A comparative study on applicability and efficiency of machine learning algorithms for modeling gamma-ray shielding behaviors

Bilmez B., TOKER O. , ALP S. , ÖZ E. , İÇELLİ O.

NUCLEAR ENGINEERING AND TECHNOLOGY, vol.54, no.1, pp.310-317, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 54 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1016/j.net.2021.07.031
  • Page Numbers: pp.310-317
  • Keywords: Mass attenuation coefficient, Artificial neural network, Fuzzy logic, Non-linear regression analysis, MASS ATTENUATION COEFFICIENT, ARTIFICIAL NEURAL-NETWORKS, RADIATION


The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/ antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).