A fuzzy inference system for predicting pavement surface damage due to combined action of traffic loading and water

References

• Abaza, K. A, 2016. Back-calculation of transition probabilities for markovian-based pavement performance prediction models. International Journal of Pavement Engineering, 17 (3), 253–264. doi: https://doi.org/10.1080/10298436.2014.993185
   Web of Science ®Google Scholar
• Al-Mansour, A. I., Sinha, K. C., and Kuczek, T., 1994. Effects of routine maintenance on flexible pavement condition. Journal of Transportation Engineering, 120 (1), 65–73. doi: https://doi.org/10.1061/(ASCE)0733-947X(1994)120:1(65)
   Web of Science ®Google Scholar
• Borkar, A. D., and Atulkar, M., 2013. Fuzzy inference system for image processing. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2 (3), 1007–1010.
   Google Scholar
• Chen, S., and Chen, Y., 2002. Automatically constructing membership functions and generating fuzzy rules using genetic algorithms. Cybernetics &Systems, 33 (8), 841–862. doi: https://doi.org/10.1080/01969720290040867
   Web of Science ®Google Scholar
• Christiansen, B, 2007. Atmospheric circulation regimes: can cluster analysis provide the number? Journal of Climate, 20 (10), 2229–2250. doi: https://doi.org/10.1175/JCLI4107.1
   Web of Science ®Google Scholar
• Dehzangi, O., Zolghardi, M.J., Taheri, S., et al., 2007. Efficient fuzzy rule generation: a new approach using data mining principles and rule weighting. In: Fourth international conference on fuzzy systems and knowledge discovery, FSKD 2007, 134–139. IEEE.
   Google Scholar
• Guillaume, S., and Charnomordic, B., 2011. Learning interpretable fuzzy inference systems with FisPro. Information Sciences, 181 (20), 4409–4427. doi: https://doi.org/10.1016/j.ins.2011.03.025
   Web of Science ®Google Scholar
• Guillaume, S., Charnomordic, B., and Lablee, J., 2013. FisPro: an open source portable software for fuzzy inference systems. https://www.fispro.org/documentation/en/inline-help/.
   Google Scholar
• Jain, A. K, 2010. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31 (8), 651–666. doi: https://doi.org/10.1016/j.patrec.2009.09.011
   Web of Science ®Google Scholar
• Jang, J, 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23 (3), 665–685. doi: https://doi.org/10.1109/21.256541
   Web of Science ®Google Scholar
• Karlson, T. K, 2005. Evaluation of cyclic pore pressure induced moisture damage in asphalt pavement. Gainesville, FL: University of Florida.
   Google Scholar
• Kennedy, T. W., Roberts, F. L., and Lee, K. W., 1983. Evaluation of moisture effects on asphalt concrete mixtures. Transportation Research Record, 911, 134–143.
   Google Scholar
• Kim, Y., et al., 2008. Evaluation of moisture damage mechanisms and effects of hydrated lime in asphalt mixtures through measurements of mixture component properties and performance testing. Journal of Materials in Civil Engineering, 20 (10), 659–667. doi: https://doi.org/10.1061/(ASCE)0899-1561(2008)20:10(659)
   Web of Science ®Google Scholar
• Landau, S, 2004. A handbook of statistical analyses using SPSS. Boca Raton, FL: Chapman & Hall.
   Google Scholar
• Lindly, J. K., and Elsayed, A. S., 1995. Estimating permeability of asphalt-treated bases. Transportation Research Record, 1504, 103–111.
   Google Scholar
• Mahmood, M. S., 2015. Network-level maintenance decisions for flexible pavement using a soft computing-based framework. Nottingham: University of Nottingham Trent.
   Google Scholar
• Mahmood, M., Rahman, M., and Mathavan, S., 2018. Multi-input deterioration-prediction model for asphalt road networks. In: Proceedings of the Institution of Civil Engineers-Transport. ICE, 1–12.
   Google Scholar
• Miller, J. S. and Bellinger, W. Y., 2003. Distress identification manual for the long-term pavement performance program. Federal Highway Administration (FHWA). Fourth revised Edition.
   Google Scholar
• Naik, V. C, 2004. Fuzzy c-means clustering approach to design a warehouse layout. Graduate School Theses and dissertations. University of South Florida, FL.
   Google Scholar
• Negnevitsky, M, 2002. Artificial intelligence: a guide to intelligent systems. Essex: Addison Wesley.
   Google Scholar
• Nelles, O., Fischer, M., and Muller, B., 1996. Fuzzy rule extraction by a genetic algorithm and constrained nonlinear optimization of membership functions. In: Proceedings of the fifth IEEE international conference on fuzzy systems, New Orleans, LA, USA. IEEE, 213–219.
   Google Scholar
• Saeed, F., et al., 2015. The state of pothole management in UK local authority. In: Bituminous mixtures and pavements, Thessaloniki, Greecevi Boca Raton, FL: CRC Press, vol. vi, 153–159.
   Google Scholar
• Saeed, F., et al., 2019. Deterioration prediction model of asphalt surface damage due to combined action of water and dynamic loading. In: 98th TRB annual meeting, January 13–17, Washington, D.C.
   Google Scholar
• Saeed, F., Rahman, M., and Chamberlain, D., 2018a. A novel laboratory test method to measure dynamic water pressure underneath a cracked concrete pavement. IJPEAT: The International Journal of Pavement Engineering and Asphalt Technology, 19 (2).
   Google Scholar
• Saeed, F., Rahman, M., and Chamberlain, D., 2018b. A novel test method to evaluate the performance of asphalt surface due to combined action of water and dynamic loading. Construction & Building Materials, 196, 530–538. doi: https://doi.org/10.1016/j.conbuildmat.2018.10.225
   Web of Science ®Google Scholar
• Saeed, F., Rahman, M., and Chamberlain, D., 2018c. Impact of tire and traffic parameters on water pressure in pavement. Journal of Transportation Engineering, Part B: Pavements (ASCE), 144 (4). doi:https://doi.org/10.1061/JPEODX.0000065.
   Google Scholar
• Saleh, S. E., Awda, G. J., and Ahmed, N. G., 2008. Development of pavement condition index model for flexible pavement in Baghdad city. Journal of Engineering, 14 (1), 2120–2135.
   Google Scholar
• Sheta, A, 2006. Software effort estimation and stock market prediction using takagi-sugeno fuzzy models. In: 2006 IEEE international conference on fuzzy systems, Vancouver, BC, Canada. 171–178.
   Google Scholar
• Sun, L. and Gu, W, 2011. Pavement condition assessment using fuzzy logic theory and analytic hierarchy process. Journal of Transportation Engineering, 137 (9). doi: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000239
   Google Scholar
• Vaidehi, V., et al., 2008. A prediction system based on fuzzy logic.
   Google Scholar
• Wang, L., and Mendel, J. M., 1992. Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22 (6), 1414–1427. doi: https://doi.org/10.1109/21.199466
   Web of Science ®Google Scholar
• Willway, T., et al., 2008. The effects of climate change on highway pavements and how to minimise them: technical report. Published Project Report, TRL.
   Google Scholar