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A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities

Author

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  • Harleen Kaur Sandhu

    (Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA
    These authors contributed equally to this work.)

  • Saran Srikanth Bodda

    (Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA
    These authors contributed equally to this work.)

  • Abhinav Gupta

    (Center for Nuclear Energy Facilities and Structures, North Carolina State University, Raleigh, NC 27695, USA
    These authors contributed equally to this work.)

Abstract

The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali–silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges.

Suggested Citation

  • Harleen Kaur Sandhu & Saran Srikanth Bodda & Abhinav Gupta, 2023. "A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2628-:d:1093827
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    References listed on IDEAS

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    2. Gursel, Ezgi & Madadi, Mahboubeh & Coble, Jamie Baalis & Agarwal, Vivek & Yadav, Vaibhav & Boring, Ronald L. & Khojandi, Anahita, 2025. "The role of AI in detecting and mitigating human errors in safety-critical industries: A review," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    3. Eduard Khomiak & Roman Trishch & Joanicjusz Nazarko & Miloslav Novotný & Vladislavas Petraškevičius, 2025. "Method of Quality Control of Nuclear Reactor Element Tightness to Improve Environmental Safety," Energies, MDPI, vol. 18(9), pages 1-14, April.

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