A Hybrid Monte Carlo–Machine Learning Framework for High-Energy Neutron Shielding Using Boron-Enhanced Concrete - Demet Sariyer; Elif Yıldırım (International Journal of Computational and Experimental Science and Engineering, 2026)
Secondary neutrons produced by proton-target interactions in high-energy proton accelerator facilities present a major shielding challenge due to their high penetrability and broad energy spectra. In this study, neutron dose attenuation in B₄C- and FeB-enhanced concretes containing 5%, 10%, and 15% additives was investigated at a proton energy of 1000 MeV using FLUKA-based Monte Carlo (MC) simulations coupled with Machine-learning (ML) surrogate models.MC-generated dose data were used to train log-linear Linear Regression (log-linear LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Gradient Boosting Regressor (GBR) models to enable rapid dose prediction. The results show that RF and GBR achieve the highest predictive accuracy under all configurations, with test-set R² values of approximately 0.98-0.99 in tunnel air and 0.99-0.996 in concrete shielding. In contrast, the LR model performs poorly in shielding regions due to strong nonlinearity, while KNN also provides high predictive accuracy exceeding 90%, albeit with lower performance compared to RF and GBR. A comparative analysis reveals that FeB-enhanced concrete exhibits more complex attenuation behavior due to the combined effects of iron-induced scattering and boron absorption. Overall, the validated hybrid MC-ML framework demonstrates that RF- and GBR-based surrogate models provide a fast, reliable, and computationally efficient approach for neutron dose estimation and shielding optimization in high-energy proton accelerator facilities.