Artificial neural network approach for calculating mass attenuation coefficient of different glass systems

Abstract

In this study, we propose an alternative approach using Artificial Neural Networks (ANN) for determining Mass Attenuation Coefficients (MAC) in various glass systems. This method takes into account the weights of glass compositions, density, and photon energy as input features. The ANN model was trained and tested on a dataset consisting of 650 data points and subsequently validated through a K-fold cross-validation procedure. Our findings demonstrate a high level of accuracy, with R2 values ranging from 0.90 to 0.99. Additionally, the model exhibits robust extrapolation capabilities with an R2 score of 0.87 for predicting MAC values in a new glass system. Furthermore, this approach significantly reduces the need for costly and time-consuming computations and experiments, making it a potential tool for selecting materials for effective radiation protection.

Keywords

• Artificial neural network
• Mass attenuation coefficient
• Radiation shielding glasses
• Heavy metal oxides