Development and evaluation of MgO-grit iron aggregate heavy density concrete for high temperature radiation shielding: Experimental and machine learning approach

Abstract

This research focuses on the development and evaluation of heavy density concrete (HDC) for radiation shielding, utilizing both experimental and machine learning techniques. Various HDC specimens with different proportions (25 %, 50 %, 75 %, and 100 %) of grit iron aggregate replacing normal weight aggregate, in addition to control specimens with no grit iron scale aggregate, were cast. These samples were subjected to testing at temperatures ranging from room temperature to 1200°C to assess properties such as compressive strength, rebound number, ultrasonic pulse velocity, density loss, mass loss, linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), tenth-value layer (TVL), and mean free path (MFP). Ensemble learning algorithms were employed using experimental data to predict compressive strength and new empirical expressions were formulated for mechanical and radiation shielding properties, including LAC, HVL, and TVL. The addition of grit iron aggregate, combined with MgO, demonstrated a significant improvement in the mechanical and radiation shielding properties of HDC. This research holds promise for applications in nuclear reactors operating at high temperatures.