Operational Modal Identification of Offshore Jacket Platforms Based on RDT and CNN

SHAO Weidong; SUN Youyi; HU Wenjia; SUN Shiyu

doi:10.7643/issn.1672-9242.2026.10.015

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Equipment Environmental Engineering ›› 2026, Vol. 23 ›› Issue (10) : 132-141. DOI: 10.7643/issn.1672-9242.2026.10.015

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Operational Modal Identification of Offshore Jacket Platforms Based on RDT and CNN

SHAO Weidong, SUN Youyi, HU Wenjia, SUN Shiyu

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Abstract

The work aims to address the challenges of non-stationary vibration responses, strong noise interference, and insufficient stability in damping ratio identification for deep-water jacket platforms under complex marine environmental loads, by proposing an operational modal parameter identification method suitable for field monitoring data. A novel RDT-CNN modal identification framework integrating Adaptive Random Decrement Technique (RDT), Hilbert envelope analysis, and Convolutional Neural Networks (CNN) was established. Firstly, an adaptive triggering strategy was employed to extract random decrement signatures from ambient vibration acceleration responses. Secondly, multi-channel input features comprising the RDT signals, Hilbert envelopes, and logarithmic envelopes were constructed. Subsequently, a CNN was utilized to establish the nonlinear mapping relationship between these signal features and the structural modal parameters (natural frequency and damping ratio). The numerical results demonstrated that, compared with traditional RDTs, the proposed RDT-CNN method significantly reduced identification errors: the error for natural frequency decreased from 20.0% to 4.8%, and the damping ratio error decreased from 44.3% to 12.0%. Field measurement validation further confirmed that the method exhibited superior identification stability under complex environmental vibration conditions. The adaptive RDT-CNN method effectively extracts decay characteristics from non-stationary responses, mitigates the dependence of traditional RDT on idealized exponential decay assumptions, and improves the precision of modal identification. This study provides valuable references for structural health monitoring and operational modal analysis of offshore platforms.

Key words

deep-water jacket / random decrement technique / convolutional neural network / operational modal identification / damping estimation

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SHAO Weidong, SUN Youyi, HU Wenjia, SUN Shiyu. Operational Modal Identification of Offshore Jacket Platforms Based on RDT and CNN[J]. Equipment Environmental Engineering. 2026, 23(10): 132-141 https://doi.org/10.7643/issn.1672-9242.2026.10.015

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