Integrated Frameworks for Artificial Intelligence in Radioactive Waste Characterization and Nuclear Lifecycle Safety

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Year : 2026 | Volume : 16 | 02 | Page :
    By

    Chethan Rao,

  1. Associate Professor, Department of Physics, Amruta Institute of Engineering and Management Sciences,Bengaluru, Karnataka, India

Abstract

The management and characterization of radioactive waste represent a pivotal challenge for the global energy sector, requiring the convergence of advanced physics, material science, and computational intelligence. As the nuclear industry undergoes a paradigm shift toward decommissioning legacy facilities and establishing deep geological repositories, the limitations of traditional, manually-intensive waste management processes have become increasingly apparent. Rigid separation from the biosphere is required for radioactive waste, which is defined by international standards as material containing nuclear substances for which no future use is anticipated. The primary objective is to manage these materials such that any return of radio nuclides to the environment occurs at rates or concentrations that are demonstrably harmless. Achieving this objective necessitates a lifecycle approach to characterization, identifying the physical, chemical, and radiological properties of waste at every stage from generation and processing to transportation, storage, and final disposal. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this context, offering the ability to automate complex sorting tasks, enhance the precision of radioisotope quantification, and provide robust predictive models for long-term safety assessments. This study explores the technical foundations and industrial applications of AI in nuclear waste characterization and safety, articulating how these technologies address the inherent hazards of radioactive materials while optimizing operational efficiency.

Keywords: Artificial Intelligence (AI), Radioactive Waste Characterization, Machine Learning, Nuclear Safety Management, Digital Twins and Predictive Analytics.

How to cite this article:
Chethan Rao. Integrated Frameworks for Artificial Intelligence in Radioactive Waste Characterization and Nuclear Lifecycle Safety. Journal of Nuclear Engineering & Technology. 2026; 16(02):-.
How to cite this URL:
Chethan Rao. Integrated Frameworks for Artificial Intelligence in Radioactive Waste Characterization and Nuclear Lifecycle Safety. Journal of Nuclear Engineering & Technology. 2026; 16(02):-. Available from: https://journals.stmjournals.com/jonet/article=2026/view=247354


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Ahead of Print Subscription Review Article
Volume 16
02
Received 04/05/2026
Accepted 18/06/2026
Published 23/06/2026
Publication Time 50 Days
1

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