Developing a Hybrid ANN-Jaya Procedure for Backcalculation of Flexible Pavements Moduli

Document Type : Research Papers

Authors

1 Associate Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.

2 Research Assistant, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.

3 Assistant Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.

Abstract

This research aim is to develop a procedure for backcalculation of flexible pavements moduli based on the hybridization of the Artificial Neural Network (ANN) and the Jaya optimization algorithm. The ANN was applied to predict the pavement deflection basin, and the Jaya was employed for moduli backcalculation. The comparison of hybrid ANN-Jaya procedure with some backcalculation software indicates the high ability of the developed method to perform backcalculation of flexible pavements moduli. The comparison of the computational speed and accuracy of hybrid ANN-Jaya with ANN-PSO and ANN-GA indicates the superior performance of ANN-Jaya compared to other methods.

Keywords


Alexander, D.R., Kohn, S.D. and Grogan, W.P. (1989). “Nondestructive testing techniques and evaluation procedures for airfield pavements”, In Nondestructive Testing of Pavements and Backcalculation of Moduli, ASTM International, West Conshohocken, 502-524.
Aubdulnibe, F.F. (2019). “An application of Artificial Neural Networks (ANNs) to the backcalculation of flexible pavement moduli”, Journal of Physics: Conference Series, 1362(1), 012146.
Bendana, L., Yang, W. and Lu, J. (1994). “Interpreting data from the falling weight deflectometer”, Engineering Research and Development Bureau, New York State Department of Transportation, Research Report, 160.
Brill, D.R. and Hughes, W.J. (2007). “New FAA pavement design software”, International Airport Review, 11(2), 17-20.
Bush, A.J. and Baladi, G.Y. (1989). Nondestructive testing of pavements and backcalculation of moduli, ASTM International, West Conshohocke.
Ceylan, H., Guclu, A., Tutumluer, E. and Thompson, M. R. (2005). “Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior”, International Journal of Pavement Engineering, 6(3), 171-182.
Eberhart, R. and Kennedy, J. (1995). “A new optimizer using particle swarm theory”, Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya.
Fu, G., Xue, C., Zhao, Y., Cao, D. and Alae, M. (2020). “Accuracy evaluation of statically backcalculated layer properties of asphalt pavements from falling weight deflectometer data”, Canadian Journal of Civil Engineering, 47(3), 317-325.
Ghanizadeh, A.R. and Ziaie, A. (2015). “NonPAS: A program for nonlinear analysis of flexible pavements”, International Journal of Integrated Engineering, 7(1), 21-28.
Ghanizadeh, A.R., Heidarabadizadeh, N. and Mahmoodabadi, M.J. (2020). “Effect of objective function on the optimization of highway vertical alignment by means of metaheuristic algorithms”, Civil Engineering Infrastructures Journal, 53(1), 115-136.
Ghanizadeh, A.R. and Heidarabadizadeh, N. (2018). “Optimization of vertical alignment of highways in terms of earthwork cost using colliding bodies optimization algorithm”, International Journal of Optimization in Civil Engineering, 8, 657-674.
Gopalakrishnan, K. (2010). “Neural network-swarm intelligence hybrid nonlinear optimization algorithm for pavement moduli back-calculation”, Journal of Transportation Engineering, 136(6), 528-536.
Gopalakrishnan, K. (2012). “Instantaneous pavement condition evaluation using non-destructive neuro-evolutionary approach”, Structure and Infrastructure Engineering, 8(9), 857-872.
Gopalakrishnan, K. and Papadopoulos, H. (2011). “Reliable pavement backcalculation with confidence estimation”, Scientia Iranica, 18(6), 1214-1221.
Gopalakrishnan, K. and Thompson, M.R. (2004). “Backcalculation of airport flexible pavement non-linear moduli using Artificial Neural Networks”, Proceedings of the FLAIRS Conference, Florida.
Gurney, K. (2005). An introduction to neural networks, CRC Press, London.
Guzzarlapudi, S.D., Kumar Adigopula, V. and Kumar, R. (2017). “Comparative study of flexible pavement layers moduli backcalculation using approximate and static approach”, Materials Today: Proceedings, 4(9), 9812-9816.
Hajiazizi, M., Taban, M.H. and Ghobadian, R. (2021). “Prediction of Q-value by multi-variable regression and novel Genetic Algorithm based on the most influential parameters”, Civil Engineering Infrastructures Journal, 54(2), 267-280.
Hassan, H. and Mousa, R. (2003). “Evaluation of nondestructive testing data using AASHTO and WESDEF backcalculation approaches”, Journal of Engineering and Applied Science, 50(1), 75-93.
Holland, J. (1975). Adaptation in natural and artificial systems, Michigan Press, Ann Arbor.
Huang, Y.H. (2004). Pavement analysis and design, Pearson Education, New Jersey.
Irwin, L. (1983). User’s guide to Modcomp2, Version 3.2, Local Roads Program, Cornell University, Ithaca, NY.
Kaveh, A. and Dadras, A. (2017). “A guided tabu search for profile optimization of Finite Element models”, International Journal of Optimization in Civil Engineering, 7(4), 527-537.
Li, M. and Wang, H. (2019). “Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters”, International Journal of Pavement Engineering, 20(4), 490-498.
Li, Y., Ma, D., Zhu, M., Zeng, Z. and Wang, Y. (2018). “Identification of significant factors in fatal-injury highway crashes using Genetic Algorithm and Neural Network”, Accident Analysis and Prevention, 111, 354-363.
Maher, A. and Bennert, T.A. (2008). “Evaluation of Poisson’s ratio for use in the mechanistic empirical pavement design guide (MEPDG)”, Transportation Resaerch Board, No. FHWA-NJ-2008-004.
Öcal, A. (2014). “Backcalculation of pavement layer properties using artificial neural network based gravitational search algorithm”, Ph.D. Thesis, Middle East Technical University, Ankara, Turkey.
Pekcan, O., Tutumluer, E. and Thompson, M. (2008). “Artificial Neural Network based backcalculation of conventional flexible pavements on lime stabilized soils”, Proceedings of the 12th International Conference of Iinternational Association for Computer Methods And Advances in Geomechanics (IACMAG), Goa, India.
Rakesh, N., Jain, A., Reddy, M.A. and Reddy, K.S. (2006). “Artificial Neural Networks-Genetic Algorithm based model for backcalculation of pavement layer moduli”, International Journal of Pavement Engineering, 7(3), 221-230.
Rao, R. (2016). “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems”. International Journal of Industrial Engineering Computations, 7(1), 19-34.
Rao, R.V., Savsani, V.J. and Vakharia, D. (2011). “Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, 43(3), 303-315.
Richardson, D.N. and Lusher, M. (2015). “MoDOT pavement preservation research program volume III, development of pavement family and treatment performance models”, Division of Construction and Materials, Final Report Prepared for Missouri Department of Transportation.
Saltan, M. and Terzi, S. (2008). “Modeling deflection basin using artificial neural networks with cross-validation technique in backcalculating flexible pavement layer moduli”, Advances in Engineering Software, 39(7), 588-592.
Saltan, M., Tigdemir, M. and Karasahin, M. (2002). “Artificial Neural Network application for flexible pavement thickness modeling”, Turkish Journal of Engineering and Environmental Sciences, 26(3), 243-248.
Saltan, M., Uz, V.E. and Aktas, B. (2013). “Artificial Neural Networks-based backcalculation of the structural properties of a typical flexible pavement”, Neural Computing and Applications, 23(6), 1703-1710.
Samadi, D., Taghaddos, H., Nili, M.H. and Noghabaei, M. (2021). “Development of a bridge maintenance system using bridge information modeling”, Civil Engineering Infrastructures Journal, 54(2), 351-364.
Saric, A. and Pozder, M. (2017). “Artificial Neural Networks application in the backcalculation process of flexible pavement layers elasticity modulus”, In: International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, 549-559, Springer, Cham.
Scimemi, G.F., Turetta, T. and Celauro, C. (2016). “Backcalculation of airport pavement moduli and thickness using the Lévy Ant Colony Optimization algorithm”, Construction and Building Materials, 119, 288-295.
Sonmez, M., Akgüngör, A.P. and BektaƟ, S. (2017). “Estimating transportation energy demand in Turkey using the Artificial Bee Colony Algorithm”, Energy, 122, 301-310.
Strategic Highway Research Program (SHRP). (1991). “SHRP layer moduli backcalculation procedure software selection”, SHRP Technical Report, Washington D.C.
Ullidtz, P. (1987). Pavement analysis, developments in Civil Engineering, Elsevier, Netherlands.
Van Cauwelaert, F.J., Alexander, D.R., White, T.D. and Barker, W.R. (1989). “Multilayer elastic program for backcalculating layer moduli in pavement evaluation”, 1st International Symposium on Nondestructive Testing of Pavements and Backcalculation of Moduli, Baltimore, Maryland, USA.
Vasant, P., Zelinka, I. and Weber, G. (2019). Intelligent computing and optimization, Springer, Turkey.
Von Quintus, H.L. and Simpson, A.L. (2002). Back-calculation of layer parameters for LTPP test sections, Volume II: Layered elastic analysis for flexible and rigid pavements, FHWA-RD-01-113, United States.
Wang, H., Xie, P., Ji, R. and Gagnon, J. (2020). “Prediction of airfield pavement responses from surface deflections: Comparison between the traditional backcalculation approach and the ANN model”, Road Materials and Pavement Design, 22(9), 1930-1945.
William, G.W. (1999). “Backcalculation of pavement layers moduli using 3D nonlinear explicit finite element analysis”, Ph.D. Thesis, West Virginia University Libraries.
Yang, X.-S. (2009). Firefly algorithms for multimodal optimization, Springer, Cambridge.
Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms, Luniver press, UK.
You, L., Yan, K. and Liu, N. (2020). “Assessing Artificial Neural Network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement”, Frontiers of Structural and Civil Engineering, 14(2), 487-500.
Zhang, X., Otto, F. and Oeser, M. (2021). “Pavement moduli back-calculation using Artificial Neural Network and Genetic Algorithms”, Construction and Building Materials, 287, 123026.
Volume 55, Issue 1
June 2022
Pages 89-108
  • Receive Date: 05 October 2020
  • Revise Date: 19 July 2021
  • Accept Date: 19 July 2021
  • First Publish Date: 01 June 2022