Detail publikace

Probabilistic Inverse Analysis: Random Material Parameters of Reinforced Concrete Frame

Originální název

Probabilistic Inverse Analysis: Random Material Parameters of Reinforced Concrete Frame

Anglický název

Probabilistic Inverse Analysis: Random Material Parameters of Reinforced Concrete Frame

Jazyk

en

Originální abstrakt

The paper focuses on the statistical inverse analysis of material model parameters, where statistical moments of input parameters have to be indentified based on experimental data (histograms of response). Stratified simulation technique of Monte Carlo type combined with artifical neural network is efficiently used. The methodology is shown using the 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.

Anglický abstrakt

The paper focuses on the statistical inverse analysis of material model parameters, where statistical moments of input parameters have to be indentified based on experimental data (histograms of response). Stratified simulation technique of Monte Carlo type combined with artifical neural network is efficiently used. The methodology is shown using the 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.

BibTex


@inproceedings{BUT18369,
  author="David {Lehký} and Drahomír {Novák}",
  title="Probabilistic Inverse Analysis: Random Material Parameters of Reinforced Concrete Frame",
  annote="The paper focuses on the statistical inverse analysis of material model parameters, where statistical moments of input parameters have to be indentified based on experimental data (histograms of response). Stratified simulation technique of Monte Carlo type combined with artifical neural network is efficiently used. The methodology is shown using the 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.",
  booktitle="Novel Applications of Neural Networks in Engineering",
  chapter="18369",
  year="2005",
  month="august",
  pages="147--154",
  type="conference paper"
}