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

Hui Chen1,2, Bin Huang1, Kong Fah Tee3

  • 1School of Civil Engineering & Architecture, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stochastic model updating method using the improved cross-model cross-mode (ICMCM) technique. It efficiently handles uncertain measurement data for accurate structural model updates, outperforming traditional methods.

Keywords:
cross-model cross-mode methodstochastic hybrid perturbation-Galerkin methodstochastic model updating

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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.