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.
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 param- eters. 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.
出处
《数学的实践与认识》
CSCD
北大核心
2013年第9期115-120,共6页
Mathematics in Practice and Theory
关键词
参数反演
径向基概率神经网络
遗传算法
inverse calculation
radial basis pro babilistic neural network
genetic algorithm
