Abstract

A geotechnical parameter identification method based on multi-output support vector machines (M-SVMs) and the clonal selection algorithm (CSA) is proposed. Using this method, the rockfill material parameters of a concrete-faced rockfill dam (CFRD) are identified. Based on the Taguchi design, some possible combinations of material parameters are generated within the admissible ranges of material parameters. Then, using these combinations, the displacement of all the observation points of the CFRD is calculated using the finite element method (FEM). Next, different combinations of material parameters are used as input, and the calculated displacement is used as output to train some M-SVM models to simulate the complex relationship between the material parameters and the dam displacement. Integrated inversion analysis, which takes the static and creep properties of rockfill material into account simultaneously, is implemented to achieve a comprehensive understanding of the mechanical properties of the rockfill material. The optimization problem corresponding to the integrated inversion analysis is solved by the CSA, which has global convergence and is robust. During the process of searching for optimal material parameters, the dam displacement is calculated by the M-SVM mapping instead of the FEM, which may greatly reduce the computation time. Based on the observed settlement and finite element model of the CFRD, the inversion analysis method described above is implemented. The results indicate that the parameter identification method for rock-fill material proposed in this article is accurate, has a fast convergence rate and can be applied in practical engineering applications.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call