在预应力桥梁之间使用结构状态转换进行结构健康监测的条件转移
Furkan Luleci1, F Necati Catbas1
1Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816 USA.
概括
结构状态翻译 (SST) 能够使用不同结构的数据预测建筑结构条件. 这项研究成功地翻译了桥梁状态,显示了数据稀缺的结构性健康监测有希望的结果.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构健康监测 结构健康监测
- 机器学习 机器学习
背景情况:
- 在所有民用建筑物上实施全面的结构健康监测 (SHM) 往往由于广泛的传感要求而在经济上是不可行的.
- 使用来自不同,不相似结构的数据估计一个土木结构的状况,为SHM提供了一个有价值的替代方案.
- 结构状态翻译 (SST) 已经成为一种方法,通过利用不同结构的信息来预测结构响应数据.
研究的目的:
- 应用结构状态转换 (SST) 方法来将一个桥 (桥号1) 的状态转换为一个新的状态,使用结构不同桥 (桥号2) 的知识.
- 评估Domain-Generalized Cycle-Generative (DGCG) 模型在不同桥梁状态和结构之间概括和转移学到的知识方面的有效性.
主要方法:
- 域泛化循环生成 (DGCG) 模型使用域泛化方法训练了两个不同的数据域 (State-H和State-D) 从桥 #1.
- 然后使用训练好的 DGCG 模型来转换 #2 号桥的状态 (从 H 状态转换为 D 状态,从 D 状态转换为 H 状态).
- 翻译的桥梁状态与使用模态标识符和平均大小平方连贯性 (MMSC) 的实际状态进行了定量比较.
主要成果:
- 翻译的桥梁状态与真实状态具有显著的相似性,通过模态标识符和MMSC进行验证.
- 模式分析显示高相关性,最大频率差异为1.12%,最小模式保证标准 (MAC) 值为0.923.
- 平均MMSC值达到0.947的最低值,进一步证实了状态翻译的准确性.
结论:
- 结构状态翻译 (SST) 是一种非常有前途的方法,特别是在涉及数据稀缺和基于人口的结构健康监测 (PBSHM) 的研究中应对挑战.
- 该研究成功地证明了DGCG模型在SST框架内能够将结构状态信息在不相似的桥梁之间概括和传输的能力.
- 提供了对采用的方法的批判性讨论,承认潜在的担忧和未来研究领域.
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