2014). Bayesian model for predicting the performance of pavements treated with thin HMA overlays. Paper presented at the 93rd annual meeting of the Transportation Research Board, Washington, DC. Received May 29, 2016, from http://docs.trb.org/prp/14-3030.pdf
  • de Melo e Siva, F., Van Dam, T. J., Bulleit, W. M., & Ylitalo, R. (2000). Proposed pavement performance models for local government agencies in Michigan. Transportation Research Record: Journal of the Transportation Research Board, 1699, 81–86. doi:http://dx.doi.org/10.3141/1699-11
  • Nawi, N. M., Khan, A., Rehman, M. Z., Aziz, M. A., Herawan, T., & Abawajy, J. H. (2014). An accelerated particle swarm optimization based Levenberg Marquardt back propagation algorithm. In C. K. Loo, K. S. Yap, K. W. Wong, A. Teoh, & K. Huang (Eds.), Neural information processing: 21st international conference, ICONIP 2014, Kuching, Malaysia, November 3–6, 2014. Proceedings, part II (pp. 245–253). Cham, Switzerland: Springer International Publishing.
  • Papagiannakis, A. T., & Masad, E. A. (2008). Pavement design and materials. Hoboken, NJ: John Wiley and Sons.
  • Raab, C., Grenfell, J., Abd El Halim, A. O., & Partl, M. N. (2015). The influence of age on interlayer shear properties. International Journal of Pavement Engineering, 16(6), 559–569. doi:10.1080/10298436.2014.943212
  • Rakesh, N., Jain, A. K., Reddy, M. A., & Reddy, K. S. (2006). Artificial neural networks – Genetic algorithm based model for backcalculation of pavement layer moduli. International Journal of Pavement Engineering, 7(3), 221–230. doi:10.1080/10298430500495113
  • Saleh, M. F., Mamlouk, M. S., & Owusu-Antwi, E. B. (2000). Mechanistic roughness model based on vehicle-pavement interaction. Transportation Research Record: Journal of the Transportation Research Board, 1699, 114–120. doi:http://dx.doi.org/10.3141/1699-16
  • Shekharan, A. R. (1999). Assessment of relative contribution of input variables to pavement performance prediction by artificial neural networks. Transportation Research Record: Journal of the Transportation Research Board, 1655, 35–41. doi:http://dx.doi.org/10.3141/1655-06
  • Terzi, S. (2007). Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction and Building Materials, 21(3), 590–593. doi: 10.1016/j.conbuildmat.2005.11.001
  • Ziari, H., Sobhani, H., Ayoubinejad, J., & Hartmann, T. (2016). Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods. International Journal of Pavement Engineering, 17(9), 776–788. doi:10.1080/10298436.2015.1019498

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.