552
Views
14
CrossRef citations to date
0
Altmetric
Articles

Prioritisation of pavement maintenance sections deploying functional characteristics of pavements

ORCID Icon, , ORCID Icon &
Pages 1815-1822 | Received 08 Oct 2018, Accepted 29 Dec 2018, Published online: 24 Jan 2019

References

  • Adeli, H., 2001. Neural networks in civil engineering: 1989–2000. Computer-Aided Civil and Infrastructure Engineering, Blackwell Publishers, 16, 126–142. doi: 10.1111/0885-9507.00219
  • Ahmed, S., Vedagiri, P., and Rao, K.V.K., 2017. Prioritization of pavement maintenance sections using objective based analytic hierarchy process. International Journal of Pavement Research and Technology, Chinese Society of Pavement Engineering. doi:10.1016/j.ijprt.2017.01.001.
  • Archondo-callao, R.S., 1981. Typical unpaved roads roughness predicted by the HDM-III model. Infrastructure Notes, Transport, Water and Urban Development, 1–5.
  • Babashamsi, P., et al., 2016. Integrated fuzzy analytic hierarchy process and VIKOR method in the prioritization of pavement maintenance activities. International Journal of Pavement Research and Technology, Chinese Society of Pavement Engineering, 9 (2), 112–120. doi:10.1016/j.ijprt.2016.03.002.
  • Basic road statistics of India 2015–16., 2015. Government of India.
  • Bo, P., Sheng, J.Y., and Liang, Y., 2012. Relationship between roughness and distresses of high-grade concrete pavement. 7th International Conference on computer Science and Education, IEEE, Melboune, Australia, 1271–1276.
  • Bosurgi, G., D’Andrea, A., and Pellegrino, O., 2013. What variables affect to a greater extent the driver’s vision while driving? Transport, Taylor & Francis, 28 (4), 331–340. doi:10.3846/16484142.2013.864329.
  • Bosurgi, G. and Trifirò, F., 2005. A model based on artificial neural networks and genetic algorithms for pavement maintenance management. International Journal of Pavement Engineering, Taylor & Francis, 6 (3), 201–209. doi:10.1080/10298430500195432.
  • Ceylan, H., Bayrak, M.B., and Gopalakrishnan, K., 2014. Neural networks applications in pavement engineering: A recent survey. International Journal of Pavement Research and Technology, Chinese Society of Pavement Engineering, 7 (6), 434–444. doi:10.6135/ijprt.org.tw/2014.
  • Chandra, S., et al., 2013. Relationship between pavement roughness and distress parameters for Indian highways. Journal of Transportation Engineering, ASCE, 139 (5), 467–475. doi:10.1061/(ASCE)TE doi: 10.1061/(ASCE)TE.1943-5436.0000512
  • Chandran, S., Isaac, K.P., and Veeraragavan, A., 2007. Prioritization of low volume pavement sections for maintenance by using fuzzy logic. Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board, 1989-1 (1989), 53–60. doi: 10.3141/1989-06
  • Chopra, T., et al., 2017. Development of Pavement Maintenance Management System (PMMS) of urban road network using HDM-4 model. International Journal of Engineering and Applied Sciences, Drive, C. 9 (1), 14–31. doi: 10.24107/ijeas.286473
  • Dalal, J., Mohapatra, K.J.P., and Mitra, G.C., 2010. Prioritization of rural roads: AHP in group decision. Engineering Construction and Architecture Management, 17 (2), 135–158. doi:10.1108/09699981011024669.
  • Doughty, M., 1997. Applications of neural network in transportation. Transportation Research Part C: Emerging Technologies, 5 (5), 255–257. doi: 10.1016/S0968-090X(97)00013-2
  • Elseifi, M., Khalek, A.M.A., and Dasari, K., 2015. Implementation of Rolling Wheel Deflectometer (RWD) in PMS and pavement preservation. Technical Report, Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA.
  • Graupe, D., 2007. Principles of artificial neural networks. Advanced Series of Circuits and Systems, 6. doi:10.1142/9789812770578_fmatter doi: 10.1142/6429
  • Gupta, A., Kumar, P., and Rastogi, R. (2011). Pavement deterioration and maintenance model for low volume roads. International Journal of Pavement Research and Technology, Chinese Society of Pavement Engineering, 4 (4), 195–202, ISSN 1997-1400. Available from: http://web.worldbank.org/WEBSITE/EXTERNAL/COUNTRIES/SOUTHASIAEXT/EXTSARREGTOPTRANSPORT/0,contentMDK:20703625~pagePK:34004173~piPK:34003707~theSitePK:579598,00.html [Accessed 24 April 2018].
  • IRC: 81., 1997. Guidelines for strengthening of flexible road pavements using Benkelman beam deflection technique. New Delhi: Indian Roads Congress.
  • IRC: SP 72., 2015. Guidelines for the design of flexible pavements for low volume rural road. New Delhi: Indian Roads Congress.
  • IS 2720-16: Part XVI, 1987. Methods of test for soils. New Delhi: Bureau of Indian Standards.
  • IS 2720-7: Part VII, 1980. Methods of test for soils. New Delhi: Bureau of Indian Standards.
  • Karayiannis, N.B. and Venetsanopoulos, A.N., 1993. Artificial neural networks: Learning algorithms, performance evaluation, and applications. Boston: Boston: Kluwer Academic.
  • Khademi, N. and Sheikholeslami, A., 2010. Multicriteria group decision-making technique for a low-class road maintenance program. Journal of Infrastructure Systems, ASCE, 16 (3), 188–198. doi: 10.1061/(ASCE)IS.1943-555X.0000023
  • Lera, G. and Pinzolas, M., 2002. Neighbourhood based levenberg-marquardt algorithm for neural network training. IEEE Transactions on Neural Networks, 13 (5), 1200–1203. doi: 10.1109/TNN.2002.1031951
  • Nirman, B., 2007. Bharat Nirman, and Flagship Programmes.
  • Obaidat, T.I.A. and Shiyab, A.M.S., 2007. Prediction of pavement remaining service life using roughness data — case study in Dubai. International Journal of Pavement Engineering, & Francis, 4 (2), 121–129. doi:10.1080/10298430310001634834.
  • Park, K., Thomas, N.E., and Lee, K.W., 2007. Applicability of the international roughness index as a predictor of asphalt pavement condition. Journal of Transportation Engineering, ASCE, 133, 706–709. doi: 10.1061/(ASCE)0733-947X(2007)133:12(706)
  • Priddy, K.L. and Keller, P.E., 2005. Artificial neural networks: an introduction. Tutorial Texts in Optical Engineering, TT68, 107–114.
  • Rada, R.G., et al., 2012. Relating ride quality and structural adequacy for pavement rehabilitation and management decisions. Annual Meeting Report, TRB.
  • Sollazzo, G., et al., 2016. Hybrid procedure for automated detection of cracking with 3d pavement data. Journal of Computing in Civil Engineering, ASCE, 30 (6), 0401603. doi:10.1061/(ASCE)CP.1943-5487.0000597.
  • Svozil, D., Kvasnicka, V., and Pospichal, J., 1997. Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, Elsevier, 39 (1), 43–62. doi:10.1016/S0169-7439(97)00061-0.
  • Terzi, S., 2007. Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction and Building Materials, Elsevier, 21 (3), 590–593. doi:10.1016/j.conbuildmat.2005.11.001.
  • Wang, K.C.P. and Li, Q, 2011. Pavement smoothness prediction based on fuzzy and gray theories. Computer-Aided Civil Infrastructure Engineering, 26 (1), 69–76.
  • Woldemariam, W., Hoyos, J. M., and Labi, S., 2016. Estimating annual maintenance expenditures for infrastructure : artificial neural network approach. Journal of Infrastructure Systems, ASCE, 22 (2), 04015025. doi:10.1061/(ASCE)IS.1943-555X.0000280.
  • World Bank., 2014. India Transport Sector, The World Bank Group. Available from: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/SOUTHASIAEXT/EXTSARREGTOPTRANSPORT/0,,contentMDK:20703625~menuPK:868822~pagePK:34004173~piPK:34003707~theSitePK:579598,00.html [Accessed 15 November 2018].
  • Xiao, F., et al., 2012. Model developments of long-term aged asphalt binders. Construction and Building Materials, Elsevier, 37, 248–256. doi:10.1016/j.conbuildmat.2012.07.047.
  • Zhang, Z., et al., 2003. Development of a new methodology for characterizing pavement structural condition for network-level applications. Research Report, TX: Texas Department of Transportation Austin.

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.