In recent years, a new research direction in structural condition assessment has been focusing on developing automated or semi-automated procedures to identify a structure’s modal parameters from its response measurements. This is because long-term structural monitoring systems rely on the implementation of system identification methodologies that often involve the intervention of an expert user with an acquired experience in the field.

This paper aims to offer a semi-automated methodology for extracting the modal parameters independently of the chosen parametric system identification technique with minimum user involvement in the parameter selection process. Here, the framework is applied to two different parametric system identification algorithms: Data-Driven Stochastic Subspace Identification (DD-SSI) and Output Only Observer Kalman Filter (O/O OKID). The procedure can be represented as a multi-stage strategy where unsupervised tools and three clustering options are offered to the user to reach a reliable estimate of the modal parameters. The proposed procedure is validated with an application in the operational modal analysis of an existing hospital structure located in Italy. The results demonstrated excellent accuracy and robust performance of the methodology, even in the presence of closely spaced modes. The proposed procedure helps to improve the data analysis process in continuous monitoring, where usually, the algorithm’s parameters need to be constantly updated by the user.

近年来，结构状态评估的一个新研究方向一直专注于开发自动或半自动程序，以从响应测量中识别结构的模态参数。这是因为长期结构监测系统依赖于系统识别方法的实施，这些方法通常涉及具有该领域经验的专家用户的干预。

本文旨在提供一种半自动方法，用于独立于所选参数系统识别技术提取模态参数，同时最大限度地减少用户参与参数选择过程。在这里，该框架应用于两种不同的参数系统识别算法：数据驱动随机子空间识别（DD-SSI）和仅输出观察者卡尔曼滤波器（O/O OKID）。该过程可以表示为多阶段策略，其中向用户提供无监督工具和三个聚类选项，以实现对模态参数的可靠估计。建议的程序通过在意大利现有医院结构的运行模式分析中的应用进行验证。结果表明该方法具有出色的准确性和稳健的性能，即使存在紧密间隔的模式。所提出的程序有助于改进连续监测中的数据分析过程，其中通常需要用户不断更新算法的参数。