Structural Damage Identification with Machine Learning Based Bayesian Model Selection for High-Dimensional Systems

Identifying structural damage in high-dimensional systems remains a major challenge due to the curse of dimensionality and the inherent sparsity of real-world damage scenarios. Traditional Bayesian or optimization-based approaches often become computationally intractable when applied to structures with a large number of uncertain parameters, where only a few members are actually damaged. To address this problem, this study proposes a Machine Learning and Widely Applicable Information Criterion (WAIC) based Bayesian framework for efficient and accurate damage identification in high-dimensional systems. In the proposed approach, an ML is first trained using simulated modal responses under randomly generated damage patterns. The ML predicts the most likely damaged members by measured responses, effectively reducing the high-dimensional search space to a small subset of candidates. Subsequently, a WAIC is employed to estimate the model combined by these candidates, while automatically selecting the optimal damage model. By combining the localization capability of ML with the uncertainty quantification of Bayesian inference, the proposed method achieves high identification accuracy with significantly reduced computational cost of model selection. Numerical experiments on a high-dimensional truss system demonstrate that the method can accurately locate and quantify multiple damages even under noise contamination. The results confirm that the hybrid framework effectively mitigates the curse of dimensionality and provides a robust solution for structural damage identification in large-scale structural systems.