• DocumentCode
    2569391
  • Title

    Estimating mechanics parameters of rock mass based on improved genetic algorithm

  • Author

    Ling, Xianzhang ; Zhang, Feng ; Zhu, Zhanyuan ; Tang, Liang

  • Author_Institution
    Sch. of Civil Eng., Harbin Inst. of Technol., Harbin
  • fYear
    2008
  • fDate
    2-4 July 2008
  • Firstpage
    4608
  • Lastpage
    4612
  • Abstract
    To estimate the mechanics parameters of rock mass, the genetic algorithm (GA) is adopted Considering the weakness of GA on convergence performance, a new improved genetic algorithm (IGA) is developed based on the niche algorithm, adaptive probability of crossover and mutation, and elitism strategy. Optimum result of the Shubert function using the proposed algorithm shows that the global convergence performance of genetic algorithms is greatly improved. Based on these improved methods, the process of estimating mechanics parameters is established through the improved genetic algorithms, the finite element method and the theory of displacement back analysis, to estimate the mechanics parameters of rock mass. Moreover, the optimization displacement back analysis program (ODBA) is worked out. Finally, using this program, the mechanics parameters of rock mass in shisanling pumped storage station are estimated, and the results indicate that estimated parameters are compared well with field test mechanics parameters. Consequently, the new IGA should be popularized to estimate the mechanics parameters in geotechnical engineering.
  • Keywords
    finite element analysis; genetic algorithms; parameter estimation; rocks; Shisanling pumped storage station; Shubert function; adaptive crossover probability; adaptive mutation probability; displacement back analysis; elitism strategy; finite element method; genetic algorithm; geotechnical engineering; mechanics parameter estimation; niche algorithm; optimization displacement back analysis program; rock mass; Displacement control; Genetic algorithms; Parameter estimation; Testing; Adaptive Algorithms; Estimating parameters; Niche Algorithms; Optimization Displacement Back Analysis; the Improved Genetic Algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2008. CCDC 2008. Chinese
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-1733-9
  • Electronic_ISBN
    978-1-4244-1734-6
  • Type

    conf

  • DOI
    10.1109/CCDC.2008.4598203
  • Filename
    4598203