Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements

  • A. Öcal
  • O. PekcanEmail author
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA.


Cuckoo search Optimization Metaheuristics Artificial neural networks Backcalculation Flexible pavements 


  1. 1.
    Fwa TF (2006) The handbook of engineering of highway. CRC PressGoogle Scholar
  2. 2.
    Goktepe AB, Agar E, Lav AH (2006) Advances in backcalculating the mechanical properties of flexible pavements. Adv Eng Softw 37:421–431Google Scholar
  3. 3.
    Sivaneswaran N, Kramer, Steven L, Mahoney JP (1991) Advanced backcalculation using a nonlinear least squares optimization technique. Trans Res Rec 93–102Google Scholar
  4. 4.
    Meier R, Rix G (1993) An initial study of surface wave inversion using artificial neural networks. Geotech Test J 16:425–431Google Scholar
  5. 5.
    Quintus HL, Von Bush AJ, Baladi GY (1993) Nondestructive testing of pavements and backcalculation of moduli: second volume. ASTMGoogle Scholar
  6. 6.
    Bush AJ, Baladi GY (1988) Nondestructive testing of pavements and backcalculation of moduli. ASTMGoogle Scholar
  7. 7.
    Van Deusen D (1996) Selection of flexible backcalculation software for the minnesota road research project. MinnesotaGoogle Scholar
  8. 8.
    Reddy MA, Reddy KS, Pandey BB (2004) Selection of genetic algorithm parameters for backcalculation of pavement moduli. Int J Pavement Eng 5:81–90Google Scholar
  9. 9.
    Kim N, Im S-B (2005) A comparative study on measured versus Predicted pavement responses from falling weight deflectometer (FWD) measurements. KSCE J Civ Eng 9:91–96Google Scholar
  10. 10.
    Sangghaleh A, Pan E, Green R et al (2013) Backcalculation of pavement layer elastic modulus and thickness with measurement errors. Int J Pavement Eng 15:521–531Google Scholar
  11. 11.
    Fileccia Scimemi G, Turetta T, Celauro C (2016) Backcalculation of airport pavement moduli and thickness using the Levy Ant Colony Optimization Algorithm. Constr Build Mater 119:288–295Google Scholar
  12. 12.
    Kim M (2007) Three-dimensional finite element analysis of flexible pavements considering nonlinear pavement foundation behavior. PhD Thesis, University of Illinois at Urbana-ChampaignGoogle Scholar
  13. 13.
    Mulungye RM, Owende PMO, Mellon K (2007) Finite element modelling of flexible pavements on soft soil subgrades. Mater Des 28:739–756Google Scholar
  14. 14.
    Gopalakrishnan K, Agrawal A, Ceylan H et al (2013) Knowledge discovery and data mining in pavement inverse analysis. Transport 28:1–10Google Scholar
  15. 15.
    Karadelis JN (2000) A numerical model for the computation of concrete pavement moduli: a non-destructive testing and assessment method. NDT E Int 33:77–84Google Scholar
  16. 16.
    Picoux B, El Ayadi A, Petit C (2009) Dynamic response of a flexible pavement submitted by impulsive loading. Soil Dyn Earthq Eng 29:845–854Google Scholar
  17. 17.
    Dong Q, Hachiya Y, Takahashi O et al (2002) An efficient backcalculation algorithm of time domain for large-scale pavement structures using Ritz vectors. Finite Elem Anal Des 38:1131–1150zbMATHGoogle Scholar
  18. 18.
    Li M, Wang H (2019) Development of ANN-GA program for backcalculation of pavement moduli under FWD testing with viscoelastic and nonlinear parameters. Int J Pavement Eng 20:490–498Google Scholar
  19. 19.
    Yi J-H, Mun S (2009) Backcalculating pavement structural properties using a Nelder-Mead simplex search. Int J Numer Anal Methods Geomech 33:1389–1406zbMATHGoogle Scholar
  20. 20.
    Zaabar I, Chatti K, Lee H, Lajnef N (2014) Backcalculation of asphalt concrete modulus master curve from field-measured falling weight deflectometer data. Transp Res Rec J Transp Res Board 2457:80–92Google Scholar
  21. 21.
    Varma S, Emin Kutay M (2016) Backcalculation of viscoelastic and nonlinear flexible pavement layer properties from falling weight deflections. Int J Pavement Eng 17:388–402Google Scholar
  22. 22.
    Lav A, Goktepe A, Lav M (2009) Backcalculation of flexible pavements using soft computing. Intell Soft Comput Infrastruct Syst Eng 67–106Google Scholar
  23. 23.
    Meier R, Rix G (1995) Backcalculation of flexible pavement moduli from dynamic deflection basins using artificial neural networks. Transp Res Rec 1473:72–81Google Scholar
  24. 24.
    Meier R, Rix G (1994) Backcalculation of flexible pavement moduli using artificial neural networks. Transp Res Rec 1448:75–82Google Scholar
  25. 25.
    Saltan M, Tigdemir M, Karasahin M (2002) Artificial neural network application for flexible pavement thickness modeling. Turkish J Eng Environ Sci 26:243–248Google Scholar
  26. 26.
    Saltan M, Terzi S, Küçüksille EU (2011) Backcalculation of pavement layer moduli and Poisson’s ratio using data mining. Expert Syst Appl 38:2600–2608Google Scholar
  27. 27.
    Ceylan H, Gopalakrishnan K (2006) Artificial neural network models incorporating unbound material nonlinearity for rapid prediction of critical pavement responses and layer moduli. Int Cent Aggreg Res 14th Annu Symp 1–22Google Scholar
  28. 28.
    Ceylan H, Guclu A, Tutumluer E, Thompson MR (2005) Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. Int J Pavement Eng 6:171–182Google Scholar
  29. 29.
    Pekcan O, Tutumluer E, Thompson M (2008) Artificial neural network based backcalculation of conventional flexible pavements on lime stabilized soils. In: Proceedings of the 12th international conference of international association for computer methods and advances in geomechanics (IACMAG), 1–6 Oct 2008, Goa, India, pp 1647–1654Google Scholar
  30. 30.
    Sharma S, Das A (2008) Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Can J Civ Eng 35:57–66Google Scholar
  31. 31.
    Rakesh N, Jain A, Reddy MA, Reddy KS (2006) Artificial neural networks—genetic algorithm based model for backcalculation of pavement layer moduli. Int J Pavement Eng 7:221–230Google Scholar
  32. 32.
    Gopalakrishnan K (2009) Backcalculation of non-linear pavement moduli using finite-element based neuro-genetic hybrid optimization. Open Civ Eng J 3:83–92Google Scholar
  33. 33.
    Pekcan O (2011) Soft computing based parameter identification in pavements and geomechanical systems. PhD Thesis, University of Illinois at Urbana-Champaign, Urbana (IL)Google Scholar
  34. 34.
    Harichandran R, Mahmood T (1993) Modified Newton algorithm for backcalculation of pavement layer properties. Transp Res Rec J Transp Res Board 1384:15–22Google Scholar
  35. 35.
    Washington State Department of Transportation (2005) Everseries user’s guide pavement analysis computer software and case studiesGoogle Scholar
  36. 36.
    Fwa TF, Tan CY, Chan WT (1997) Backcalculation analysis of pavement-layer moduli using genetic algorithms. Transp Res Rec 1570:134–142Google Scholar
  37. 37.
    Hu K-F, Jiang K-P, Chang D-W (2007) Study of dynamic backcalculation program with genetic algorithms for FWD on pavements. Tamkang J Sci Eng 10:297–305Google Scholar
  38. 38.
    Tsai B, Harvey J, Monismith C (2009) Case studies of asphalt pavement analysis/design with application of the genetic algorithm. In: Gopalakrishnan K, Ceylan H, Nii OA-O (eds) Intelligent and soft computing in infrastructure systems engineering. Springer, Berlin, Heidelberg, pp 205–238Google Scholar
  39. 39.
    Nazzal M, Tatari O (2013) Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus. Int J Pavement Eng 14:364–373Google Scholar
  40. 40.
    Gopalakrishnan K (2009) Backcalculation of pavement moduli using bio-inspired hybrid metaheuristics and cooperative strategies. In: Proceedings of the 2009 mid-continent transportation research symposium, Ames, IAGoogle Scholar
  41. 41.
    Gopalakrishnan K, Khaitan SK (2010) Development of an intelligent pavement analysis toolbox. Proc ICE—Transp 163:211–221Google Scholar
  42. 42.
    Öcal A (2014) Backcalculation of pavement layer properties using artificial neural network based gravitational search algorithm. M.Sc. Thesis, Middle East Technical UniversityGoogle Scholar
  43. 43.
    Yang X-S, Deb S (2009) Cuckoo Search via Levy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214Google Scholar
  44. 44.
    Li Z, Dey N, Ashour AS, Tang Q (2018) Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem. Neural Comput Appl 30:2685–2696Google Scholar
  45. 45.
    Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16Google Scholar
  46. 46.
    Laha D, Gupta JND (2018) An improved cuckoo search algorithm for scheduling jobs on identical parallel machines. Comput Ind Eng 126:348–360Google Scholar
  47. 47.
    Chakraborty S, Dey N, Samanta S et al (2017) Optimization of non-rigid demons registration using cuckoo search algorithm. Cogn Comput 9:817–826Google Scholar
  48. 48.
    Binh HTT, Hanh NT, Van Quan L, Dey N (2018) Improved Cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Comput Appl 30:2305–2317Google Scholar
  49. 49.
    Yang X-S, Deb S (2010) Engineering optimisation by Cuckoo search. Int J Math Model Numer Optim 1:330–343zbMATHGoogle Scholar
  50. 50.
    Kaveh A, Bakhshpoori T, Ashoory M (2012) An efficient optimization procedure based on Cuckoo search algorithm for practical design of. Int J Optim Civ Eng 2:1–14Google Scholar
  51. 51.
    Kaveh A, Bakhshpoori T (2013) Optimum design of steel frames using Cuckoo search algorithm with Lévy flights. Struct Des Tall Spec Build 22:1023–1036Google Scholar
  52. 52.
    Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35Google Scholar
  53. 53.
    Gandomi AH, Talatahari S, Yang X-S, Deb S (2013) Design optimization of truss structures using Cuckoo search algorithm. Struct Des Tall Spec Build 22:1330–1349Google Scholar
  54. 54.
    Thompson MR, Robnett QL (1979) Resilient properties of subgrade soils. J Transp Eng ASCE 105:71–89Google Scholar
  55. 55.
    Ceylan H, Bayrak MB, Gopalakrishnan K (2014) Neural networks applications in pavement engineering: a recent survey. Int J Pavement Res Technol 7:434–444Google Scholar
  56. 56.
    Ghaboussi J (2001) Biologically inspired soft computing methods in structural mechanics and engineering. Struct Eng Mech 11:485–502Google Scholar
  57. 57.
    Payne RB (2005) The Cuckoos. Oxford University PressGoogle Scholar
  58. 58.
    Yang X (2010) Nature-Inspired metaheuristic algorithms, 2nd edn. Luniver PressGoogle Scholar
  59. 59.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company, IncGoogle Scholar
  60. 60.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948Google Scholar
  61. 61.
    Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248zbMATHGoogle Scholar
  62. 62.
    FHWA LTPP InfoPave (2019). Accessed 18 Aug 2019

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Civil Engineering DepartmentÇankaya UniversityAnkaraTurkey
  2. 2.Civil Engineering DepartmentMiddle East Technical UniversityAnkaraTurkey

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