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.

基于振动的损伤识别一直是结构健康监测中的一项具有挑战性的任务。主要困难在于测量的振动特性与结构的损坏状态（例如，刚度降低）之间的可靠相关性。此类状态可以理想地指示结构损坏的存在、位置和严重程度。该过程被认为是从输入测量中提取特征的过程，将选定的特征映射到损坏状态。时域振动响应，例如加速度响应，在本研究中用于损伤识别。为了解决这个模式识别问题，近年来已经开发了许多方法，包括各种神经网络。本文提出了一种基于密集连接卷积网络（DenseNets）的新方法，这是计算机视觉领域的重大突破之一，用于基于振动的结构损伤识别。它在卷积神经网络架构中实现了密集连接，非常适合这项使用加速响应的研究。在训练期间学习和重用低级和高级特征。它不仅简化了训练过程中的信息流，而且保留了所有级别的特征，并且对于损伤识别往往更有效。此外，密集连接缓解了梯度消失问题并加强了通过网络的特征传播。同时，这些设计大大减少了参数的数量，使网络易于训练。通过数值和实验验证来评估所提出方法的性能。数值研究中同时考虑了建模不确定性和测量噪声。数值和实验研究的结果表明，损伤定位和量化具有高精度（例如，数值数据集上的回归值≥ 96.0%，实验数据集上的回归值≥ 94.9%）和良好的鲁棒性。