Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Xia-Ting Feng and Bing-Rui Chen and 
                 Chengxiang Yang and Hui Zhou and Xiuli Ding",
  title =        "Identification of visco-elastic models for rocks using
                 genetic programming coupled with the modified particle
                 swarm optimization algorithm",
  journal =      "International Journal of Rock Mechanics and Mining
  year =         "2006",
  volume =       "43",
  number =       "5",
  pages =        "789--801",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Visco-elastic
                 models, Rock, Evolutionary algorithm, Particle swarm
  DOI =          "doi:10.1016/j.ijrmms.2005.12.010",
  abstract =     "The response of rocks to stress can be highly
                 non-linear, so sometimes it is difficult to establish a
                 suitable constitutive model using traditional mechanics
                 methods. It is appropriate, therefore, to consider
                 modelling methods developed in other fields in order to
                 provide adequate models for rock behaviour, and this
                 particularly applies to the time-dependent behavior of
                 rock. Accordingly, a new system identification method,
                 based on a hybrid genetic programming with the improved
                 particle swarm optimization (PSO) algorithm, for the
                 simultaneous establishment of a visco-elastic rock
                 material model structure and the related parameters is
                 proposed. The method searches for the optimal model,
                 not among several known models as in previous methods
                 proposed in the literatures, but in the whole model
                 space made up of elastic and viscous elementary
                 components. Genetic programming is used for exploring
                 the model's structure and the modified PSO is used to
                 identify parameters (coefficients) in the provisional
                 model. The evolution of the provisional models
                 (individuals) is driven by the fitness based on the
                 residual sum of squares of the behaviour predicted by
                 the model and the actual behaviour of the rock given by
                 a set of mechanical experiments. Using this proposed
                 algorithm, visco-elastic models for the celadon
                 argillaceous rock and fuchsia argillaceous rock in the
                 Goupitan hydroelectric power station, China, are
                 identified. The results show that the algorithm is
                 feasible for rock mechanics use and has a useful
                 ability in finding potential models. The algorithm
                 enables the identification of models and parameters
                 simultaneously and provides a new method for studying
                 the mechanical characteristics of visco-elastic
  notes =        "a Institute of Rock and Soil Mechanics, Chinese
                 Academy of Sciences, Wuhan 430071, China

                 b School of Resources and Civil Engineering,
                 Northeastern University, Shenyang 110004, China

                 c Yangtze River Scientific Research Institute, Wuhan
                 430010, China",

Genetic Programming entries for Xia-Ting Feng Bing-Rui Chen Chengxiang Yang Hui Zhou Xiuli Ding