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Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data
Author(s) -
Levasseur S.,
Malecot Y.,
Boulon M.,
Flavigny E.
Publication year - 2009
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.776
Subject(s) - representativeness heuristic , principal component analysis , inverse , algorithm , population , genetic algorithm , ellipsoid , set (abstract data type) , inverse problem , mathematical optimization , uniqueness , component (thermodynamics) , computer science , data set , synthetic data , reliability (semiconductor) , identification (biology) , data mining , mathematics , statistics , geology , mathematical analysis , demography , geometry , geodesy , quantum mechanics , sociology , programming language , botany , biology , physics , thermodynamics , power (physics)
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.