SMT-GP method of prediction for ground subsidence due to tunneling in mountainous areas

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@Article{Li:2012:TUST,
  author =       "Wen-Xiu Li and Ji-Fei Li and Qi Wang and Yin Xia and 
                 Zhan-Hua Ji",
  title =        "SMT-GP method of prediction for ground subsidence due
                 to tunneling in mountainous areas",
  journal =      "Tunnelling and Underground Space Technology",
  volume =       "32",
  month =        nov,
  pages =        "198--211",
  year =         "2012",
  keywords =     "genetic algorithms, genetic programming, Tunneling,
                 Ground subsidence, Engineering parameters",
  ISSN =         "0886-7798",
  DOI =          "doi:10.1016/j.tust.2012.06.012",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0886779812001228",
  size =         "14 pages",
  abstract =     "This paper introduces a new analysis method -
                 stochastic medium technique (SMT) combined with genetic
                 programming (GP) in the prediction of ground subsidence
                 due to tunnelling in mountainous areas. The methodology
                 involves the use of stochastic medium theory to
                 generate theory models and to predict ground subsidence
                 due to tunnelling in mountainous areas. The parameters
                 in the theory models which are optimised by genetic
                 programming. The use of the integrated methodology is
                 demonstrated via a case study in the prediction of
                 ground subsidence due to tunnelling in mountainous
                 areas in Hebei, North China. The results show that the
                 integrated stochastic medium technique - genetic
                 programming (SMT-GP) gives the smallest error on the
                 ground subsidence data when compared to traditional
                 finite element method. The SMT-GP method is expected to
                 provide a significant improvement when the ground
                 subsidence data come from mountainous areas. The
                 agreement of the theoretical results with the field
                 measurements shows that the SMT-GP is satisfactory and
                 the models and SMT-GP method proposed are valid and
                 thus can be effectively used for predicting the ground
                 surface subsidence due to tunneling engineering in
                 mountainous areas and urban areas.",
}

Genetic Programming entries for Wen-Xiu Li Ji-Fei Li Qi Wang Yin Xia Zhan-Hua Ji

Citations