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