Shielding of Gamma Radiation by Using Porous Materials

Gedik, Şansal; Baytaş, A. Filiz

doi:10.12693/aphyspola.128.b-174

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…It should be clearly stated that it is not always feasible for manufacturers of radiation-shielding concrete to evaluate a wide variety of mineral powders and/or their mixing ratios with heavy nanoparticles on a workbench in the lab. As a result, computational approaches such as Monte Carlo code [13][14][15] or the National Institute of Standards and Technology, the NIST photon cross-section database (XCOM) software are commonly used [8,9,[16][17][18][19][20][21][22][23]. The XCOM database has been used in several academic articles to determine the (µ/ρ) rate for various elements [24], composites [25], compounds [26,27], concretes [28,29], glasses [30][31][32], polymers [33,34], rocks [31,35], construction materials [36], alloys [37], ceramics [38], and biological materials [39,40].…”

Section: Introductionmentioning

confidence: 99%

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

Varone

Ieracitano

Çiftçioğlu

et al. 2023

Entropy

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The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials’ shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete’s γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE.

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Section: Introductionmentioning

confidence: 99%

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

Varone

Ieracitano

Çiftçioğlu

et al. 2023

Entropy

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“…As this subject is very important for health, there are many studies on the linear attenuation coefficient of different materials in the literature. This includes building materials [2][3][4][5], alloys [6,7] and also compounds [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Radiation Transmission of Some Gypsum Concretes for 511, 835 and 1275 keV Gamma Rays

Günoğlu

2017

Acta Phys. Pol. A

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Although gypsum is one of synthetic building materials, nowadays it is used as interior coating of walls and ceilings of buildings. Thus, measurement of its radiation shielding properties is vital. The gamma attenuation coefficients of gypsum concretes, produced using different rates of boron, have been measured at gamma energies of 511, 835 and 1275 keV. The measured results have been compared with the results of calculation, which has been done using XCOM computer code. The obtained results have also been compared with the normal type of gypsum concrete, in order to see the effect of boron on the radiation shielding properties.

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Flexural strength increases of concrete with AISI 4140 steel chip as reinforcement

Vega-Pava,

Torrente-Prato

2023

Construction and Building Materials

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Shielding of Gamma Radiation by Using Porous Materials

Cited by 10 publications

References 8 publications

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

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

Radiation Transmission of Some Gypsum Concretes for 511, 835 and 1275 keV Gamma Rays

Flexural strength increases of concrete with AISI 4140 steel chip as reinforcement

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