The expansion of offshore wind energy into harsh marine environments is critically challenged by synergistic
corrosion-fatigue (C-F), which compromises the structural integrity of turbines. While digital twin (DT) technology offers a
promising solution, existing frameworks for C-F prognostic health management are often limited by lack of adaptation to
dynamic environmental data and simplistic, deterministic maintenance strategies. To address these deficiencies, this study
develops and demonstrates a high-fidelity DT framework founded on three innovations: a coupled-physics model that captures
the synergistic feedback between corrosion and fatigue; an artificial intelligence (AI)-driven, Gaussian Process (GP)-based
physics-informed machine learning (PIML) engine for real-time environmental adaptation; and a stochastic, opportunistic
condition-based maintenance (O-CBM) framework for risk-informed decision-making. These capabilities are demonstrated
through a detailed theoretical case study of a floating offshore wind turbine (OWT) tower base, integrating Bayesian inference
for model updating with Monte Carlo simulation for lifecycle performance evaluation. Results demonstrate that modeling C-F
synergy is critical, reducing predicted service life by 67% compared to fatigue-only analysis, while the O-CBM policy, enabled
by the DT's probabilistic intelligence, reduces lifecycle costs by 13.5% and failure risk by 21% over traditional approaches. The
study establishes that such an integrated approach, combining coupled physics with AI-driven adaptation and stochastic
optimization, is essential for the reliable and economically viable management of offshore assets.
Yasmin Ali — 1)Department of Civil Engineering, Sichuan University, Chengdu 610065, China 2)Department of Civil Engineering, Delta University for Science and Technology, Gamasa 11152, Egypt
Kaoshan Dai — 1)Department of Civil Engineering, Sichuan University, Chengdu 610065, China 2)3State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China
Ahmed Elgammal — Department of Civil Engineering, Sichuan University, Chengdu 610065, China
Yuxiao Luo — 1)Department of Civil Engineering, Sichuan University, Chengdu 610065, China 2)National Engineering Technology Research Centre for Prefabrication Construction in Civil Engineering, Tongji University, Shanghai 200092, China
Junlin Heng — Department of Civil Engineering, Sichuan University, Chengdu 610065, China
Charalampos Baniotopoulos — Department of Civil Engineering, School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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