TY - GEN
T1 - Estimating mechanics parameters of rock mass based on improved genetic algorithm
AU - Ling, Xianzhang
AU - Zhang, Feng
AU - Zhu, Zhanyuan
AU - Tang, Liang
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Adaptive algorithms
KW - Estimating parameters
KW - Niche algorithms
KW - Optimization displacement back analysis
KW - The improved genetic algorithms
UR - https://www.scopus.com/pages/publications/52349095489
U2 - 10.1109/CCDC.2008.4598203
DO - 10.1109/CCDC.2008.4598203
M3 - 会议稿件
AN - SCOPUS:52349095489
SN - 9781424417346
T3 - Chinese Control and Decision Conference, 2008, CCDC 2008
SP - 4608
EP - 4612
BT - Chinese Control and Decision Conference, 2008, CCDC 2008
T2 - Chinese Control and Decision Conference 2008, CCDC 2008
Y2 - 2 July 2008 through 4 July 2008
ER -