Structural Damage Identification Based on Transfer Learning and Power Spectral Density

Abstract

This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine-tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency-domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient (R value), and reduced mean absolute error (MAE) compared to time-domain signals. The effectiveness of this method was verified on a six-story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real-world data, thereby facilitating effective structural health monitoring and damage identification.