The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is...The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.展开更多
Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation n...Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation neural network(BPNN), and the HS-BPNN algorithm is formed and applied for the inversion analysis of the parameters of rock-fill materials. The sensitivity of the parameters in the Duncan and Chang's E-B model is analyzed using the orthogonal test design. The case study shows that the parameters φ0, K, Rf, and Kb are sensitive to the deformation of the rock-fill dam and the inversion analysis for these parameters is performed by the HS-BPNN algorithm. Compared with the traditional BPNN, the HS-BPNN algorithm exhibits the advantages of high convergence precision, fast convergence rate, and strong stability.展开更多
文摘The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.
基金supported by the National Natural Science Foundation of China(Grant Nos.51579086,51479054,51379068&51139001)Jiangsu Natural Science Foundation(Grant No.BK20140039)the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.YS11001)
文摘Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation neural network(BPNN), and the HS-BPNN algorithm is formed and applied for the inversion analysis of the parameters of rock-fill materials. The sensitivity of the parameters in the Duncan and Chang's E-B model is analyzed using the orthogonal test design. The case study shows that the parameters φ0, K, Rf, and Kb are sensitive to the deformation of the rock-fill dam and the inversion analysis for these parameters is performed by the HS-BPNN algorithm. Compared with the traditional BPNN, the HS-BPNN algorithm exhibits the advantages of high convergence precision, fast convergence rate, and strong stability.
