Parallel convolutional neural network toward high efficiency and robust structural damage identification

Artificial intelligence has been implemented recently for processing and analyzing monitored data for damage detection and identification in the field of structural health monitoring (SHM). Existing machine learning methods such as convolutional neural networks (CNNs) usually rely on inputs from a single domain (time or frequency), which may only provide partial information for damage identification. To address this issue, this work proposes a parallel convolutional neural network (P-CNN) that extracts multidimensional features assisted by a computer vision technique. The proposed network comprises a one-dimensional (1D) CNN branch, a two-dimensional (2D) CNN branch, and several fully connected layers. The efficiency and robustness of the proposed network were validated by a public experimental dataset. Our results show that (1) the features extracted by the P-CNN were separated more easily compared with those by 1D-CNN or 2D-CNN; (2) when detecting structural damages, the accuracy of the P-CNN is above 99.4%; (3) the P-CNN exhibits a robust performance when subjected to a high (5 dB) signal-to-noise ratio of the original data; and (4) when compared with traditionally used methods such as GoogLenet and Resnet, the P-CNN outperforms on many aspects of damage identification. We envision that the proposed P-CNN can be integrated into advanced SHM systems with high fidelity and intelligence.