Investigation of model uncertainties in Bayesian structural model updating.

Area of Science:

• Engineering
• Computational Mechanics
• Statistical Modeling

Background:

• Finite element (FE) models require updating to align with experimental data, improving prediction accuracy.
• Discrepancies arise from uncertainties in structural parameters, measurements, data completeness, and inherent model simplifications (model uncertainties).

Purpose of the Study:

• To present a model updating procedure within the Bayesian statistical framework.
• To demonstrate the consideration of model uncertainties in Bayesian updating using a numerical example.

Main Methods:

• Applying model updating procedures to reconcile FE model output with experimental data.
• Utilizing the Bayesian statistical framework to incorporate and manage model uncertainties.
• Employing a numerical example with varying degrees of nonlinearity to illustrate the methodology.

Main Results:

• The updated FE model shows improved agreement with experimental data.
• The Bayesian approach effectively accounts for model uncertainties, enhancing model reliability.
• The numerical example validates the proposed method across different nonlinearity levels.

Conclusions:

• Bayesian model updating provides a robust framework for improving FE model accuracy and reliability.
• Addressing model uncertainties is crucial for dependable structural performance predictions in engineering applications.