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1- Ph.D. Student, Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran
2- Assistant Professor, Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran
2- Assistant Professor, Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran
Abstract: (43 Views)
Fiber-reinforced polymer (FRP) composites are widely used in marine structures due to their high strength-to-weight ratio and good corrosion resistance. However, long-term exposure to humid and thermal environmental conditions can lead to a reduction in the mechanical properties of these materials. Traditional durability prediction models, such as the Arrhenius and Fick models, despite their widespread use, are limited in capturing the complex synergistic effects of environmental conditions due to their simplified assumptions. In this regard, the use of artificial intelligence models, especially machine learning (ML), has gained attention in recent years. This article examines the application of various machine learning algorithms in predicting the lifespan of polymer composites under humid and thermal conditions. Various studies have shown that different AI algorithms can accurately predict the degradation of mechanical properties in composites under various environmental conditions. Furthermore, input sensitivity analyses in different models have highlighted the greater importance of factors such as exposure time, temperature, pH level, and fiber volume in the degradation of mechanical properties. Despite the progress made, challenges such as the lack of field data and differences in model performance remain significant obstacles to accurately predicting the service life of composites.
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