Presenting a new estimation technique to model International Roughness Index and treatment improvement

Document Type : Research Paper

Authors

Amirkabir University of Technology, Tehran, Iran

Abstract

International Roughness Index is one of the most important indices to assess the pavement quality, and it is generally employed in pavement maintenance planning. In this study, by virtue of differential evolutionary programming and Long Term Pavement Performance data, two prediction models are introduced to predict pavement deterioration function and treatments’ improvement. For pavement deterioration modeling, climate condition, traffic, and pavement structural features are taken into account. Besides, the most-applicable pavement treatments utilized in Iran are considered in this investigation. 520 and 336 data are applied to model pavement deterioration and treatments’ improvement. The predicted models have a very high accuracy and can be used in various fields. The pavement deterioration model and treatments’ improvement model reach the coefficient determination of 99% and 97% for both training and testing data, which indicates the high-level accuracy of the introduced models. The treatments’ improvement model provides the mean squared error of 0.2 approximately.

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References

Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M. and Afify, H. A. 2018. “International roughness index prediction model for flexible pavements”. Int. J. Pavement Eng., 21(1): 88-99. 
Chan, Y. H. 2003. “Biostatistics 104: Correlation analysis”. Singapore Med. J., 44: 614-619.
Choi, J., Adams, T. M. and Bahia, H. U. 2004. “Pavement roughness modeling using back-propagation neural networks”. Computer-Aided Civ. Infrastruct. Eng., 19(4): 295-303.
Dalla Rosa, F., Liu, L. and Gharaibeh, N. G. 2017. “IRI prediction model for use in network-level pavement management systems”. J. Transport. Eng., Part B: Pavements, 143(1): 04017001.
Golafshani, E. M. and Behnood, A. 2019. “Estimating the optimal mix design of silica fume concrete using biogeography-based programming”. Cement Concrete Compos., 96: 95-105.
Gordon, R. A. 2015. Regression analysis for the social sciences. Routledge, New York.
Labi, S. and Sinha, K. C. 2003. “The effectiveness of maintenance and its impacts on capital expenditures”. Technical Report No. FHWA/IN/JTRP 2002-27, Joint Transportation Research Program, School of Civil Engineering, Purdue University, West Lafayette, Indiana.
Lin, J., Yau, J. and Hsiao, L. 2003. “Correlation analysis between international roughness index (IRI) and pavement distress by neural network”. 82th Transportation Research Board Annual Meeting (CD ROM), Washington, D.C.
Lu, P. and Tolliver, D. 2012. “Pavement treatment short-term effectiveness in IRI change using LTPP data”. J. Transport. Eng., 138(11): 1297-1302.
Mazari, M. and Rodriguez, D. D. 2016. “Prediction of pavement roughness using a hybrid gene expression programming-neural network technique”. J. Traffic Transport. Eng., 3(5): 448-455.
Mirzahosseini, M., Jiao, P., Barri, K., Riding, K. and Alavi, A. H. 2019. “New machine learning prediction models for compressive strength of concrete modified with glass cullet”. Eng. Comput., 36: 876-898.
Mohamed Jaafar, Z. F. B., Uddin, W. and Najjar, Y. 2016. “Asphalt pavement roughness modeling using the artificial neural network and linear regression approaches for LTPP Southern region”. Transportation Research Board 95th Annual Meeting, Washington, D.C.
Naseri, H., Jahanbakhsh, H., Moghadas Nejad, F. and Golroo, A. 2020. “Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages”. AUT J. Civ. Eng.,  4(4). DOI: 10.22060/ajce.2019.16124.5569
Owolabi, A. O., Sadiq, O. M. and Abiola, O. S. 2012. “Development of performance models for a typical flexible road pavement in Nigeria”. Int. J. Traffic Transport Eng., 2(3): 178-184.
Shahin, M. Y. 1994. “Pavement maintenance management for airports, roads, and parking lots”. Chapman & Hall, New York.
Shirzadi Javid, A. A., Naseri, H. and Etebari Ghasbeh, M. A. 2020. “Estimating the optimal mixture design of concrete pavements using a numerical method and meta-heuristic algorithms”. Iran. J. Sci. Tech.-Trans. Civ. Eng., 45: 913-927.
Storn, R. and Price, K. 1997. “Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces”. TR-95-012, 1995 [Online]. Available: http://http.icsi.berkeley.edu/~storn/litera.html.
Varadarajan, M. and Swarup, K. S. 2008. “Differential evolutionary algorithm for optimal reactive power dispatch”. Electr. Power Energy Syst., 30: 435-441.
Wang, K. C. P., Li, Q., Hall, K. D. and Elliott, R. P. 2007. “Experimentation with Gray theory for pavement smoothness prediction”. Transport. Res. Record, 1990: 3-13.
Ziari, H., Sobhani, J., Ayoubinejad, J. and Hartmann, T. 2016. “Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods”. Int. J. Pavement Eng., 17(9): 776-788.

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History

  • Receive Date: 19 November 2020
  • Revise Date: 28 September 2021
  • Accept Date: 29 September 2021

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