Inverse FEM analysis II: random parameters identification of reinforced concrete frame

Abstract eng:
The paper is focused on statistical inverse analysis of material model parameters, where statistical parameters of input parameters have to be identified based on experimental data (histograms of response). Stratified simulation technique of Monte Carlo combined with artificial neural network is efficiently used. The methodology is shown using example of reinforced concrete frame solved by nonlinear fracture mechanics tool for objective failure modeling of structures with significant nonlinear effects. Means and standard deviations of fracture-mechanical parameters (like modulus of elasticity, fracture energy, etc.) are the subject of identification. The paper shows differences in results when using different load levels for identification.

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