A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

Area of Science:

• Structural dynamics and computational mechanics.
• Advanced methods for structural model updating.

Background:

• Structural model updating is crucial for accurate performance prediction.
• Limited measurement data and inherent uncertainties pose significant challenges.
• Existing methods struggle with data uncertainty and computational efficiency.

Purpose of the Study:

• To develop a new stochastic model updating method.
• To address challenges of limited and uncertain measurement data.
• To improve computational efficiency in structural model updating.

Main Methods:

• Combines the stochastic hybrid perturbation-Galerkin method with the improved cross-model cross-mode (ICMCM) technique.
• Establishes a stochastic model updating equation considering uncertain modal data.
• Solves the equation to obtain random updated coefficients and their statistical characteristics.

Main Results:

• The proposed method effectively handles significant uncertainty in measured data.
• Achieves computational efficiency several orders of magnitude higher than Monte Carlo simulation.
• Demonstrates superior accuracy compared to the standard cross-model cross-mode (CMCM) method, especially with rank deficiency.
• Successfully updates structural stiffness and mass, with updated frequencies consistent with measurements.

Conclusions:

• The novel stochastic ICMCM method provides an effective and efficient approach for structural model updating.
• The method's ability to handle data uncertainty and rank deficiency ensures practical significance.
• Validated through numerical and experimental examples, confirming its reliability and accuracy.