Potential application of artificial intelligence to the alpha and gamma radiation from agricultural byproducts used as building and construction materials

Abstract

Recycled agricultural wastes are being used in the building and construction sector as cement additives as a result of the environmental impact of cement production. Agricultural byproducts, on the other hand, are naturally occurring radioactive elements that could expose people and the environment to radiation dangers. As a result, this research assesses the radiological characteristics of agricultural byproducts utilized as building and construction materials with special attention to their activity concentrations (226Ra series, 232Th series, and 40K isotopes). The levels of alpha and gamma radiation were measured via the activity concentrations. Alpha and gamma radiation (output data) and activity concentrations (input data) were trained using artificial intelligence techniques, and the model's effectiveness was evaluated. In terms of the metrics of the model, the linear regression algorithm outperformed other algorithms. Finally, none of the agricultural byproducts studied are at risk from alpha and gamma radiation. Thus, the findings provide the reference information needed to build a framework for radiation monitoring of surveyed agricultural byproducts.

Keywords

• Alpha radiation
• Artificial intelligence
• Gamma radiation
• Good health and well-being
• Responsible consumption and production
• Sustainable cities and communities