Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data
Résumé
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. Copyright © 2009 John Wiley & Sons, Ltd.
Mots clés
Experimental application
First-order approximations
Genetic-algorithm optimizations
Geotechnical measurement
Geotechnics
Nonuniqueness
Optimal values
Population sizes
Pressuremeter tests
Soil constitutive model
Soil parameters
Solution set
Synthetic data
Genetic algorithms
Geologic models
Identification (control systems)
Inverse problems
Population statistics
Soils
constitutive equation
experimental study
finite element method
genetic algorithm
identification method
inverse analysis
optimization
parameterization
pressuremeter
principal component analysis
reliability analysis
soil analysis
uncertainty analysis