Prognoses in geotechnical engineering require both adequate
material models and identification of the involved material
constants. Scatter of the properties of geomaterials renders
parameter identification a challenging task. Herein, this problem
is treated in an integrated manner. The first part of the book is
devoted to methodical aspects: based on soft computing, an
iterative parameter identification method is developed. Artificial
neural networks are trained to approximate the underlying direct
problem. Subsequently, a genetic algorithm solves the inverse
problem. The method is applied to tunneling according to the New
Austrian Tunneling Method and to ground improvement by means of
jet-grouting. The second part of the book deals with conceptual
aspects of parameter identification. To this end, the problem of
rockfall protection of a gravel-buried steel pipeline is
considered. It is highlighted that identification of material
parameters and verification of a structural model must be based on
two independent sets of experiments. The book will appeal to the
scientific community as well as to practical engineers faced with
the task of parameter identification.
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