Structural damage detection (SDD) still suffers from environmental uncertainties or modeling errors, causing a gap between the numerical model and the real structure. It results in performance degradation in the application of many model-based methods, which are usually designed on a numerical model and needed to be applied to a real structure. Such a situation is defined as a cross-domain SDD problem in this work. This paper aims to address the cross-domain SDD problem by designing a feature-extractor to generate both damage-sensitive and domain-invariant features, instead of trying to reduce the gap, as the traditional methods do. A domain adaptation (DA) neural network is designed and trained on the data from both the numerical model and the real structure at the same time. In addition, no damage label of the real structure is needed. Both numerical and laboratory experiments show that the proposed method has excellent performance and outperforms the baseline model, a traditional convolutional neural network (CNN). This paper provides a new methodology to solve the cross-domain SDD problem, that is, to learn better features instead of just trying to reduce the gap.

中文翻译：

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通过深度迁移学习进行基于动力学的跨域结构损伤检测

结构损伤检测 (SDD) 仍然受到环境不确定性或建模错误的影响，导致数值模型与真实结构之间存在差距。它导致许多基于模型的方法的应用性能下降，这些方法通常是在数值模型上设计的，需要应用于实际结构。这种情况在这项工作中被定义为跨域 SDD 问题。本文旨在通过设计特征提取器来生成损伤敏感和域不变特征来解决跨域 SDD 问题，而不是像传统方法那样试图减少差距。域适应 (DA) 神经网络被设计和训练同时来自数值模型和真实结构的数据。此外，不需要真实结构的损坏标签。数值和实验室实验都表明，所提出的方法具有出色的性能，并且优于传统的卷积神经网络 (CNN) 基线模型。本文提供了一种解决跨域SDD问题的新方法，即学习更好的特征而不是仅仅试图缩小差距。