Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data. (English) Zbl 1168.74357
Summary: This study concerns the identification of parameters of soil constitutive models from geotechnical measurements by inverse analysis. To deal with the non-uniqueness of the solution, the inverse analysis is based on a genetic algorithm (GA) optimization process. For a given uncertainty on the measurements, the GA identifies a set of solutions. A statistical method based on a principal component analysis (PCA) is, then, proposed to evaluate the representativeness of this set. It is shown that this representativeness is controlled by the GA population size for which an optimal value can be defined. The PCA also gives a first-order approximation of the solution set of the inverse problem as an ellipsoid. These developments are first made on a synthetic excavation problem and on a pressuremeter test. Some experimental applications are, then, studied in a companion paper, to show the reliability of the method.
MSC:
| 74G75 | Inverse problems in equilibrium solid mechanics |
| 74L10 | Soil and rock mechanics |
| 74S05 | Finite element methods applied to problems in solid mechanics |
Keywords:
soil parameters identification; inverse analysis; optimization; genetic algorithm; principal component analysis; finite element method; geotechnics; synthetic dataReferences:
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