JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)

A two-stage framework for automated operational modal identification using OPTICS-KNN-based clustering

Author(s): Yi CHEN^1, ², Wenwei FU^3, ⁴, Yaozhi LUO¹, Yanbin SHEN^1, ⁴, Hui YANG², Shi-Ying WANG⁵
Affiliation(s): ¹College of Civil and Architecture Engineering, Zhejiang University, Hangzhou 310058, China; more
Corresponding email(s): fww@usts.edu.cn
Key Words: Structural health monitoring; Covariance-driven stochastic subspace identification; Automated operational modal analysis; Ordering points to identify the clustering structure; k-Nearest neighbors.

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Yi CHEN^1,², Wenwei FU^3,⁴, Yaozhi LUO¹, Yanbin SHEN^1,⁴, Hui YANG², Shi-Ying WANG⁵. A two-stage framework for automated operational modal identification using OPTICS-KNN-based clustering[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400538

@article{title="A two-stage framework for automated operational modal identification using OPTICS-KNN-based clustering",
author="Yi CHEN^1,², Wenwei FU^3,⁴, Yaozhi LUO¹, Yanbin SHEN^1,⁴, Hui YANG², Shi-Ying WANG⁵",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2400538"
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doi="https://doi.org/10.1631/jzus.A2400538"

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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Modal analysis, which provides modal parameters including natural frequencies, damping ratios, and mode shapes, is essential for assessing structural safety in structural health monitoring. Automated operational modal analysis (AOMA) offers a promising alternative to traditional methods that depend heavily on human intervention and engineering judgment. However, estimating structural dynamic properties and managing spurious modes remain challenging due to uncertainties in practical application conditions. To address this issue, we propose an automated modal identification approach comprising three key aspects: (1) identification of modal parameters using covariance-driven stochastic subspace identification; (2) automated interpretation of the stabilization diagram; and (3) an improved self-adaptive algorithm for grouping physical modes based on ordering points to identify the clustering structure (OPTICS) combined with k-nearest neighbors (KNN). The proposed approach can play a crucial role in enabling real-time structural health monitoring without human intervention. A simulated ten-story shear frame was used to verify the methodology. Identification results from a cable-stayed bridge demonstrate the practicality of the proposed method for conducting AOMA in engineering practice. The proposed approach can automatically identify modal parameters with high accuracy, making it suitable for a real-time structural health monitoring framework.

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