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Dergiler / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 10 Sayı: 2

MULTIDIRECTIONAL NEUTRON TRANSPORT AND DOSE COMPLIANCE IN MULTILAYER IRON-CONCRETE SHIELDING FOR A 50 MEV PROTON ACCELERATOR: A MONTE CARLO-MACHINE LEARNING APPROACH

Mustafa Emre Erbil(MANİSA CELAL BAYAR ÜNİVERSİTESİ HASAN FERDİ TURGUTLU TEKNOLOJİ FAKÜLTESİ)0009-0003-9394-8588
Demet Sariyer(MANİSA CELAL BAYAR ÜNİVERSİTESİ TURGUTLU MESLEK YÜKSEKOKULU)0000-0002-7803-111X
Sayfa
402–424
Yayın Tarihi
2026-08-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In conclusion, the proposed MC-ML framework provides a reliable and scalable methodology for the rapid analysis and optimization of multilayer shielding systems by significantly reducing the computational cost associated with conventional MC-based dose predictions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this study, the multidirectional transport and dose compliance of secondary neutron fields generated by the interaction of 50 MeV protons with a copper target were investigated within a multilayer iron-concrete hybrid shielding system using a hybrid Monte Carlo (MC)-Machine Learning (ML) approach.
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Temel Bulgular & Veri:High-accuracy FLUKA MC simulations were employed to compute the ambient dose equivalent H*(10) distributions along both horizontal (±x) and vertical (±y) directions in a realistic tunnel-type accelerator geometry.
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Sonuç & Katkı:In conclusion, the proposed MC-ML framework provides a reliable and scalable methodology for the rapid analysis and optimization of multilayer shielding systems by significantly reducing the computational cost associated with conventional MC-based dose predictions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Hybrid Monte Carlo-Machine Learning · Surrogate Modeling · Random Forest

Özet

In this study, the multidirectional transport and dose compliance of secondary neutron fields generated by the interaction of 50 MeV protons with a copper target were investigated within a multilayer iron-concrete hybrid shielding system using a hybrid Monte Carlo (MC)-Machine Learning (ML) approach. High-accuracy FLUKA MC simulations were employed to compute the ambient dose equivalent H*(10) distributions along both horizontal (±x) and vertical (±y) directions in a realistic tunnel-type accelerator geometry. Based on the resulting dose-distance dataset, Linear Regression (LR), Random Forest (RF), and Gradient Boosting Regressor (GBR) models were developed. During the data preprocessing stage, log(1+y) transformation and min-max normalization were applied. Model performances were evaluated using an 80% training – 20% testing split, 5-fold cross-validation, and GridSearch-based hyperparameter optimization. The results demonstrate that tree-based ensemble models can accurately capture nonlinear dose distributions spanning a wide dynamic range (RF: R² = 0.9999 / 0.9992; GBR: R² = 0.9997 / 0.9988), whereas the performance of the LR model remains limited. From a radiation protection perspective, the analysis indicates that dose levels along the horizontal direction and particularly in the upward (+y) region exceed international regulatory limits. In contrast, a significant attenuation is observed in the downward direction due to the combined moderating and absorbing effects of the concrete floor and surrounding soil. In conclusion, the proposed MC-ML framework provides a reliable and scalable methodology for the rapid analysis and optimization of multilayer shielding systems by significantly reducing the computational cost associated with conventional MC-based dose predictions.

Abstract

In this study, the multidirectional transport and dose compliance of secondary neutron fields generated by the interaction of 50 MeV protons with a copper target were investigated within a multilayer iron-concrete hybrid shielding system using a hybrid Monte Carlo (MC)-Machine Learning (ML) approach. High-accuracy FLUKA MC simulations were employed to compute the ambient dose equivalent H*(10) distributions along both horizontal (±x) and vertical (±y) directions in a realistic tunnel-type accelerator geometry. Based on the resulting dose-distance dataset, Linear Regression (LR), Random Forest (RF), and Gradient Boosting Regressor (GBR) models were developed. During the data preprocessing stage, log(1+y) transformation and min-max normalization were applied. Model performances were evaluated using an 80% training – 20% testing split, 5-fold cross-validation, and GridSearch-based hyperparameter optimization. The results demonstrate that tree-based ensemble models can accurately capture nonlinear dose distributions spanning a wide dynamic range (RF: R² = 0.9999 / 0.9992; GBR: R² = 0.9997 / 0.9988), whereas the performance of the LR model remains limited. From a radiation protection perspective, the analysis indicates that dose levels along the horizontal direction and particularly in the upward (+y) region exceed international regulatory limits. In contrast, a significant attenuation is observed in the downward direction due to the combined moderating and absorbing effects of the concrete floor and surrounding soil. In conclusion, the proposed MC-ML framework provides a reliable and scalable methodology for the rapid analysis and optimization of multilayer shielding systems by significantly reducing the computational cost associated with conventional MC-based dose predictions.

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Bu Makaleye Atıf Yap

Erbil, M. E., Sariyer, D. (2026). MULTIDIRECTIONAL NEUTRON TRANSPORT AND DOSE COMPLIANCE IN MULTILAYER IRON-CONCRETE SHIELDING FOR A 50 MEV PROTON ACCELERATOR: A MONTE CARLO-MACHINE LEARNING APPROACH. International Journal of 3D Printing Technologies and Digital Industry, Cilt: 10 Sayı: 2, 402–424. https://doi.org/10.46519/ij3dptdi.1944437

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