The use of ionising radiation to image nuclear fuel: A review

Parker, Helen M. O'd.; Joyce, Malcolm J.

doi:10.1016/j.pnucene.2015.06.006

Cited by 36 publications

(9 citation statements)

References 84 publications

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“…Due to the increasing use of nuclear technology for a wide range of applications such as treatment of cancer disease, thermal energy production, imaging nuclear fuel, etc. [3][4][5][6] and the associated health hazards that this technology entails, the evaluation of a construction material against nuclear radiation is vital [7].…”

Section: Introductionmentioning

confidence: 99%

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

et al. 2022

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Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

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Section: Introductionmentioning

confidence: 99%

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

et al. 2022

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“…Major applications of nuclear technology include diagnosis and treatment of a variety of diseases, generation of electricity, archaeology, pollution mitigation, etc. [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Nuclear technology uses different radioactive rays, such as gamma rays, X-rays, and neutrons, that have the potential to cause serious health and environmental problems [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches

et al. 2022

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This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.

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“…Since the discovery of x-ray by Roentgen in 1895 and the radioactivity by Henri Becquerel in 1896, the radiation applications such as nuclear power, medical imaging, cancer treatment and nuclear engineering are dramatically increased over the past decades [1][2][3]. The improper use of radiation can lead to serious injuries.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Shielding Effectiveness of Tellurite Glass with Composition 85TeO2-5Nb2O5-5ZnO-5Ag2O for Diagnostic Radiology Application

Hussein

2021

CJAST

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The metal oxide glasses have attracted huge interest as promising types of shielding materials to replace the toxic, heavy and costly conventional shielding materials. In this work, the physical and the shielding effectiveness of Tellurite glass sample (S1) contain host metal oxides (85TeO2-5Nb2O5-5ZnO-5Ag2O) were evaluatedatphoton energies range between 15keVand1MeV.The shielding parameters of the proposed glass system such as linear attenuation coefficients, HVL, MFP, Zeff, and Neffwere evaluated. The proposed samples showed a superior performance at the diagnostic energy range between 40 and 90 keV and a comparable shielding effectiveness above 90keV when compared with other commercial standard shielding materials.

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The use of ionising radiation to image nuclear fuel: A review

Cited by 36 publications

References 84 publications

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete

Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches

An Evaluation of the Shielding Effectiveness of Tellurite Glass with Composition 85TeO2-5Nb2O5-5ZnO-5Ag2O for Diagnostic Radiology Application

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