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Research Article

Effects of maintenance, traffic and climate condition on International Roughness Index of flexible pavement

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Article: 2038382 | Received 08 Nov 2021, Accepted 31 Jan 2022, Published online: 17 Feb 2022

References

  • AASHTO, 2008. Mechanistic-empirical pavement design guide, a manual of practice. Washington, DC: American Association of State Highway and Transportation Officials, 1–204.
  • Abdelaziz, N., et al., 2020. International roughness index prediction model for flexible pavements. International Journal of Pavement Engineering, 21 (1), 88–99. doi:10.1080/10298436.2018.1441414.
  • Al-Suleiman, T.I., and Shiyab, A.M.S, 2003. Prediction of pavement remaining service life using roughness data – case study in Dubai. International Journal of Pavement Engineering, 4 (2), 121–129. doi:10.1080/10298430310001634834.
  • Ali, A., et al., 2020. A k-nearest neighbours based ensemble via optimal model selection for regression. IEEE Access, 8, 132095–132105. doi:10.1109/ACCESS.2020.3010099.
  • Arevalos, S., Lopez-Pires, F., and Baran, B, 2016. A comparative evaluation of algorithms for auction-based cloud pricing prediction. 2016 IEEE International Conference on Cloud Engineering (IC2E). Berlin, Germany, 99–108. doi:10.1109/IC2E.2016.45.
  • Baykasoğlu, A., et al., 2008. Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications, 35 (1–2), 111–123. doi:10.1016/j.eswa.2007.06.006.
  • Bhatia, N., and Vandana, A, 2010. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security, 8 (2), 302–305.
  • Breiman, L., 1997. Arcing the edge (Report No. 486). Statistics Department, University of California at Berkeley.
  • Breiman, L, 1999. Random forests–Random features (Report No. 567). Statistics Department, University of California at Berkeley.
  • Demirci, M., et al., 2021. Monthly groundwater level modeling using data mining approaches. Air and Water–Components of the Environment Conference Proceedings, 75–86. doi:10.24193/AWC2021_07.
  • Elkins, G. E., et al., 2003. Long-term pavement performance information management system: Pavement Performance Database User Reference Guide (Report No. FHWA-RD-03-088). Federal Highway Administration, Washington, DC.
  • Friedman, J. H, 2001. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29 (5), 1189–1232.
  • Friedman, J. H, 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4), 367–378. doi:10.1016/S0167-9473(01)00065-2.
  • Gaweda, A. E., and Zurada, J. M, 2003. Data-driven linguistic modeling using relational fuzzy rules. IEEE Transactions on Fuzzy Systems, 11 (1), 121–134. doi:10.1109/TFUZZ.2002.803491.
  • George, K. P, 2000. MDOT pavement management system. FHWA/MSDOT-RD-00-119.
  • Georgiou, P., Plati, C., and Loizos, A, 2018. Soft computing models to predict pavement roughness: as comparative study. Advances in Civil Engineering, 2018, 1–8. doi:10.1155/2018/5939806.
  • Gharieb, M., and Nishikawa, T, 2021. Development of roughness prediction models for Laos national road network. CivilEng, 2, 158–173. doi:10.3390/civileng2010009.
  • Gillespie, T. D., 1992. Everything you always wanted to know about the IRI, but were afraid to ask!. Road Profile Users Group Meeting. Lincoln, Nebraska: The University of Michigan Transportation Research Institute.
  • Gong, H., et al., 2018. Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189 (2018), 890–897. doi:10.1016/j.conbuildmat.2018.09.017.
  • James, G., et al., 2013. An introduction to statistical learning: with applications in R. New York: Springer. doi:10.1007/978-1-4614-7138-7
  • Kaloop, M. R., et al., 2020. A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements. International Journal of Pavement Engineering, 1–15. doi:10.1080/10298436.2020.1776281.
  • Karatas, I., and Budak, A, 2021. Prediction of labor activity recognition in construction with machine learning algorithms. Icontech International Journal, 5 (3), 38–47. doi:10.46291/ICONTECHvol5iss3pp38-47.
  • Kaya, Y.Z., et al., 2021. Estimation of daily evapotranspiration in Košice City (Slovakia) using several soft computing techniques. Theoretical and Applied Climatology, 144, 287–298. doi:10.1007/s00704-021-03525-z.
  • Kayadelen, C, 2008. Estimation of effective stress parameter of unsaturated soils by using artificial neural networks. International Journal for Numerical and Analytical Methods in Geomechanics, 32 (9), 1087–1106. doi:10.1002/nag.660.
  • Kayadelen, C., et al., 2021b. Sequential minimal optimization for local scour around bridge piers. Marine Georesources & Geotechnology. doi:10.1080/1064119X.2021.1907635.
  • Kayadelen, C., Altay, G., and Önal, Y, 2021a. Numerical simulation and novel methodology on resilient modulus for traffic loading on road embankment. International Journal of Pavement Engineering. doi:10.1080/10298436.2021.1886296.
  • Kulkarni, S. G., and Babu, M. V, 2013. Introspection of various K-nearest neighbor techniques. International Journal of Advances in Computer Science and Its Applications, 3 (2), 103–106.
  • Kutylowska, M, 2018. K-nearest neighbours method as a tool for failure rate prediction. Periodica Polytechnica Civil Engineering, 62 (2), 318–322. doi:10.3311/PPci.10045.
  • Li, W., et al., 2019. International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization. Expert Systems with Applications: X, 10 (2), 100006. doi:10.1016/j.eswax.2019.100006.
  • Lin, L., Wang, Q., and Sadek, A. W, 2016. A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations. Accident Analysis & Prevention, 91, 114–126. doi:10.1016/j.aap.2016.03.001.
  • Long Term Pavement Performance (LTPP), 2021. Informational Management Systems Database. Available from: https://infopave.fhwa.dot.gov/ [Accessed 8 Apr 2021].
  • Lydia, M., et al., 2016. Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy Conversion and Management, 112, 115–124. doi:10.1016/j.enconman.2016.01.007.
  • Mark H., Eibe, et al., 2009. The WEKA data mining software: an update. SIGKDD Explorations, 11, 1. doi:10.1145/1656274.1656278.
  • Mazari, M., and Rodriguez, D. D, 2016. Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition), 3 (5), 448–455. doi:10.1016/j.jtte.2016.09.007.
  • Najjar, Y. M., and Basheer, I. A, 1996. Discussion: stress–strain modelling of sands using artificial neural networks. Journal of Geotechnical and Engineering, 122 (11), 949–950. doi:10.1061/(ASCE)0733-9410(1996)122:11(949).
  • Natekin, A., and Knoll, A, 2013. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21. doi:10.3389/fnbot.2013.00021.
  • Platt, J, 1998. Sequential minimal optimization: a fast algorithm for training support vector machines (Report No. MSR-TR-98-14). Microsoft Research.
  • Quinlan, J. R, 1992. Learning with continuous classes. 5th Australian Joint Conference on Artificial Intelligence, 92, 343–348. doi:10.1142/9789814536271.
  • Robbins, T., and Tran, P. E., 2016. A synthesis report: value of pavement smoothness and ride quality to roadway users and the impact of pavement roughness on vehicle operating costs (Report No. 16-03). National Center for Asphalt Technology (NCAT) at Auburn University.
  • Shin, J. H., and Park, H. I., 2010. Neural network formula for local Scour at Piers using field data. Marine Georesources & Geotechnology, 28 (1), 37–48. doi:10.1080/1064119X.2021.1907635.
  • Sihag, P., Tiwari, N. K., and Ranjan, S, 2019. Prediction of cumulative infiltration of sandy soil using random forest approach. Journal of Applied Water Engineering and Research, 7 (2), 118–142. doi:10.1080/23249676.2018.1497557.
  • Singh, P., and Agrawal, S, 2013. Node localization in wireless sensor networks using the M5P tree and SMOreg algorithms. 5th International Conference and Computational Intelligence and Communication Networks. doi:104-104. 10.1109/CICN.2013.32.
  • Smola, A. J, and Scholkopf, B., 1998. A tutorial on support vector regression (Report No. NC2-TR-1998-030). NeuroCOLT2 Technical Report Series. doi:10.1023/B:STCO.0000035301.49549.88.
  • Sun, B., et al., 2017. Flow-aware WPT k-nearest neighbours regression for short-term traffic prediction. IEEE Symposium on Computers and Communications, 48–53. doi:10.1109/ISCC.2017.8024503.
  • Tavakoli, A., Lapin, M. L., and Ludwig, F, 1992. PMSC: pavement management system for small communities. Journal of Transport Engineering, 118 (2), 270–280. doi:10.1061/(ASCE)0733-947X(1992)118:2(270).
  • Taylor, K. E, 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106 (D7), 7183–7192. doi:10.1029/2000JD900719.
  • Terzi, S, 2013. Modeling for pavement roughness using the ANFIS approach. Journal of Advances in Engineering Software, 57, 59–64. doi:10.1016/j.advengsoft.2012.11.013.
  • Truccolo, W., and Donoghue, J. P, 2007. Nonparametric modeling of neural point processes via stochastic gradient boosting regression. Neural Computation, 19 (3), 672–705. doi:10.1162/neco.2007.19.3.672.
  • Üneş, F., et al., 2019a. Estimating dam reservoir level fluctuations using data-driven techniques. Polish Journal of Environmental Studies, 28 (5), 3451–3462. doi:10.15244/pjoes/93923.
  • Üneş, F., et al., 2019b. Modeling of dam reservoir volume using generalized regression neural network, support vector machines and m5 decision tree models. Applied Ecology and Environmental Research, 17, 3. doi:10.15666/aeer/1703_70437055.
  • Wang, L., et al., 2012. Study and application of non-linear time series prediction in ground source heat pump system. 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), 3522-3525. doi:10.1109/CECNet.2012.6201751.
  • 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.
  • Wang, Y., and Witten, I. H, 1997. Inducing model trees for continuous classes. Proceedings of the Ninth European Conference on Machine Learning, 9, 128–137.
  • Ye, J., et al., 2009. Stochastic gradient boosted distributed decision trees. Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2061-2064. doi:10.1145/1645953.1646301.
  • Yu, B., et al., 2016. K-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition. Journal of Transportation Engineering, 142 (6), 04016018. doi:10.1061/(ASCE)TE.1943-5436.0000816.
  • Zhang, L.N., He, D.P., and Zhao, Q.Q, 2021. Modeling of international roughness index in seasonal frozen area. Magazine of Civil Engineering, 104, 4. Article No. 10402. doi: 10.34910/MCE.104.2.

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