Machine Learning for Modeling Stress Evolution

Pengyu W. Mosavi A. Felde I. Azodinia M. Delavar A. Azamat A. Wei S.
2025 Budapest Tech Polytechnical Institution

Acta Polytechnica Hungarica
2025 #22 Issue 12 95 - 114 pp.

We present a detailed review and evaluation of machine learning (ML) methods for modeling and predicting stress evolution in various materials and systems. Stress evolution is considered a fundamental phenomenon in materials science, structural engineering and biomechanics. It is frequently modeled with deterministic methods, which struggle to handle high-dimensional, complex and non-linear data. A promising substitute is Machine Learning (ML), which offers instruments to enhance predictive accuracy and more effectively capture complex patterns. We used the Scopus database to find relevant literature and the PRISMA framework for systematic screening for creating an extensive database for this review. Based on how well supervised, unsupervised and deep learning approaches apply to stress modeling, under various loading and environmental circumstances, we present a new taxonomy of machine learning approaches. Furthermore, we critically evaluate these approaches advantages and disadvantages, and further highlight the significance of feature engineering, data quality and model interpretability. The review ends by outlining potential future directions, especially with regard to deep and hybrid models that combine ML with traditional techniques to improve prediction of stress evolution in a variety of applications.

applied AI , applied mathematics , artificial intelligence , big data , data science , deep learning , machine learning , stress evolution

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Department of Mechanical Engineering, Tsinghua University, No. 30 Shuangqing Road, Beijing, 100084, China
John von Neumann Faculty of Informatics, Obuda University, Becsi ut 96/B, Budapest, 1034, Hungary
Ludovika University of Public Service, Budapest, Hungary
Univerzita J. Selyeho Komarom, Slovakia Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Bécsi út 96/B, Budapest, 1034, Hungary
ABB Corporate Research Center in Germany, Kallstadter Str. 1, Mannheim, 68309, Germany
Abylkas Saginov Karaganda Technical University, No. 56 Nursultan Nazarbayev, Karaganda, 100027, Kazakhstan

Department of Mechanical Engineering
John von Neumann Faculty of Informatics
Ludovika University of Public Service
Univerzita J. Selyeho Komarom
Doctoral School of Applied Informatics and Applied Mathematics
ABB Corporate Research Center in Germany
Abylkas Saginov Karaganda Technical University

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