-Abaza, K. A., Ashur, S. A., & Al-Khatib, I. A., (2004), “Integrated pavement management system with a Markovian prediction model”, Journal of Transportation Engineering, 130(1), pp.24-33.
-Abed, M. S., (2020), “Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index”, Journal of Engineering, 26(12), pp.81-94.
-Agatonovic-Kustrin, S., & Beresford, R., (2000), “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research”, Journal of pharmaceutical and biomedical analysis, 22(5), pp.717-727.
-Arhin, S. A., Williams, L. N., Ribbiso, A., & Anderson, M. F., (2015), “Predicting pavement condition index using international roughness index in a dense urban area”, Journal of Civil Engineering Research, 5(1), pp.10-17.
-Bui, D. K., Nguyen, T. N., Ngo, T. D., & Nguyen-Xuan, H., (2020), “An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings”, Energy, 190, 116370.
-Chan, C. Y., Huang, B., Yan, X., & Richards, S. (2010), “Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)”, Journal of advanced transportation, 44(3),
pp.150-161.
-Dewan, S., & Smith, R., (2002), “Estimating IRI from pavement distresses to calculate vehicle operating costs for the cities and counties of San Francisco Bay area” Transportation Research Record.
-Elhadidy, A. A., El-Badawy, S. M., & Elbeltagi, E. E., (2021), “A simplified pavement condition index regression model for pavement evaluation”, International Journal of Pavement Engineering, 22(5), pp.643-652.
-Hasibuan, R. P., & Surbakti, M. S., (2019), “Study of Pavement Condition Index (PCI) relationship with International Roughness Index (IRI) on Flexible Pavement”, Paper presented at the MATEC web of conferences.
-Kamboozia, N., Ziari, H., & Behbahani, H. (2018), “Artificial neural networks approach to predicting rut depth of asphalt concrete by using of visco-elastic parameters”, Construction and Building Materials, 158, pp.873-882.
-Kukreja, H., Bharath, N., Siddesh, C., & Kuldeep, S., (2016), “An introduction to artificial neural network”, Int J Adv Res Innov Ideas Educ, 1, pp.27-30.
-Mactutis, J. A., Alavi, S. H., & Ott, W. C., (2000), “Investigation of relationship between roughness and pavement surface distress based on WesTrack project”, Transportation Research Record, 1699(1), pp.107-113.
-Mirabdolazimi, S., & Shafabakhsh, G., (2017), “Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique”, Construction and Building Materials, 148, pp.666-674.
-Park, K., Thomas, N. E., & Wayne Lee, K. (2007), “Applicability of the international roughness index as a predictor of asphalt pavement condition”, Journal of Transportation Engineering, 133(12),
pp.706-709.
-Pérez-Acebo, H., Gonzalo-Orden, H., Findley, D. J., & Rojí, E., (2021), “Modeling the international roughness index performance on semi-rigid pavements in single carriageway roads”, Construction and Building Materials, 272, 121665.
-Piryonesi, S. M., & El-Diraby, T. E., (2021), “Examining the relationship between two road performance indicators: Pavement condition index and international roughness index”, Transportation Geotechnics, 26, 100441.
-Recknagel, F., French, M., Harkonen, P., & Yabunaka, K. I., (1997), “Artificial neural network approach for modelling and prediction of algal blooms”, Ecological Modelling, 96(1-3), pp.11-28.
-Shahin, M., (2005), “Pavement Preservation for Airports, Roads, and Parking Lots”, Springer, New York, NY, United States.
-Sirhan, M., Bekhor, S., & Sidess, A., (2022), “Implementation of Deep Neural Networks for Pavement Condition Index Prediction”, Journal of Transportation Engineering, Part B: Pavements, 148(1), 04021070.
-Sollazzo, G., Fwa, T., & Bosurgi, G., (2017), “An ANN model to correlate roughness and structural performance in asphalt pavements”, Construction and Building Materials, 134, pp.684-693.
-Zarei, B., & Shafabakhsh, G. A., (2018), “Dynamic analysis of composite pavement using finite element method and prediction of fatigue life”, Computational Research Progress in Applied Science & Engineering (CRPASE), 4.