Identifying frequency-domain operating deflection shapes and internal damage in structures using geometric vision method
Year: 2025
Type: article
Abstract: • Depth-parametrized stereo vision enables geometric optical ODS sensing • Consistent calibration enhances stereo rectification for vibration analysis • Linear model maps image motion to frequency-domain ODS responses • Wavelet-based algorithm detects structural damage from ODS fields • Findings support automated vibration and damage monitoring in service Operating deflection shape (ODS) analysis characterizes structural vibration in the frequency domain without requiring prior knowledge of excitation, offering an automatic means to assess dynamic behavior and detect damage in service. Building on this, we present a geometric vision–based sensing method for ODS identification under a depth-parameterization framework, enabling direct recovery of full-field vibration responses from spatiotemporal image motion captured by a stereo imaging system. To ensure accurate ODS reconstruction, a random sample consensus–based calibration algorithm is developed to rectify stereo geometry and establish a depth-parameterized projective system. Within this framework, we formulate a linear model that maps measured image motion of object points to their ODS responses in the frequency domain. In addition, a wavelet-based algorithm is proposed to extract damage parameters from defective structures using the reconstructed ODS fields. Experimental validations confirm that the proposed vision-based sensing method and damage identification approach are both effective and robust, providing a promising pathway for structural vibration monitoring and health assessment in complex operational environments.
Source: Results in Engineering
Authors: Haoran Zhang, Jiaren Zuo, Zhangyu Yao, Deyang Zhang, Lei Lu, Wei Pan, Zhilong Su
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4416825200
