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

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@Article{Gandomi:2015:IPSE,

• author = "Amir H. Gandomi and Gun Jin Yun",
• title = "Coupled {SelfSim} and genetic programming for non-linear material constitutive modelling",
• journal = "Inverse Problems in Science and Engineering",
• year = "2015",
• volume = "23",
• number = "7",
• pages = "1101--1119",
• keywords = "genetic algorithms, genetic programming, linear genetic programming, Discipulus, inverse analysis, artificial neural network, ANN, non-linear material constitutive model",
• ISSN = "1741-5977",
• URL = "http://www.tandfonline.com/doi/abs/10.1080/17415977.2014.968149",
• DOI = "doi:10.1080/17415977.2014.968149",
• size = "19 pages",
• abstract = "In the present study, an improved SelfSim is combined with a recent genetic programming technique called linear GP (LGP) for the inverse extraction of non-linear material behaviour. The SelfSim prepares a comprehensive database including stresses and strains of the structural elements. Then, a steady-state LGP is used to formulate the strain-stress relationship. In this research, a space truss with a reference material model is used as a hypothetical structure. The derived LGP-based formula is very simple and can be employed for design and pre-design purposes. The implementation of LGP-based model is also tested in a general purpose finite element programme. Since the proposed model is an explicit formula, its implementation becomes standard and practically useful. The results show that the procedure is reliable and can be used to derive and formulate the non-linear constitutive material models with a high degree of accuracy.",

}

Genetic Programming entries for A H Gandomi Gunjin Yun

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