[1] T.F. Fwa, The handbook of highway engineering, CRC press, 2005.
[2] K.H. McGhee, Automated pavement distress collection techniques, Transportation Research Board, 2004.
[3] K.A. Abaza, Deterministic performance prediction model for rehabilitation and management of flexible pavement, International Journal of Pavement Engineering, 5(2) (2004) 111-121.
[4] J.A. Prozzi, F. Hong, Transportation infrastructure performance modeling through seemingly unrelated regression systems, Journal of Infrastructure Systems, 14(2) (2008) 129-137.
[5] K.A. Zimmerman, D.M. Testa, An Evaluation of Idaho Transportation Department Needs for Maintenance Management and Pavement Management Software Tools, (2008.(
[6] A. Miege, Tyre model for truck ride simulations, CPGS Dissertation, University of Cambridge, (2004).
[7] A.R. Archilla, Development of rutting progression models by combining data from multiple sources, University of California, Berkeley, 2000.
[8] B. Ali, Numerical Model for the Mechanical Behavior of Pavement: Application to the Analysis of Rutting, PhD, University of Science and Technology Lille, France, (2006.(
[9] M. Anyala, J. Odoki, C. Baker, Hierarchical asphalt pavement deterioration model for climate impact studies, International Journal of Pavement Engineering, 15(3) (2014) 251-266.
[10] J.D. Porras-Alvarado, Z. Zhang, L.G.L. Salazar, Probabilistic approach to modeling pavement performance using IRI data, 2014.
[11] I. ARA, ERES Consultants Division, Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, Final Rep., NCHRP Project 1-37A, (2004).
[12] A. Isa, D. Ma’Soem, L.T. Hwa, Pavement performance model for federal roads, in: Proceedings of the Eastern Asia Society for Transportation Studies, Citeseer, 2005, pp. 428-440.
[13] A.K. Naiel, Flexible pavement rut depth modeling for different climate zones, (2010).
[14] M. Svensson, Modeling pavement performance based on data from the Swedish LTPP database: predicting cracking and rutting, KTH Royal Institute of Technology, 2013.
[15] M. RADWAN, A.-H. Mostafa, M. HASHEM, H. FAHEEM, Modeling pavement performance based on LTPP database for flexible pavements, Teknik Dergi, 31(4) (2020) 10127-10146.
[16] A. Fathi, M. Mazari, M. Saghafi, A. Hosseini, S. Kumar, Parametric study of pavement deterioration using machine learning algorithms, in: Airfield and highway pavements 2019: Innovation and sustainability in highway and airfield pavement technology, American Society of Civil Engineers Reston, VA, 2019, pp. 31-41.
[17] S. Inkoom, J. Sobanjo, A. Barbu, X. Niu, Prediction of the crack condition of highway pavements using machine learning models, Structure and Infrastructure Engineering, 15(7) (2019) 940-953.
[18] W. Zeiada, S.A. Dabous, K. Hamad, R. Al-Ruzouq, M.A. Khalil, Machine learning for pavement performance modelling in warm climate regions, Arabian Journal for Science and Engineering, (2020) 1-19.
[19] X. Cai, P. Wang, L. Du, Z. Cui, W. Zhang, J. Chen, Multi-objective three-dimensional DV-hop localization algorithm with NSGA-II, IEEE Sensors Journal, 19(21) (2019) 10003-10015.
[20] P. Lu, D. Tolliver, Pavement treatment short-term effectiveness in IRI change using long-term pavement program data, Journal of transportation engineering, 138(11) (2012) 1297-1302.
[21] FHWA, LONG-TERM PAVEMENT PERFORMANCE Information Management System Pavement Performance Database User Reference Guide, 088 (2003).
[22] G.E. Elkins, P.N. Schmalzer, T. Thompson, A. Simpson, Long-term pavement performance information management system: Pavement performance database user reference guide, Turner-Fairbank Highway Research Center, 2003.
[23] https://infopave.fhwa.dot.gov, in.
[24] H.J. Adèr, G.J. Mellenbergh, Advising on research methods.: Proceedings of the 2007 KNAW colloquium, Johannes van Kessel Publ., 2008.
[25] R.J. Little, D.B. Rubin, Statistical analysis with missing data, John Wiley & Sons, 2019.
[26] F. Yu, X. Xu, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network, Applied Energy, 134 (2014) 102-113.
[27] V. Safak, Min-mid-max scaling, limits of agreement, and agreement score, arXiv preprint arXiv:2006.12904, (2020).
[28] G. Shafabakhsh, O.J. Ani, M. Talebsafa, Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates, Construction and Building Materials, 85 (2015) 136-143.
[29] A. Moniri, H. Ziari, A. Amini, M. Hajiloo, Investigating the ANN model for cracking of HMA in terms of temperature, RAP and fibre content, International Journal of Pavement Engineering, (2020) 1-13.
[30] J. Domitrović, H. Dragovan, T. Rukavina, S. Dimter, Application of an artificial neural network in pavement management system, Tehnički vjesnik, 25(Supplement 2) (2018) 466-473.
[31] E. Heidari, M.A. Sobati, S. Movahedirad, Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN), Chemometrics and intelligent laboratory systems, 155 (2016) 73-85.
[32] M. Jalal, Z. Grasley, C. Gurganus, J.W. Bullard, A new nonlinear formulation-based prediction approach using artificial neural network (ANN) model for rubberized cement composite, Engineering with Computers, (2020) 1-18.
[33] A. Kostopoulos, T. Grapsa, Self-scaled conjugate gradient training algorithms, Neurocomputing, 72(13-15) (2009) 3000-3019.
[34] K. Gopalakrishnan, Effect of training algorithms on neural networks aided pavement diagnosis, International Journal of Engineering, Science and Technology, 2(2) (2010) 83-92.
[35] M.H. Esfe, H. Hajmohammad, R. Moradi, A.A.A. Arani, Multi-objective optimization of cost and thermal performance of double walled carbon nanotubes/water nanofluids by NSGA-II using response surface method, Applied Thermal Engineering, 112 (2017) 1648-1657.
[36] S. Ramesh, S. Kannan, S. Baskar, Application of modified NSGA-II algorithm to multi-objective reactive power planning, Applied Soft Computing, 12(2) (2012) 741-753.
[37] M. Ehsani, Development of a prediction model for concrete pavement failures using the LTPP data, Amirkabir University of Technology, 2021.
[38] Z. He, X. Wen, H. Liu, J. Du, A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region, Journal of Hydrology, 509 (2014) 379-386.
[39] J.-L. Chen, H.-B. Liu, W. Wu, D.-T. Xie, Estimation of monthly solar radiation from measured temperatures using support vector machines–a case study, Renewable Energy, 36(1) (2011) 413-420.
[40] H. Adeli, Neural networks in civil engineering: 1989–2000, Computer‐Aided Civil and Infrastructure Engineering, 16(2) (2001) 126-142.
[41] J.D. Olden, D.A. Jackson, Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks, Ecological modelling, 154(1-2) (2002) 135-150.
[42] M. Gevrey, I. Dimopoulos, S. Lek, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological modelling, 160(3) (2003) 249-264.
[43] J.H. Choi, T.M. Adams, H.U. Bahia, Pavement Roughness Modeling Using Back‐Propagation Neural Networks, Computer‐Aided Civil and Infrastructure Engineering, 19(4) (2004) 295-303.
)