Operational modal analysis (OMA) of long-span road bridges from vibration measurements is a topic of interest due to its potential applications within structural health monitoring. Algorithms for the automation of OMA (AOMA) have been proposed since around 2005, relying on machine learning to automate previously manual tasks. The fundamental principles of AOMA are explained in the theory of this work, as are the functioning principles of the clustering algorithms employed by most AOMA algorithms. A performance comparison of six AOMA algorithms (Magalhaes 2009, Reynders 2012, Zhang 2014, Neu 2017, Yang 2019, Kvåle 2020) is provided using real-world data from the Hardanger suspension bridge. To the authors’ best knowledge, it is the first comparison of AOMA algorithms for bridges. While Reynders 2012 is shown to be the only fully automated algorithm, Magalhaes 2009, Zhang 2014, and Kvåle 2020 are the algorithms with the highest successful detection rate. Neu 2017 and Yang 2019, however, make the least detection errors, respectively, the least false detections and the least duplicate detections. The variability amongst datasets is shown not to impact the algorithms’ comparison. An outright recommendation on which algorithm to use is impossible due to the multitude of potential use cases, but Neu 2017 seems to provide the best performance compromise for the tested case.

由于其在结构健康监测中的潜在应用，基于振动测量的大跨度公路桥梁的运行模态分析 (OMA) 是一个令人感兴趣的话题。自 2005 年左右以来，已经提出了 OMA（AOMA）自动化的算法，依靠机器学习来自动化以前的手动任务。AOMA 的基本原理在这项工作的理论中进行了解释，正如大多数 AOMA 算法所采用的聚类算法的功能原理一样。使用 Hardanger 吊桥的真实数据提供了六种 AOMA 算法（Magalhaes 2009、Reynders 2012、Zhang 2014、Neu 2017、Yang 2019、Kvåle 2020）的性能比较。据作者所知，这是桥梁 AOMA 算法的首次比较。虽然 Reynders 2012 被证明是唯一的全自动算法，Magalhaes 2009、Zhang 2014 和 Kvåle 2020 是成功检测率最高的算法。然而，Neu 2017 和 Yang 2019 的检测错误最少，错误检测最少，重复检测最少。数据集之间的可变性显示不会影响算法的比较。由于潜在用例众多，不可能完全推荐使用哪种算法，但 Neu 2017 似乎为测试用例提供了最佳性能折衷方案。数据集之间的可变性显示不会影响算法的比较。由于潜在用例众多，不可能完全推荐使用哪种算法，但 Neu 2017 似乎为测试用例提供了最佳性能折衷方案。数据集之间的可变性显示不会影响算法的比较。由于潜在用例众多，不可能完全推荐使用哪种算法，但 Neu 2017 似乎为测试用例提供了最佳性能折衷方案。