The demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data-driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field-acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low-resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large-scale structures is proposed. The framework relies on a 5D, time-dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short-term memory networks. A 10-story, 10-bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.

中文翻译：

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使用复合递归神经网络和网格环境的大规模结构健康监测

近几十年来，对弹性和智能结构的需求一直在迅速增长。随着大数据革命的发生，数据驱动的结构健康监测（SHM）研究在土木工程界得到了广泛关注。特别是无监督学习，可以仅使用现场获取的数据直接使用。然而，大多数无监督学习 SHM 研究侧重于检测简单结构或组件中的损伤以及可能的低分辨率损伤定位。在这项研究中，提出了一种用于检测和定位大型结构中的损伤的无监督学习、新颖的检测框架。该框架依赖于 5D、时间相关的网格环境和新颖的时空复合自动编码器网络。该网络是自动编码器卷积神经网络和长短期记忆网络的混合体。一个 10 层、10 个开间的数值结构用于评估所提出的框架损坏诊断能力。该框架成功诊断结构健康状态，损伤检测和定位的平均准确度分别为 93% 和 85%。