Abstract
Structural health monitoring based on vibration signal analysis has been extensively employed for damage identification. Mainstream machine learning techniques, such as convolutional neural networks (CNN), often rely on single-domain inputs, which may provide limited information for accurate damage identification. To overcome this limitation, this study proposes a novel approach that combines an inner product matrix (IPM) with a parallel CNN (IPM-PCNN) to extract multidimensional features for detecting structural damage in a steel frame structure. The proposed IPM-PCNN framework consists of a one-dimensional (1D) CNN branch for processing time series data, a two-dimensional (2D) CNN branch for handling structural modal data, and several fully connected layers. This unique combination leverages the strengths of both 1D and 2D CNNs to capture temporal and modal features of the signal effectively. To validate the effectiveness and superiority of the proposed method, a five-story steel frame model is used as the research object, and five comparative methods are evaluated under the same experimental conditions. The results demonstrate that the IPM-PCNN model can automatically extract relevant features from the signals to accurately identify structural damage, achieving an accuracy of 96.60% on the test set, outperforming machine learning methods in performance. Furthermore, the internal inference processes of these methods are explored and visualized to provide insights into their decision-making mechanisms.