Densely connected convolutional networks for vibration based structural damage identification

Vibration-based damage identification has been a challenging task in structural health monitoring. The main difficulty lies on the reliable correlation between the measured vibration characteristics and damage states (e.g., stiffness reductions) of structures. Such states can ideally indicate the presence, locations, and severities of structural damages. The procedure is considered as a feature extraction process from the input measurement, mapping the selected features to damage states. Time domain vibration responses, e.g., acceleration responses, are used in this study for damage identification. To address this pattern recognition problem, many methods have been developed including various neural networks in recent years. This paper proposes a novel approach based on densely connected convolutional networks (DenseNets), which is one of the major breakthroughs in the computer vision community, for vibration based structural damage identification. It implements dense connectivity in the convolutional neural network architecture, which fits well for this study using acceleration responses. Both low-level and high-level features are learned and reused during training. It not only eases the information flow during training, but also preserves all levels of features and tends to be more effective for damage identification. Besides, the dense connectivity alleviates the gradient vanishing problem and strengthens feature propagation through the network. In the meantime, these designs substantially reduce the number of parameters, making the network easy to train. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Both modelling uncertainties and measurement noises are considered in numerical studies. The results from numerical and experimental studies demonstrate that the damage localization and quantification are achieved with high accuracies (e.g., Regression value > 96.0% on numerical datasets, and > 94.9% on experimental datasets) and good robustness.