Current practices and research trends on fatigue analysis in cranes
DOI:
https://doi.org/10.5902/2179460X94321Keywords:
Cranes, Fatigue, Machine learning, Finite element method, Digital twinAbstract
Cranes are critical industrial systems subjected to prolonged cyclic and stochastic loading, making their metal structures prone to fatigue failure, especially at welded joints. This review systematically examines conventional and advanced methodologies for fatigue assessment in cranes. Traditional approaches based on S–N curves and Miner’s rule are discussed, alongside advanced local stress methods (notch stress, hot-spot stress, equivalent structural stress) and multiaxial fatigue criteria. The role of numerical simulation, including FEM and co-simulation with Multi-Body Dynamics (MBD), is highlighted. Emerging trends such as Machine Learning (ML), Digital Twin (DT) frameworks, and Bayesian reliability updating are reviewed as transformative tools for real-time prediction and risk-based inspection. The paper emphasizes the mathematical and computational foundations of these methods, aligning with the scope of applied mathematics. Finally, a critical discussion synthesizes gaps in the literature, and future research directions are proposed.
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