Hybrid Stochastic Global Optimization Scheme for Rapid Pavement Backcalculation

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.

[1]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[2]  M Thompson,et al.  ILLI-PAVE BASED FLEXIBLE PAVEMENT DESIGN CONCEPTS FOR MULTIPLE WHEEL HEAVY GEAR LOAD AIRCRAFT , 2002 .

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  Kasthurirangan Gopalakrishnan.  Backcalculation of Non-Linear Pavement Moduli Using Finite-Element Based Neuro-Genetic Hybrid Optimization , 2009 .

[5]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[6]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[7]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Gonzalo R. Rada,et al.  COMPREHENSIVE EVALUATION OF LABORATORY RESILIENT MODULI RESULTS FOR GRANULAR MATERIAL , 1981 .

[10]  Nitin Muttil,et al.  Superior exploration-exploitation balance in shuffled complex evolution , 2004 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  R. W. Dobbins,et al.  Computational intelligence PC tools , 1996 .

[13]  Erol Tutumluer,et al.  Nondestructive Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models , 2006 .

[14]  Donath Mrawira,et al.  Shuffled complex evolution algorithms in infrastructure works programming , 2004 .

[15]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[16]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[17]  Lutfi Raad,et al.  LOAD RESPONSE OF TRANSPORTATION SUPPORT SYSTEMS , 1980 .

[18]  C L Monismith,et al.  FACTORS INFLUENCING THE RESILIENT RESPONSE OF GRANULAR MATERIALS , 1971 .

[19]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[20]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[21]  Marshall R Thompson,et al.  BACKCALCULATING NONLINEAR RESILIENT MODULI FROM DEFLECTION DATA , 1982 .

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[24]  Wael Alkasawneh,et al.  Backcalculation of Pavement Moduli Using Genetic Algorithms , 2008 .

[25]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[26]  Hee Mun Park,et al.  Use of Genetic Algorithm and Finite Element Method for Backcalculating Layer Moduli in Asphalt Pavements , 2007 .

[27]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[28]  Weng Tat Chan,et al.  Backcalculation Analysis of Pavement-Layer Moduli Using Genetic Algorithms , 1997 .

[29]  Kasthurirangan Gopalakrishnan,et al.  Backcalculation of Airport Flexible Pavement Non-Linear Moduli Using Artificial Neural Networks , 2004, FLAIRS Conference.