PSI - Issue 81

Oleh Yasniy et al. / Procedia Structural Integrity 81 (2026) 116–122

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The results obtained open up prospects for using machine learning models for diagnosing the technical condition of machine and structure elements, predicting the residual resource, as well as creating adaptive digital models in the context of the concept of digital twins. Further research can be aimed at expanding the feature space, in particular, including crack resistance parameters, geometric characteristics of samples, temperature effects and environmental conditions, as well as integrating machine learning with physically based models (hybrid approaches). References Acar, E.; Ozbulut, O.E.; Karaca, H.E, 2015. Experimental Investigation and Modeling of the Loading Rate and Temperature Dependent Superelastic Response of a High Performance Shape-Memory Alloy. Smart Mater. Struct., 24, 075020. Ahmed, S., Alshater, M. M., Ammari, A. E., & Hammami, H., 2022. Artificial intelligence and machine learning in finance: A bibliometric review. 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