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<p>This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis for a sparse prior distribution. Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. A numerical case of a steel truss bridge shows that IEMSER’s preliminary damage estimates closely match actual damage, yielding a reliable sparse prior and significantly improving identification accuracy. The method’s effectiveness is further validated using modal test data from an 18-story frame structure, confirming its applicability to real structures.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月2日

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  • Li Chen,
  • Hui Chen,
  • Lu-ling Liu

摘要

This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis for a sparse prior distribution. Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. A numerical case of a steel truss bridge shows that IEMSER’s preliminary damage estimates closely match actual damage, yielding a reliable sparse prior and significantly improving identification accuracy. The method’s effectiveness is further validated using modal test data from an 18-story frame structure, confirming its applicability to real structures.