A Self-adapting Algorithm for Identifying Rheology Model and Its Parameters of Rock Mass

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Bing-Rui Chen and Xia-Ting Feng and Cheng-Xiang Yang",
  title =        "A Self-adapting Algorithm for Identifying Rheology
                 Model and Its Parameters of Rock Mass",
  booktitle =    "International Conference on Computational Intelligence
                 and Natural Computing, CINC '09",
  year =         "2009",
  month =        jun,
  volume =       "2",
  pages =        "478--481",
  keywords =     "genetic algorithms, genetic programming, Jinping-2
                 hydropower station, chaos-genetic algorithm, hybrid
                 genetic programming, optimal rheological model,
                 rheology model identification, rock mass parameters,
                 self-adapting system identification method, tentative
                 model, identification, natural resources, rheology",
  DOI =          "doi:10.1109/CINC.2009.39",
  abstract =     "As it is difficult to previously determine rockmass
                 rheology constitutive model using phenomena methods of
                 mechanics, so a new self-adapting system identification
                 method, a hybrid genetic programming (GP) with the
                 chaos-genetic algorithm (CGA) based on self-rheological
                 characteristic of rock mass, is proposed. Genetic
                 programming is used for exploring the model's structure
                 and the chaos-genetic algorithm is produced to identify
                 parameters (coefficients) in the tentative model. The
                 optimal rheological model is determined by mechanical
                 and rheological characteristic, important expertise etc
                 and can describe rheological behavior of identified
                 rock mass perfectly. The assistant tunnel B of
                 Jinping-2 hydropower station is used as an example for
                 verifying the proposed method. The results show that
                 the algorithm is feasible and has great potential in
                 finding new rheological models.",
  notes =        "Also known as \cite{5230917}",

Genetic Programming entries for Bing-Rui Chen Xia-Ting Feng Chengxiang Yang