Life-cycle performance prediction for rigid runway pavement using artificial neural network

References

• Abdesssemed, M., Kenai, S., and Bali, A, 2015. Experimental and numerical analysis of the behavior of an airport pavement reinforced by geogrids. Construction & Building Materials, 94 (9), 547–554. doi:10.1016/j.conbuildmat.2015.07.037.
   Google Scholar
• Alvappillai, A., Zaman, M., and Laguros, J, 1992. Finite element algorithm for jointed concrete pavements subjected to moving aircraft. Computers & Geotechnics, 14 (3), 121–147. doi:10.1016/0266-352X(92)90030-W.
   Google Scholar
• Baziar, M. H., Azizkandi, A. S., and Kashkooli, A, 2015. Prediction of pile settlement based on cone penetration test results: an ANN approach. KSCE Journal of Civil Engineering, 19 (1), 98–106. doi:10.1007/s12205-012-0628-3.
   Web of Science ®Google Scholar
• Bianchini, A., and Bandini, P, 2010. Prediction of pavement performance through Neuro-fuzzy reasoning. Computer-Aided Civil and Infrastructure Engineering, 25 (1), 39–54. doi:10.1111/j.1467-8667.2009.00615.x.
   Web of Science ®Google Scholar
• Broutin, M., and Sadoun, A, 2016. Advanced modelling for rigid pavement assessment using HWD. Transportation Research Procedia, 14, 3572–3581. doi:10.1016/j.trpro.2016.05.425.
   Google Scholar
• Butt, A. A., et al., 1987. Pavement performance prediction model using the Markov process. Transportation Research Record 1123, Transportation Research Board, Washington, D.C., 12–19.
   Google Scholar
• Chandra, S., et al., 2013. Relationship between pavement roughness and distress parameters for Indian highways. Journal of Transportation Engineering, 139 (5), 467–475. doi:10.1061/(ASCE)TE.1943-5436.0000512.
   Google Scholar
• Federal Aviation Administration, 2009. Airport pavement design and evaluation, Advisory Circular 150/5320-6E. Washington, D.C.: Federal Aviation Administration (FAA).
   Google Scholar
• Gendreau, M., and Soriano, P, 1998. Airport pavement management systems: an appraisal of existing methodologies. Transportation Research Part A Policy & Practice, 32 (3), 197–214. doi:10.1016/S0965-8564(97)00008-6.
   Web of Science ®Google Scholar
• Gopalakrishnan, K., 2005. Prediction of national airport pavement test facility pavement layer moduli from heavy weight deflectometer test data using artificial neural networks. Proceedings of the 2005 Mid-Continent Transportation Research Symposium, Ames, Iowa, 313–320.
   Google Scholar
• Hata, Y., Ichii, K., and Nozu, A, 2012. Three-dimensional non-linear FEM analysis of a seismic induced crack at an airport runway. Soil Dynamics & Earthquake Engineering, 42 (4), 105–118. doi:10.1016/j.soildyn.2012.06.009.
   Google Scholar
• International Civil Aviation Organization, 1983. Aerodrome Design Manual, Part 3, Pavements, Doc 9157-AN/901, 2nd Ed.
   Google Scholar
• Kim, D., et al., 2017. Mechanistic performance evaluation of pavement sections containing RAP and WMA additives in Manitoba. Construction and Building Materials, 133, 39–50. doi:10.1016/j.conbuildmat.2016.12.035.
   Web of Science ®Google Scholar
• Kim, S. H., Yang, J., and Jeong, J. H, 2014. Prediction of subgrade resilient modulus using artificial neural network. KSCE Journal of Civil Engineering, 18 (5), 1372–1379. doi:10.1007/s12205-014-0316-6.
   Web of Science ®Google Scholar
• Mehdi, P, 2002. Artificial neural network modeling of pavement performance using expert judgement. Road Materials and Pavement Design, 3 (4), 383–384. doi:10.1080/14680629.2002.9689931.
   Google Scholar
• Mehta, Y., Cleary, D., and Ali, A. W, 2017. Field cracking performance of airfield rigid pavements. Journal of Traffic and Transportation Engineering (English Edition), 4 (4), 380–387. doi:10.1016/j.jtte.2017.05.010.
   Google Scholar
• Meier, R. W., and Rix, G. J, 1994. Back calculation of flexible pavement moduli using artificial neural networks. Transportation Research Record 1448, TRB, National Research Council, Washington, D.C., 75–82, 1995.
   Google Scholar
• Moghadas, N. F., and Zakeri, H, 2011. An expert system based on wavelet transform and radon neural network for pavement distress classification. Expert Systems with Applications, 38 (6), 7088–7101. doi:10.1016/j.eswa.2010.12.060.
   Web of Science ®Google Scholar
• Mun, S., and Kim, Y. R, 2004. Determination of subgrade stiffness under intact Portland cement concrete slabs for rubblization projects. KSCE Journal of Civil Engineering, 8 (5), 527–533.
   Google Scholar
• Pan, N.-F., et al., 2011. Pavement performance prediction through fuzzy regression. Expert Systems with Applications, 38 (8), 10010–10017. doi:10.1016/j.eswa.2011.02.007.
   Web of Science ®Google Scholar
• Panas, A., Pantouvakis, J. P., and Lambropoulos, S, 2012. Non-linear analysis of concrete pavement construction by the use of artificial neural networks. Procedia-Social and Behavioral Sciences, 48 (3), 3671–3680. doi:10.1016/j.sbspro.2012.06.1329.
   Google Scholar
• Paris, P. C., and Erdogan, F, 1963. A critical analysis of crack propagation laws. Transactions of the ASME. Journal of Basic Engineering, Series, D, 85 (3), 528–534.
   Google Scholar
• Park, D. W, 2010. Evaluation of predicted pavement fatigue life based on surface profiles and asphalt mixture types. KSCE Journal of Civil Engineering, 14 (2), 191–196. doi:10.1007/s12205-010-0191-8.
   Web of Science ®Google Scholar
• Premkumar, L., and Vavrik, W. R, 2016. Enhancing pavement performance prediction models for the Illinois tollway system. International Journal of Pavement Research and Technology, 9 (1), 14–19. doi:10.1016/j.ijprt.2015.12.002.
   Google Scholar
• Ramsamooj, D. V., Lin, G. S., and Ramadan, J, 1998. Stresses at joints and cracks in highway and airport pavements. Engineering Fracture Mechanics, 60 (5), 507–518. doi:10.1016/S0013-7944(98)00059-9.
   Web of Science ®Google Scholar
• Ranasinghe, R.A.T.M., et al., 2017. Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results. Journal of Rock Mechanics and Geotechnical Engineering, 9 (2), 340–349. doi:10.1016/j.jrmge.2016.11.011.
   Web of Science ®Google Scholar
• Rezaei-Tarahomi, A., et al., 2017. Development of rapid three-dimensional finite-element based rigid airfield pavement foundation response and moduli prediction models. Transportation Geotechnics, 13, 81–91. doi:10.1016/j.trgeo.2017.08.011.
   Web of Science ®Google Scholar
• Ruta, P., Krawczyk, B., and Szydło, A., 2015. Identification of pavement elastic moduli by means of impact test. Engineering Structures, 100 (10), 201–211. doi:10.1016/j.engstruct.2015.05.038.
   Google Scholar
• Saleh, M., 2015. Prediction of rigid pavement responses under axle loads using artificial neural network. International Journal of Pavement Research & Technology, 8 (1), 10–16.
   Google Scholar
• Sollazzo, G., Fwa, T. F., and Bosurgi, G., 2017. An ANN model to correlate roughness and structural performance in asphalt pavements. Construction & Building Materials, 134 (3), 684–693. doi:10.1016/j.conbuildmat.2016.12.186.
   Google Scholar
• Sultan, S. A., and Guo, Z., 2016. Evaluating life cycle costs of perpetual pavements in China using operational pavement management system. International Journal of Transportation Science and Technology, 5 (2), 103–109. doi:10.1016/j.ijtst.2016.09.007.
   Google Scholar
• Taheri, M. R., Zaman, M. M., and Alvappillai, A., 1990. Dynamic response of concrete pavements to moving aircraft. Applied Mathematical Modelling, 14 (11), 562–575.
   Web of Science ®Google Scholar
• Tarawneh, B., and Imam, R., 2014. Regression versus artificial neural networks: predicting pile setup from empirical data. KSCE Journal of Civil Engineering, 18 (4), 1018–1027. doi:10.1007/s12205-014-0072-7.
   Web of Science ®Google Scholar
• Terzi, S., 2007. Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks. Construction & Building Materials, 21 (3), 590–593. doi:10.1016/j.conbuildmat.2005.11.001.
   Web of Science ®Google Scholar
• Wang, K. C. P., and Li, Q., 2011. Pavement smoothness prediction based on fuzzy and gray theories. Computer-Aided Civil and Infrastructure Engineering, 26 (1), 69–76. doi:10.1111/j.1467-8667.2009.00639.x.
   Web of Science ®Google Scholar
• Yu, B., and Lu, Q., 2012. Life cycle assessment of pavement: methodology and case study. Transportation Research Part D: Transport and Environment, 17 (5), 380–388. doi:10.1016/j.trd.2012.03.004.
   Web of Science ®Google Scholar