Work

Unsupervised Screening and Lightweight Cross-Modal Classification for Damage Identification in Offshore Jacket Structures

Year: 2026

Type: article

Abstract: Damage identification in offshore jacket structures requires state screening when damaged samples are scarce and local damage classification under measurement noise. We propose a framework that combines unsupervised screening with a lightweight bimodal classifier. An autoencoder learns undamaged response features from undamaged data alone and provides initial damage screening. The bimodal classifier combines acceleration time histories and mode-shape curvature difference (MSCD) images through bidirectional cross-modal attention and gated fusion. Beam simulations achieved 92.26 ± 1.41% accuracy at 15% Gaussian noise, exceeding direct concatenation by 3.63 percentage points. In scaled jacket tests, the autoencoder detected 98.58% of damaged samples. With 5% low-frequency colored perturbation, the bimodal classifier achieved 100% accuracy across seven structural states on a bootstrap-resampled test set. The classifier contains 3.44 million parameters and required approximately 12 ms per sample on the tested CPU.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse14212103

Authors: Wenxiang Chen, Xinkai Zhao, Jinqi Wang, Zheliang Fan

Institution: Dalian University of Technology

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Dalian University of Technology