Abstract
The repair method for pavements should be selected considering the structural capacity of sublayers, in addition to the conditions observed at the pavement surface, to reduce the recurrence of distress in the repaired area. However, it is practically impossible to include the structural capacity of sublayers in the database of the pavement management system (PMS) because this would require additional tests in all expressway sections. Therefore, an artificial neural network model for predicting the indirect tensile strength (ITS) of the intermediate layer of all asphalt pavement sections in an expressway was developed in this study, taking the international roughness index, rut depth, surface distress, and equivalent single axle load as independent variables. The ITS of specimens cored from target sections was measured in the laboratory, and the PMS data for the target sections were collected. The ITS was predicted by conducting a feedforward process prior to the training step. When the error between the predicted and measured ITSs exceeded the allowable error, the model was repetitively trained using the resilient backpropagation method until the error fell within the acceptable boundary. The model was validated by analyzing the correlations between the ITSs predicted from the data of the training and test sets. Finally, the model was complemented by the corresponding minimum and maximum values of the ITS measured at the target section.
Similar content being viewed by others
References
MOLIT: Road Practice Manual. Ministry of Land, Infrastructure and Transport (2019)
MOLIT: Yearbook of Road Statistics. Ministry of Land, Infrastructure and Transport, Korea (2019)
Lee, J.G.; Jeon, G.S.; Nam, M.S.; Kim, K.S.; Lee, J.S.: A Study on an Establishment of Countermeasures for Adaption of Expressways to Climate. Report, Report, Expressway and Transportation Research Institute, Korea Expressway Corporation (2017)
KEC: Highway Pavement Maintenance System Operation Manual. Korea Expressway Corporation (1996)
MOLIT: Annual Research Report on National Highway Pavement Management System in 2018. Ministry of Land, Infrastructure and Transport, Korea (2019)
Kennedy, T.W.; Hudson, W.R.: Application of the indirect tensile test to stabilized materials. Highway Res. Rec. 235, 36–48 (1968)
Islam, M.R.; Hossain, M.I.; Tarefder, R.A.: A study of asphalt aging using indirect tensile strength test. Constr. Build. Mater. 95, 218–223 (2015)
Baus, R.L.; Stires, N.R.: Mechanistic-Empirical Pavement Design Guide Implementation. Technical Report, No. FHWA-SC-10-01, University of South Carolina, US (2010)
Kim, D.H.; Lee, J.M.; Moon, K.H.; Park, J.S.; Suh, Y.C.; Jeong, J.H.: Development of remodeling index model to predict priority of large-scale repair works of deteriorated expressway concrete pavements in Korea. KSCE J. Civ. Eng. 23(5), 2096–2107 (2019)
Choi, J.H.; Adams, T.M.; Bahia, H.U.: Pavement roughness modeling using back-propagation neural networks. Comput. Aided Civ. Infrastruct. Eng. 19(4), 295–303 (2004)
Achanta, A.S.; Kowalski, J.G.; Rhodes, C.T.: Artificial neural networks: implications for pharmaceutical sciences. Drug Dev. Ind. Pharm. 21(1), 119–155 (1995)
Kim, S.; Gopalakrishnan, K.; Ceylan, H.: Neural networks application in pavement infrastructure materials. Intell. Soft Comput. Infrastruct. Syst. Eng. 259, 47–66 (2009)
KS F 2382: Standard Test Method for Indirect Tension of Asphalt Mixtures. Korean Industrial Standards, Korean Standards Association, Seoul, Korea (2013)
Gillespie, T.D.; Paterson, W.; Sayers, M.W.: Guidelines for Conducting and Calibrating Road Roughness Measurements. World Bank Technical Paper, No. WTP 46, World Bank Group, Washington, DC, US (1986)
Mamlouk, M.; Vinayakamurthy, M.; Underwood, B.S.; Kaloush, K.E.: Effects of the international roughness index and rut depth on crash rates. Transp. Res. Rec. 2672(40), 418–429 (2018)
Mubaraki, M.: Highway subsurface assessment using pavement surface distress and roughness data. Int. J. Pavement Res. Technol. 9(5), 393–402 (2016)
MOLIT: Road Pavement Structure Design Manual. Ministry of Land, Infrastructure and Transport, Korea (2015)
Kline, R.B.: Methodology in the Social Sciences: Principles and Practice of Structural Equation Modeling, 2nd edn. Guilford Press, New York (2005)
Altun, H.; Bilgil, A.; Fidan, B.C.: Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Syst. Appl. 32, 599–605 (2007)
Guh, R.S.: Effects of non-normality on artificial neural network based control chart pattern recognizer. J. Chin. Inst. Ind. Eng. 19(6), 13–22 (2002)
Kumar, U.A.: Comparison of neural networks and regression analysis: a new insight. Expert Syst. Appl. 29(2), 424–430 (2005)
Melesse, A.M.; Ahmad, S.; McClain, M.E.; Wang, X.; Lim, Y.H.: Suspended sediment load prediction of river systems: an artificial neural network approach. Agric. Water Manag. 98(5), 855–866 (2011)
Wilson, E.B.; Hilferty, M.M.: The distribution of Chi square. Proc. Natl. Acad. Sci. U.S.A. 17(12), 684–688 (1931)
Matsumoto, M.; Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)
Gong, H.; Sun, Y.; Mei, Z.; Huang, B.: Improving accuracy of rutting prediction for mechanistic-empirical pavement design guide with deep neural networks. Constr. Build. Mater. 190, 710–718 (2018)
Rousseeuw, P.J.; Hubert, M.: Anomaly detection by robust statistics. WIREs Data Min. Knowl. Discov. 8(2), e1236 (2018)
Nair, V.; Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel (2010)
Abambres, M.; Ferreira, A.: Application of artificial neural networks in pavement management. In: Proceedings of the International Conference on Traffic Development, Logistics and Sustainable Transport, 1-11, Opatija, Croatia, 2017 (2017)
Gandhi, T.; Xiao, F.; Amirkhanian, S.N.: Estimating indirect tensile strength of mixtures containing anti-stripping agents using an artificial neural network approach. Int. J. Pavement Res. Technol. 2(1), 1–12 (2009)
Günther, F.; Fritsch, S.: Neuralnet: training of neural networks. R. J. 2(1), 30–38 (2010)
Riedmiller, M.: Advanced supervised learning in multi-layer perceptions—from backpropagation to adaptive learning algorithms. Comput. Stand. Interfaces 16(3), 265–278 (1994)
Igel, C.; Hüsken, M.: Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50, 105–123 (2003)
Anastasiadis, A.D.; Magoulas, G.D.; Vrahatis, M.N.: New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 64, 253–270 (2005)
Giustolisi, O.; Laucelli, D.: Improving generalization of artificial neural networks in rainfall–runoff modelling. Hydrol. Sci. J. 50(3), 439–457 (2005)
Garson, G.D.: A comparison of neural network and expert systems algorithms with common multivariate procedures for analysis of social science data. Soc. Sci. Comput. Rev. 9(3), 399–434 (1991)
Goh, A.T.: Back-propagation neural networks for modeling complex systems. Artif. Intell. Eng. 9(3), 143–151 (1995)
Acknowledgments
This research was financially sponsored by the Korea Expressway Corporation (KEC) and Inha University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, DH., Lee, SJ., Moon, KH. et al. Prediction of Indirect Tensile Strength of Intermediate Layer of Asphalt Pavements Using Artificial Neural Network Model. Arab J Sci Eng 46, 4911–4922 (2021). https://doi.org/10.1007/s13369-020-05270-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-05270-3