Scholarlink

<p>In recent years, the interest in robust automated operational modal analysis (AOMA) techniques has increased due to the growing demand for health monitoring of existing heritage buildings. In this study, an AOMA strategy based on the stochastic subspace identification (SSI) technique and the density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. This strategy involves the adaptive selection of key parameters for the DBSCAN algorithm and screening of candidate physical clusters. Its highlights include effectively addressing challenges such as closely spaced modes, modal splitting, weak excitation and limited sensor placement (including the number and position of the measured degrees of freedom). The accuracy of the proposed framework is validated through numerical simulations, with its robustness assessed under varying key parameters and the presence of measurement noise. The framework’s effectiveness is demonstrated through ambient excitation tests on the Yingxian wooden pagoda, the oldest and tallest wooden pagoda in China.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated operational modal analysis with adaptive DBSCAN-based algorithm and its engineering application

  • 期刊论文

  • 发表日期：

    2025年10月4日

查看全文
  • Wei Liu,
  • Na Yang,
  • Siu-seong Law,
  • Donato Abruzzese,
  • Fan Bai,
  • Xiaonan Cao

摘要

In recent years, the interest in robust automated operational modal analysis (AOMA) techniques has increased due to the growing demand for health monitoring of existing heritage buildings. In this study, an AOMA strategy based on the stochastic subspace identification (SSI) technique and the density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. This strategy involves the adaptive selection of key parameters for the DBSCAN algorithm and screening of candidate physical clusters. Its highlights include effectively addressing challenges such as closely spaced modes, modal splitting, weak excitation and limited sensor placement (including the number and position of the measured degrees of freedom). The accuracy of the proposed framework is validated through numerical simulations, with its robustness assessed under varying key parameters and the presence of measurement noise. The framework’s effectiveness is demonstrated through ambient excitation tests on the Yingxian wooden pagoda, the oldest and tallest wooden pagoda in China.