Damage identification of a jacket platform based on a hybrid deep learning framework

Abstract

Given the complex operational environment of offshore platforms, accurate identification of structural damage has become a crucial aspect of structural health monitoring. However, accurately pinpointing the damage locations based on vibration data under load, particularly for intricate platform structures, is a challenging task. Existing damage-identification methods, particularly those rooted in deep learning frameworks, often encounter difficulties when applied to marine platforms. Therefore, this study proposes an innovative approach. The accuracy of damage identification for marine platforms operating under unique service conditions was enhanced by introducing a deconvolutional parallel processing module and an auxiliary loss function processing module into the core ResNet50 network. This enhancement improved the accuracy of the model in detecting damage within complex marine structures. Information processing is enriched by fusing the vibration data acquired from the measurement points across different domains: time, frequency, and recurrence plots. The results of this approach were remarkable. When the algorithm model, validated through model experiments, is extended to a digital twin established based on real marine platforms, simulations and loading under real loads were performed on a refined high-fidelity finite-element model, yielding dynamic response information that closely mirrored real-world conditions. A corresponding damage-recognition database was established to support the digital twin system. For the eight different directions, the model accuracy ranged from a minimum of 87.38% to a maximum of 92.27%. This represents a significant advancement compared to the performance of the original network. Empirical experiments substantiated the efficacy of the improved algorithm, demonstrating an impressive recognition accuracy of 93.75%. This achievement underscores the potential of this method to revolutionize damage identification for marine platforms, particularly under the distinctive conditions that these structures encounter. The integration of specialized modules and enhanced processing methodologies further bolster the accuracy of deep-learning-based damage identification and makes the building of digital twin models of offshore platforms feasible.