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

Sayyed M.I.|Benhadjira A.|Bentouila O.|Aiadi K.E.
한국원자력학회 (Korean Nuclear Society)|2024.01|Nuclear Engineering and Technology|제56권 제1호|pp. 100-105|DOI 10.1016/j.net.2023.09.013

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키워드

Artificial neural networkMass attenuation coefficientRadiation shielding glassesHeavy metal oxides
주제분류원자력공학

초록

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.

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.

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