AI-Driven Radiation Risk Analysis for Sustainable Construction with Agricultural Byproducts

Agricultural byproducts are increasingly finding utility in the construction and building industry as additives to cement, addressing the environmental impact associated with cement production. Due to the environmental repercussions of cement production, the construction industry has turned to incorporating agricultural waste materials as additives in cement, marking a sustainable shift in building practices. This research focuses on assessing the radiological properties of agricultural byproducts employed in construction, with a special emphasis on their activity concentrations, including 226Ra series, 232Th series, and 40K isotopes. The study measures alpha and gamma radiation level and employs artificial intelligence techniques to analyze these data, with particular attention to the linear regression algorithm's efficacy. Through the application of machine learning, the recommendation system empowers professionals to make informed choices when selecting agricultural byproducts for construction materials.