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Hybrid Stochastic Global Optimization Scheme for Rapid Pavement Backcalculation

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Intelligent and Soft Computing in Infrastructure Systems Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 259))

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Abstract

Over the years, several techniques have been proposed for back-calculation of pavement layer moduli which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. In recent years, researchers are actively deriving inspiration from nature, biology, physical systems, and social behavior of natural systems for developing computational techniques to solve complex optimization problems. Some well-known nature-inspired meta-heuristics, which are basically high-level strategies that guide the search process to efficiently explore the search space in order to find (near-) optimal solutions, include, but are not limited to: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Shuffled Complex Evolution (SCE), etc. Potential applications of such nature-inspired hybrid optimization approaches to pavement backcalculation are conceptually illustrated in this chapter which take advantage of the combined efficiency and accuracy achieved by integrating advanced pavement numerical modeling schemes, computational intelligence based surrogate mapping techniques, and stochastic nature-inspired meta-heuristics with global optimization strategies using a system-of-systems approach.

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References

  • Alkasawneh, W.: Backcalculation of pavement moduli using genetic algorithms. Ph.D. dissertation, Dept. Civil Eng., Univ. of Akron, Akron, Ohio (2007)

    Google Scholar 

  • Davis, L.: Genetic Algorithms and Simulated Annealing. Pitman, London (1987)

    MATH  Google Scholar 

  • Duan, Q., Sorooshian, S., Gupta, V.K.: Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28(4), 1015–1031 (1992)

    Article  Google Scholar 

  • Duan, Q.A., Gupta, V.K., Sorooshian, S.: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl. 76(3), 501–521 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  • Duan, Q.A., Sorooshian, S., Gupta, V.K.: Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265–284 (1994)

    Article  Google Scholar 

  • Eberhart, R.C., Shi, Y.: Evolving artificial neural networks. In: Proc. 1998 Intl. Conf. on Neural Networks and Brain, Beijing, P.R.C., pp. 5–13 (1998)

    Google Scholar 

  • Eberhart, R.C., Simpson, P.K., Dobbins, R.W.: Computational Intelligence PC Tools. Academic Press Professional, Boston (1996)

    Google Scholar 

  • Frenzel, J.: Genetic Algorithms, A New Breed of Optimization. IEEE Potentials, 21–24 (October 1993)

    Google Scholar 

  • Fwa, T.F., Tan, C.Y., Chan, W.T.: Backcalculation analysis of pavement-layer moduli using genetic algorithms. Transportation research record 1570, 134–142 (1997)

    Article  Google Scholar 

  • Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  • Goktepe, A.B., Agar, E., Lav, A.H.: Comparison of Multilayer Per-ceptron and Adaptive Neuro-Fuzzy System on Backcalculating the Mechanical Properties of Flexible Pavements, vol. 54(3), pp. 1–13. The Bulletin of the Istanbul Technical University, ARI (2006)

    Google Scholar 

  • Gopalakrishnan, K., Thompson, M.R.: Backcalculation of airport flexible pavement non-linear moduli using artificial neural networks. In: Proc. 17th International FLAIRS Conference, Miami Beach, Florida (2004)

    Google Scholar 

  • Gopalakrishnan, K.: Backcalculation of Non-Linear Pavement Moduli Using Finite-Element Based Neuro-Genetic Hybrid Optimization. The Open Civil Engineering Journal 3, 83–92 (2009)

    Article  Google Scholar 

  • Gomez-Ramirez, F., Thompson, M.R., Bejarano, M.: ILLI-PAVE based flexible pavement design concepts for multiple wheel heavy gear load aircraft. In: Proc. Ninth International Conference on Asphalt Pavements, Copenhagen, Denmark (2002)

    Google Scholar 

  • Haykin, S.: Neural networks: A comprehensive foundation. Prentice-Hall Inc., New Jersey (1999)

    MATH  Google Scholar 

  • Hicks, R.G., Monismith, C.L.: Factors Influencing the Resilient Properties of Granular Materials. Transportation Research Record 345, 15–31 (1971)

    Google Scholar 

  • Hoffman, M.S., Thompson, M.R.: Backcalculating nonlinear resilient moduli from deflection data. Transportation research record 852, 42–51 (1982)

    Google Scholar 

  • Holland, J.H.: Adaption in Natural and Artificial Systems. Univ. Michigan Press, Ann Arbor (1975)

    MATH  Google Scholar 

  • Houck, C., Joines, J., Kay, M.: A genetic algorithm for function optimization: A Matlab implementation. North Carolina State University, Raleigh, North Carolina, Research Rep. NCSU-IE TR 95-09 (1995)

    Google Scholar 

  • Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Int’l. Conf. on Neural Networks, Piscataway, NJ, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  • Muttil, N., Liong, S.Y.: Superior exploration–exploitation balance in shuffled complex evolution. J. Hydr. Engrg. 130(12), 1202–1205 (2004)

    Article  Google Scholar 

  • Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)

    MATH  Google Scholar 

  • Nunoo, C., Mrawira, D.: Shuffled complex evolution algorithms in infrastructure works programming. ASCE Journal of Computing in Civil Engineering 18(3), 257–266 (2004)

    Article  Google Scholar 

  • Park, H.M., Park, S.W., Hwang, J.J.: Use of genetic algorithm and finite element method for backcalculating layer moduli in asphalt pavements. In: Proc. TRB 86th Annual Meeting Compendium of Papers, Washington, DC (2007)

    Google Scholar 

  • Peckcan, O., Tutumluer, E., Thompson, M.R.: Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models. Illinois Center for Transportation, Champaign, IL, Research Rep. FHWA-ICT-08-022 (2008)

    Google Scholar 

  • Raad, L., Figueroa, J.L.: Load response of transportation support systems. Transport. Eng. J. ASCE 106(TE1), 111–128 (1980)

    Google Scholar 

  • Rada, G., Witczak, M.W.: Comprehensive evaluation of laboratory resilient moduli results for granular material. Transportation Research Record No. 810, pp. 23–33 (1981)

    Google Scholar 

  • Reddy, M.A., Reddy, K.S., Pandey, B.B.: Backcalculation of pavement moduli using genetic algorithms. J. Highw. Res. Board. 66, 1–10 (2002)

    Google Scholar 

  • Reeves, W.T.: Particle systems – a technique for modelling a class of fuzzy objects. ACM Transactions on Graphics 2(2), 91–108 (1983)

    Article  Google Scholar 

  • Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propogation. In: Rumelhart, D.E. (ed.) Parallel Distributed Processing, pp. 318–362. MIT Press, Cambridge (1986)

    Google Scholar 

  • Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 24(4), 656–667 (1994)

    Article  Google Scholar 

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Authors and Affiliations

  1. Iowa State University, Ames, IA, 50011, USA

    Kasthurirangan Gopalakrishnan

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  1. Kasthurirangan Gopalakrishnan
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Editors and Affiliations

  1. Dept. of Civil, Constr. & Env. Engg., Iowa State University, 354 Town Engineering Bldg., 50011-3232, Ames, IA, USA

    Kasthurirangan Gopalakrishnan

  2. Dept. of Civil, Constr. & Env. Engg., Iowa State University, 482B Town Engineering Bldg., 50011-3232, Ames, IA, USA

    Halil Ceylan

  3. Dept. of Civil and Environmental Engineering, University of Delaware, 343B DuPont Hall, DE 19716, Newark, USA

    Nii O. Attoh-Okine

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Gopalakrishnan, K. (2009). Hybrid Stochastic Global Optimization Scheme for Rapid Pavement Backcalculation. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-04586-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04585-1

  • Online ISBN: 978-3-642-04586-8

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