.png){kind=icon}
.png){kind=icon}
Return
Structural damage identification based on pattern-coupled sparse Bayesian learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ymssp.2026.113893.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Structural damage identification inevitably involves uncertainties, necessitating their explicit consideration to enhance the reliability and precision of detection frameworks. As a prominent sparse recovery technique, sparse Bayesian learning (SBL) has demonstrated effectiveness in damage identification by leveraging structural sparsity through automatic relevance determination (ARD) priors. However, conventional SBL implementations adopt an oversimplified probabilistic model that assumes mutual independence among damage parameters, thereby failing to account for inherent spatial correlations between adjacent structural elements. This study proposes a novel pattern-coupled SBL methodology that incorporates coupled Gaussian priors to simultaneously characterize and autonomously learn both sparsity patterns and parameter correlations. This dual-learning mechanism enables enhanced precision in quantifying damage severity through correlation-aware parameter estimation, and improved robustness against measurement noise and modeling errors. Furthermore, the proposed framework extends conventional sparse recovery capabilities by effectively resolving both distributed and block-sparse damage configurations—a crucial feature where traditional SBL approaches exhibit suboptimal performance. Numerical studies on a cable-stayed bridge model and experimental investigations of a space frame validate the method’s effectiveness in accurately identifying and quantifying single and multiple damage scenarios. Compared with the SBL method, the identification accuracy and robustness of the proposed method are significantly improved, especially for structural damage with block-sparse characteristics.
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W