Abstract

Accurate calculation of gamma dose rates in medical and industrial facilities is a critical component of comprehensive dosimetry assessment. Usually, two complementary approaches are employed to this end: experimental measurements and Monte Carlo (MC) simulations, both of which have established themselves as powerful and reliable tools in radiation protection and dosimetry practice. Given the high computational cost of Monte Carlo simulations, artificial intelligence can offer a compelling and efficient alternative for predicting dose rate distributions. This study evaluates the capability of machine learning models to predict MC-calculated dose rates and to identify the optimal 60Co source arrangement for the upcoming replenishment. The replenishment scenario involves inserting six new 60Co pencil sources. Dose rate prediction was performed using FLUKA MC simulations, complemented by an Artificial Neural Network (ANN)-based predictive model. The ANN model demonstrated strong concordance with FLUKA MC results, with deviations consistently below 1%, and exhibited reliable predictive performance on previously unseen configurations. Based on the dose uniformity ratio and the coefficient of determination, configuration 3 was identified as the optimal arrangement (R2 = 0.986). The integration of machine learning with MC simulation proves highly effective, enabling rapid and accurate dose rate prediction around the 60Co source while substantially reducing computational time and CPU resource demands.

IPC Classification

Keywords