Reference : Statistical inverse analysis based on genetic algorithm and principal component analy...
Scientific journals : Article
Engineering, computing & technology : Civil engineering
Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests
Levasseur, Séverine [Université de Liège - ULiège > Département Argenco : Secteur GEO3 > Géomécanique et géologie de l'ingénieur >]
Malécot, Yann mailto [Université Joseph Fourier - Grenoble 1 - UJF > > Laboratoire 3S-R > >]
Boulon, Marc mailto [Université Joseph Fourier - Grenoble 1 - UJF > > Laboratoire 3S-R > >]
Flavigny, Etienne mailto [Université Joseph Fourier - Grenoble 1 - UJF > > Laboratoire 3S-R > >]
International Journal for Numerical and Analytical Methods in Geomechanics
John Wiley & Sons
New York
[en] Soil parameter identification ; inverse analysis ; genetic algorithm ; principal component analysis ; pressuremeter test ; excavation
[en] This study concerns the identification of constitutive models from geotechnical measurements by
inverse analysis. Soil parameters are identified from measured horizontal displacements of sheet pile walls and from a measured pressuremeter curve. An optimization method based on a genetic algorithm and a principal component analysis, developed and tested on synthetic data in a previous paper, is applied. These applications show that the conclusions deduced from synthetic problems can be extrapolated to real problems. The genetic algorithm is a robust optimization method which is able to deal with the non-uniqueness of the solution in identifying a set of solutions for a given uncertainty on the measurements. This set is then characterized by a principal component analysis (PCA) which gives a fi rst order approximation of the solution as an ellipsoid. When the solution set is not too curved in the research space, this ellipsoid characterizes the soil properties considering the measured data and the tolerate margins for the response of the numerical model. Besides, optimizations from di fferent measurements provide solution sets with a common area in the research space. This intersection gives a more relevant and accurate identification of parameters. Finally, we show that these identified parameters permit to reproduce geotechnical measurements not used in the identification process.

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