A temporal-modal parallel convolutional neural network for vibration-based damage detection in frame structures

Abstract

Structural health monitoring based on vibration-induced damage detection has become increasingly important for ensuring the safety and integrity of frame structures. Although deep learning techniques have significantly improved detection performance, many existing models rely on single-domain inputs and therefore fail to fully capture the complex characteristics of structural vibration responses under real-world conditions. To address this limitation, this study proposes a novel temporal-modal parallel convolutional neural network (TM-PCNN) framework that integrates temporal information with modal characteristics for enhanced structural damage detection. The proposed framework adopts a dual-stream parallel feature-extraction architecture, consisting of a temporal convolutional network (TCN) branch for learning long-range temporal dependencies and a two-dimensional convolutional neural network (2D-CNN) branch for extracting latent modal features from inner-product matrix representations. By fusing temporal and modal features, the TM-PCNN framework enables more accurate and robust damage identification. To validate the proposed method, experiments were conducted on a five-story steel frame structure. The TM-PCNN model was compared with several representative baseline methods, including inner product matrix-2D-CNN, TCN, extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The experimental results show that the proposed model achieved an accuracy of 98.57%. In addition, feature visualization using principal component analysis and t-distributed stochastic neighbor embedding demonstrates that TM-PCNN learns compact and highly separable feature representations. These results confirm that the proposed framework provides an effective and promising solution for structural health monitoring applications.