An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain...

Area of Science:

• Structural Engineering
• Computational Mechanics
• Damage Detection

Background:

• Accurate structural damage identification is crucial for safety and maintenance.
• Existing methods may not fully leverage prior information for improved accuracy.
• Modal analysis techniques are widely used for structural assessment.

Purpose of the Study:

• To propose a novel Bayesian damage identification method guided by the Improved Elemental Modal Strain Energy Ratio (IEMSER).
• To enhance the accuracy of damage parameter estimation by effectively utilizing prior information.
• To validate the proposed method on both numerical and experimental structural data.

Main Methods:

• Development of the IEMSER indicator using measured frequencies and mode shapes for preliminary damage assessment.
• Guiding a sparse prior distribution using IEMSER-based damage estimates.
• Application of Markov Chain Monte Carlo (MCMC) sampling to compute posterior Probability Density Functions (PDFs).
• Determination of Maximum A Posteriori (MAP) estimates for damage parameters.

Main Results:

• IEMSER provides accurate preliminary damage estimates, forming a reliable sparse prior.
• The Bayesian method with the proposed prior significantly improves damage identification accuracy.
• Numerical simulations on a steel truss bridge demonstrate high accuracy.
• Experimental validation on an 18-story frame structure confirms the method's practical applicability.

Conclusions:

• The proposed Bayesian damage identification method effectively utilizes prior information for enhanced accuracy.
• IEMSER serves as a robust indicator for guiding the Bayesian framework.
• The method is applicable to diverse structures, including bridges and frame buildings.