Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA.
Sensors (Basel). 2023 Mar 20;23(6):3290. doi: 10.3390/s23063290.
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.
基于无监督学习方法的结构损伤检测是过去几十年结构健康监测（SHM）研究领域的一个热门话题。在 SHM 中，无监督学习方法仅依赖于从完整结构中获取的数据进行统计模型训练。因此，与监督学习方法相比，它们在实施民用结构的早期预警损伤检测系统方面通常被认为更具实用性。本文回顾了过去十年中基于无监督学习方法的数据驱动的结构健康监测出版物，重点关注实际应用和实用性。迄今为止，基于振动数据的异常检测是无监督学习 SHM 中最常见的方法，因此本文对此给予了更多关注。在简要介绍之后，我们根据所使用的机器学习方法的类型，介绍了无监督学习 SHM 的最新研究进展。然后，我们研究了常用于验证无监督学习 SHM 方法的基准。我们还讨论了现有文献中的主要挑战和局限性，这些问题使得将 SHM 方法从研究转化为实际应用变得困难。因此，我们概述了当前的知识差距，并为未来的方向提供了建议，以帮助研究人员开发更可靠的 SHM 方法。
