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Articles

Coupled SelfSim and genetic programming for non-linear material constitutive modelling

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Pages 1101-1119 | Received 04 Nov 2013, Accepted 26 Aug 2014, Published online: 09 Oct 2014

References

  • Gandomi AH, Alavi AH, Sahab MG, Arjmandi P. Formulation of elastic modulus of concrete using linear genetic programming. J. Mech. Sci. Technol. 2010;24:1273–1278.
  • Xia Z, Ellyin F, Meijer G. Mechanical behavior of Al2O3-particle-reinforced 6061 aluminum alloy under uniaxial and multiaxial cyclic loading. Compos. Sci. Technol. 1997;57:237–248.
  • Choi Y, Han CS, Lee JK, Wagoner RH. modeling multi-axial deformation of planar anisotropic elasto-plastic materials, part 1: theory. Int. J. Plast. 2006;22:1745–1764.
  • Alavi AH, Gandomi AH. Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput. Struct. 2011;89:2176–2194.
  • Pernot S, Lamarque CH. Application of neural networks to the modelling of some constitutive laws. Neural Networks. 1999;12:371–392.
  • Han YF, Zeng WD, Zhao YQ, Zhang XM, Sun Y, Ma XO. Modeling of constitutive relationship of Ti-25 V-15Cr-0.2Si alloy during hot deformation process by fuzzy-neural network. Mate. Des. 2010;31:4380–4385.
  • Ghaboussi J, Pecknold DA, Zhang MF, Haj-Ali RM. Autoprogressive training of neural network constitutive models. Int. J. Numer. Methods Eng. 1998;42:105–126.
  • Pettersson F, Chakraborti N, Saxen H. A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Appl. Soft Comput. 2007;7:387–397.
  • Mondal DN, Sarangi K, Pettersson F, Sen PK, Saxen H, Chakraborti N. Cu-Zn separation by supported liquid membrane analyzed through multi-objective genetic algorithms. Hydrometallurgy. 2011;107:112–123.
  • Yun G, Saleeb A, Shang S, Binienda W, and Menzemer C. Improved selfsim for inverse extraction of nonuniform, nonlinear, and inelastic material behavior under cyclic loadings. J. Aerosp. Eng. 2012;25:256–272.
  • Yun GJ, Ghaboussi J, Elnashai AS. Self-learning simulation method for inverse non-linear modeling of cyclic behavior of connections. Comput. Methods Appl. Mech. Eng. 2008;197:2836–2857.
  • Sidarta D, Ghaboussi J. Constitutive modeling of geomaterials from non-uniform material tests. Comput. Geotech. 1998;22:53–71.
  • Shin HS, Pande GN. On self-learning finite element codes based on monitored response of structures. Comput. Geotech. 2000;27:161–178.
  • Ghaboussi J, Sidarta D. A new nested adaptive neural network for modeling of constitutive behavior of materials. Int. J. Comput. Geotech. 1998;22:29–51.
  • Tsai CC. Seismic site response and interpretation of dynamic soil behavior from down hole array measurements [Ph.D. thesis]. Urbana: University of Illinois Urbana-Champaign; 2007.
  • Shin HS, Pande GN. On self-learning finite element codes based on monitored response of structures. Comput. Geotech. 2000;27:161–178.
  • Banzhaf W, Nordin P, Keller R, Francone F. Genetic programming – an introduction: on the automatic evolution of computer programs and its application. San Francisco (CA): Morgan Kaufmann; 1998.
  • Koza JR. Genetic programming: on the programming of computers by means of natural selection. Cambridge (MA): MIT Press; 1992.
  • Gandomi AH, Alavi AH. Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf. Sci. 2011;181:5227–5239.
  • Gandomi AH, Yun GJ, Alavi AH. An evolutionary approach for modeling of shear strength of RC deep beams. Mater. Struct. 2013;46:2109–2119.
  • Gandomi AH, Alavi AH, Yun GJ. Nonlinear modeling of shear strength of SFRC beams using linear genetic programming. Struct. Eng. Mech. 2011;38:1–25.
  • Azamathulla HM, Guven A, Demir YK. Linear genetic programming to scour below submerged pipeline. Ocean Eng. 2011;38:995–1000.
  • Oltean M, Grossan C. A comparison of several linear genetic programming techniques. Complex Syst. 2003;14:1–29.
  • Gandomi AH, Alavi AH. Applications of Computational Intelligence in Behavior Simulation of Concrete Materials. In: Yang XS, Koziel S, editors. Computational optimization & applications. Berlin: Springer-Verlag; 2011. p. 221–243.
  • Jung S, Ghaboussi J. Characterizing rate-dependent material behaviors in self-learning simulation. Comput. Methods Appl. Mech. Eng. 2006;196:608–618.
  • Jung S, Ghaboussi J, Marulanda C. Field calibration of time-dependent behavior in segmental bridges using self-learning simulation. Eng. Struct. 2007;29:2692–2700.
  • Yun GJ, Ghaboussi J, Elnashai AS. Development of neural network based hysteretic models for steel beam-column connections through self-learning simulation. J. Earthquake Eng. 2007;11:453–467.
  • Yun GJ, Ghaboussi J, Elnashai AS. A new neural network-based model for hysteretic behavior of materials. Int. J. Numer. Methods Eng. 2008;73:447–469.
  • Yun GJ, Saleeb AF, Shang S, Binienda W, Menzemer C. Improved SelfSim for inverse extraction of non-uniform, nonlinear and inelastic constitutive behavior under cyclic loadings. J. Aerosp. Eng. 2012;25:256–272.
  • Koza J. Hierarchical genetic algorithms operating on populations of computer programs. In: Sridharan N, Kaufmann M, editors. In Proceedings of the 11th Internatinal Joint Conference on Artificial Intelligence. San Mateo (CA); 1989. p. 768–774.
  • Brameier M, Banzhaf W. Linear genetic programming (genetic and evolutionary computation). New York (NY): Springer; 2007.
  • Gandomi AH, Alavi AH, Hosseini SS. A discussion on ‘Genetic programming for retrieving missing information in wave records along the west coast of India’. Appl. Ocean Res. 2008;30:338–339. 2007;29:99–111.
  • Brameier M, Banzhaf W. A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 2001;5:17–26.
  • Francone FD, Deschaine LM. Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming. Inf. Sci. 2004;161:99–120.
  • Alavi AH, Gandomi AH, Mollahasani A, Bolouri Bazaz J. Linear and tree-based genetic programming for solving geotechnical engineering problems. In: Yang XS, Gandomi AH, Alavi AH, Talatahari S, editors. Metaheuristics in water, geotechnical and transportation engineering. London: Elsevier; 2013. p. 289–310.
  • Feng X, Yang C. Coupling recognition of the structure and parameters of non-linear constitutive material models using hybrid evolutionary algorithms. Int. J. Numer. Methods Eng. 2004;59:1227–1250.
  • Javadi AA, Rezania M. Intelligent finite element method: an evolutionary approach to constitutive modeling. Adv. Eng. Inf. 2009;23:442–451.
  • Reddy K. Experiment 12: unconfined compression (UC) test. Chicago (IL): University of Illinois at Chicago; 2002.
  • Sokolis DP, Kefaloyannis EM, Kouloukoussa M, Marinos E, Boudoulas H, Karayannacos PE. A structural basis for the aortic stress–strain relation in uniaxial tension. J. Biomech. 2006;39:1651–1662.
  • Gandomi AH, Alavi AH, Sahab MG. New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming. Mater. Struct. 2010;43:963–983.
  • Alavi AH, Gandomi AH. A robust data mining approach for formulation of geotechnical engineering systems. Eng. Comput. 2011;28:242–274.
  • Alavi AH, Gandomi AH. Energy-based numerical models for assessment of soil liquefaction. Geosci. Front. 2012;3:541–555.
  • Francone FD. Discipulus ProTM software owner’s manual. Littleton (CO): Register Machine Learning Technologies Inc.; 2001.
  • Conrads M, Dolezal O, Francone FD, Nordin P. Discipulus–fast genetic programming based on AIM learning technology. Littleton: Register Machine Learning Technologies Inc.; 2004.
  • Giri BK, Hakanen J, Miettinen K, Chakraborti N. Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives. Appl. Soft Comput. 2013;13:2613–2623.
  • Giri BK, Pettersson F, Saxen H, Chakraborti N. Genetic Programming Evolved through Bi-Objective Genetic Algorithms Applied to a Blast Furnace. Mater. Manuf. Processes. 2013;28:776–782.
  • Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM. A hybrid computational approach to derive new ground-motion prediction equations. Eng. Appl. Artif. Intell. 2011;24:717–732.
  • Ghaboussi J, Garrett J, Wu X. Knowledge‐based modeling of material behavior with neural networks. J. Eng. Mech. ASCE. 1991;117:132–153.
  • Haj-Ali R, Kim HK. Nonlinear constitutive models for FRP composites using artificial neural networks. Mech. Mater. 2007;39:1035–1042.
  • Shen Y, Chandrashekhara K, Breig WF, Oliver LR. Neural network based constitutive model for rubber material. Rubber Chem. Technol. 2004;77:257–277.
  • Sun Y, Zeng WD, Zhao YQ, Qi YL, Ma X, Han YF. Development of constitutive relationship model of Ti600 alloy using artificial neural network. Comput. Mater. Sci. 2010;48:686–691.

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