论文标题
结构性状态翻译：使用域将军进行结构健康监测的民用结构之间的条件转移
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring
论文作者
论文摘要
在每个民用结构上使用具有广泛感应安排的结构健康监测（SHM）系统可能是昂贵和不切实际的。已经引入了各种概念来减轻此类困难，例如基于人群的SHM（PBSHM）。然而，文献中提出的研究并未充分解决有关访问不同民用结构不同结构状态（条件）信息的挑战。本文的研究介绍了一个名为“结构性状态翻译（SST）”的新型框架，该框架旨在根据从不同结构获得的信息估算不同民用结构的响应数据。 SST可以定义为在发现和学习不同民用结构的来源领域的域名代表后，将一种民用结构的状态转化为另一种状态。 SST采用域将来循环生成（DGCG）模型来学习从在两个不同结构条件下的数字桥结构中获得的加速数据集中的域不变表示。换句话说，该模型在三个不同的数字桥模型上进行了测试，以转化其结构条件。 SST通过平均幅度方相一致性（MMSC）和模态标识符的评估结果表明，翻译的桥态（合成状态）与真实的桥态（合成状态）显着相似。因此，真实和翻译的桥状状态的最小和最大平均MMSC值为91.2％和97.1％，最小值和最大差异为5.71％和0％，最小和最大模态保证标准（MAC）值为0.998和0.870。这项研究对于数据稀缺和PBSHM至关重要，因为它表明可以在结构实际上处于不同状态或状态时从结构中获取数据。
Using Structural Health Monitoring (SHM) systems with extensive sensing arrangements on every civil structure can be costly and impractical. Various concepts have been introduced to alleviate such difficulties, such as Population-based SHM (PBSHM). Nevertheless, the studies presented in the literature do not adequately address the challenge of accessing the information on different structural states (conditions) of dissimilar civil structures. The study herein introduces a novel framework named Structural State Translation (SST), which aims to estimate the response data of different civil structures based on the information obtained from a dissimilar structure. SST can be defined as Translating a state of one civil structure to another state after discovering and learning the domain-invariant representation in the source domains of a dissimilar civil structure. SST employs a Domain-Generalized Cycle-Generative (DGCG) model to learn the domain-invariant representation in the acceleration datasets obtained from a numeric bridge structure that is in two different structural conditions. In other words, the model is tested on three dissimilar numeric bridge models to translate their structural conditions. The evaluation results of SST via Mean Magnitude-Squared Coherence (MMSC) and modal identifiers showed that the translated bridge states (synthetic states) are significantly similar to the real ones. As such, the minimum and maximum average MMSC values of real and translated bridge states are 91.2% and 97.1%, the minimum and the maximum difference in natural frequencies are 5.71% and 0%, and the minimum and maximum Modal Assurance Criterion (MAC) values are 0.998 and 0.870. This study is critical for data scarcity and PBSHM, as it demonstrates that it is possible to obtain data from structures while the structure is actually in a different condition or state.
