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A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.

Giuseppe Varone1, Cosimo Ieracitano2, Aybike Özyüksel Çiftçioğlu3

  • 1Department of Neuroscience and Imaging, University of Chieti Pescara, 66100 Chieti, Italy.

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Summary
This summary is machine-generated.

Machine learning models accurately predict gamma-ray shielding in concrete composites. The Hierarchical Extreme Learning Machine (HELM) model demonstrated superior performance, offering a data-driven alternative to traditional methods for radiation shielding applications.

Keywords:
XCOMhierarchical extreme machine learninglinear attenuation coefficient

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Area of Science:

  • Materials Science and Engineering
  • Nuclear Engineering and Radiation Shielding
  • Computational Science and Machine Learning

Background:

  • High-energy photon shielding (X-rays, gamma-rays) is crucial in industrial and healthcare settings.
  • Polymer composites and mineral admixtures offer potential for enhancing concrete's shielding capabilities.
  • Traditional shielding calculations are often time-consuming and resource-intensive.

Purpose of the Study:

  • To develop a dataset for assessing gamma-ray shielding behavior of concrete composites using magnetite and mineral powders.
  • To investigate the efficacy of data-driven machine learning (ML) approaches as an alternative to theoretical calculations.
  • To compare the performance of various ML models in predicting the linear attenuation coefficient (LAC) of concrete composites.

Main Methods:

  • A dataset was created using magnetite and seventeen mineral powder combinations with concrete, varying densities and water/cement ratios.
  • Photon cross-sections were computed using the National Institute of Standards and Technology (NIST) XCOM database to determine LAC.
  • Multiple ML regressors, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Hierarchical Extreme Learning Machine (HELM), were trained and evaluated.

Main Results:

  • The Hierarchical Extreme Learning Machine (HELM) architecture significantly outperformed other ML models (SVM, CNN, Random Forest, etc.) in predicting LAC.
  • HELM demonstrated strong consistency with XCOM-simulated LAC values, validated by stepwise regression and correlation analysis.
  • The HELM model achieved the highest R2score and the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), indicating superior accuracy.

Conclusions:

  • Data-driven ML techniques, particularly HELM, can effectively replicate and predict the gamma-ray shielding properties of concrete composites.
  • The developed dataset and ML models provide a valuable, efficient alternative for assessing radiation shielding materials.
  • HELM offers a promising approach for optimizing composite material design for enhanced radiation protection in various applications.