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遗传优化RBPNN的隧道围岩力学参数反演计算
Inverse Calculation of Tunnel Surrounding Rock Mechanics Parameters Based on the Genetic Optimization RBPNN
【摘要】 针对隧道围岩参数取值的不确定性,以现场量测得到的位移信息量为基础,利用结构分析有限元的正演分析法提取实验样本,构造遗传算法全结构优化的径向基概率神经网络(RBPNN)模型,反演围岩力学参数.结果表明,利用遗传优化径向基概率神经网络反演围岩力学参数能够达到工程应用要求,为隧道施工的理论及其工程化方法提供了有效的途径.
【Abstract】 Against uncertainty of tunnel surrounding rock parameters,and based on displacement informations measured on field,by use of the method of the finite element structure analysis to extract the experimental samples,and then constructing radial basis probabilistic neuralnetwork model optimized by the genetic algorithm,to inversion rock mechanical parameters. The results indicate that the method can achieve the requirements of engineering applications, which is inverse calculation of tunnel surrounding rock mechanics parameters based on the genetic optimization RBPNN,and provide an effective way for tunnel construction throry and engineering method.
【Key words】 inverse calculation; radial basis probabilistic neural network; genetic algorithm;
- 【文献出处】 数学的实践与认识 ,Mathematics in Practice and Theory , 编辑部邮箱 ,2013年09期
- 【分类号】TP18;U451.2
- 【被引频次】1
- 【下载频次】85