Research Article  Open Access
Chao Li, Yingming Song, Zehuan Zhang, Jie Mao, Weiwei Yuan, Bo Wang, "A Novel and HighPrecision Method for Calculating the γRay BuildUp Factor for Multilayer Shields", Science and Technology of Nuclear Installations, vol. 2021, Article ID 8860762, 15 pages, 2021. https://doi.org/10.1155/2021/8860762
A Novel and HighPrecision Method for Calculating the γRay BuildUp Factor for Multilayer Shields
Abstract
In the field of radiation protection, the pointkernel code method is a practical tool widely used in the calculation of 3D radiation field, and the accuracy of the pointkernel integration method strongly depends on the accuracy of the buildup factor. It is well known that calculation of the buildup factor for singlelayer shields is composed of single material, but it is very complicated to calculate the buildup factor for multilayer shields (MLBUF). Recently, a novel and highprecision method based on the deep neural network (DNN) for calculating MLBUF has been proposed. In this paper, the novel method is described completely by slab models. Through the study of photon transport in multilayer shields, the parameters that mainly affect the calculation of buildup factor are analyzed. These parameters are trained by DNN as the input vectors, and the buildup factor for multilayer shields is predicted based on the trained DNN. The results predicted by DNN confirm that the method can calculate the buildup factor for multilayer shields quickly and accurately. The method has been preliminarily applicated into a 3D radiation field calculation software, and it has proved that the method for calculating MLBUF has a broad application prospects in 3D radiation field calculation.
1. Introduction
To protect radiation protection personnel from the radiation harm, it is often necessary to know the radiation situation of the working environment in advance and calculate the distribution of 3D radiation field quickly and accurately. At present, the main methods for calculating the 3D radiation field are determination method, Monte Carlo method, and pointkernel method. Due to the fast calculation speed and being suitable to solve deep penetration problem, the pointkernel method [1, 2] shown in formula (1) is usually used in the calculation of 3D radiation field, such as QAD [2], MICROSHEILD [3], MERCURE [4], and Monte Carlo coupling pointkernel method [5]. Although the pointkernel method can apply in 3D radiation calculation at some extent, the radiation filed calculation results are highly relied on the estimated buildup factor [6, 7], and the error of calculation results brought by estimated buildup factor is relatively large, and calculation accuracy cannot satisfy the higher and higher accuracy requirement of practical engineering. This work is aimed to improve calculation accuracy of the buildup factor and ensure the accuracy of 3D radiation field calculation in practical engineering application.where is the dose rate at the reference point , is the conversion factor of photon flux rate to dose rate, is the source activity of specified energy located at , d is the piercing path length along , is the buildup factor, and is the linear absorption coefficient.
A lot of efforts have been done to improve the accuracy calculation of buildup factors. The methods of calculating buildup factor for singlelayer shields have been very mature, and the empirical formulas, such as Taylor formula [8] shown in formula (2), interpolation method based on ANSI/ANS6.4.3 [9] database, and GP fitting formula [10] shown in (3) and (4) are usually used to calculate the buildup factor for singlelayer shields.where E is the energy of gammaray, μ is the mass attenuation coefficient of shielding, and , and are the functions of energy, and they are related to materials.where E is the energy of photon, is the mean free path of photon, B is buildup factor, and b, c, a, and are the parameters of the GP fitting formula.
While for multilayer shields, whose each layer is composed of different materials or combinations of different materials, the transport process of photons in the multilayer shields is relatively complicated, and it is difficult to calculate the buildup factor with simple empirical formulas. In early stage, due to the limitation of computer technology, the calculation of buildup factor for muiltlayer shields mainly focused on empirical formulas fitting, mainly including Kalos formula [11], Bowman and Trubey formula [12], Broder formula [13], Burke and Beck formula [14], and Lin and Jiang formula [15]. In addition, a useful and free online platform called PyMLBUF [16] for calculating the buildup factor is available. It includes the most of empirical formulas, and it can select different empirical formulas to calculate fastly the buildup factor on the basis of requirement. With the rapid development of computer science and technology, the researchers proposed the iterative algorithm [17] and support vector regression (SVR) [18] method to achieve the accurate calculation of buildup factors for muiltlayer shields. Although the methods mentioned above achieved some good results, it cannot satisfy today’s requirement in practical engineering application. For empirical and semiempirical, the main problems are the necessity of defining the fitting function specially and a large number of correction factors are often added in the final empirical formula, which are usually not fully explained and inadequately defined [18]. As for the SVR and iterative method, their calculation accuracy was seem not far from acceptable, but they cannot satisfy the higher and higher accuracy requirement of practical engineering application. Therefore, how to calculate more accurately the buildup factor for multilayer shields has been an urgent problem in calculation of radiation field.
Recently, a novel and highprecision method for calculating the γray buildup factor for multilayer shields has been proposed in this paper. The method is based on the deep neural network (DNN) [19, 20], which is a kind of machine learning. In this paper, the main works are to obtain the appropriate parameters that are related to the buildup factor calculation and construct an approximate deep neural network which calculates the buildup factor for multilayer shields by learning the relationship between parameters and the value of buildup factor. The methods not only can calculate a large number of buildup factors at one time but also its calculation accuracy is relatively high, which can meet the accuracy requirements of pointkernel code in the 3D radiation field calculation and be applied into the practical engineering application.
In Section 2, it is a general description about the definition of buildup factor and introduction of some common calculation methods for MLBUF, and the novel and highprecision method is completely introduced in this section. Results and discussion are given in Section 3, and the method is validated by the MCNP, and a preliminary application is also illustrated. At the last, there is a conclusion about this work in Section 4.
2. Materials and Methods
2.1. BuildUp Factor
It is well known that buildup factor is a physical quantity describing the effect of scattered photons which makes the amount of received photons increase. Generally speaking, it refers to the ratio of the actual value of radiation quantities to the radiation quantities caused by the γray, which is generated by the radiation source and does not react with the shields at the point of interest in the absorbed material. Buildup factors may refer to a number of various quantities. The physical quantities commonly used in radiation protection include flux, fluence, exposure, and dose, and the counterparts of buildup factors are flux buildup factor, fluence buildup factor, exposure buildup factor, and dose buildup factor. In the 3D radiation field, the dose received by the staff is the key physical quantity to measure the radiation injury. Therefore, the work in this paper mainly considers the dose buildup factor among these buildup factors. The dose buildup factor is the ratio of the actual dose at the point of interest to the dose produced by the γray that does not interact with the material [21].
The methods for calculating the buildup factor for singlelayer shields are well known. The methods mainly include analytical expression such as Taylor formula, Berger formula, and GP fitting, tabulated form such as ANSI/ans6.4.3 database. Compared with singlelayer shields, the calculation of MLBUF is much more complicated, which not only depends on the characteristics of the current layer but also on the characteristics of previously penetrated layers. The early research studies on the calculation of MLBUF mainly focus on empirical formulas, and the empirical formula established by Broder and his collaborators is commonly used, as shown in formula (2) [13]. Lin and Jiang proposed an empirical formula for the calculation of MLBUF, which is also one of the widely used empirical formulas as shown in formula (3) [15].where N is the number of shielding layers, B is the buildup factor of Nlayer heterogeneous shielding media, and B_{n} is the buildup factor of homogeneous medium composed of nth shielding material.where B_{n} is the buildup factor of nth shielding, X_{n} is the thickness of nth shielding, and K is shown in formula (8). If the high Z material is in front of the low Z material, the calculation of C is shown in formula (9), otherwise shown in formula (10) as follows:
Although these empirical formulas can obtain good results at some extent in calculating the buildup factor for doublelayer to quadruplelayer shields, the calculation by these empirical formulas needs some complicated parameters, which increases the complexity of calculation, and the range of application is limited and cannot meet the actual engineering application.
Suteau and Chiron introduced an iterative algorithm to calculate the buildup factor for multilayer shields in 2005 [17]. The algorithm is based on the empirical formula of doublelayer shield buildup factor. In each iteration process, a single equivalent layer is used to replace the first two layers of shield materials, thus converting nlayer shields into (n − 1) layer shields. A large number of samples are trained to determine the atomic number and thickness of a single equivalent shield layer. Through a number of iterations, nlayer shields are transformed into doublelayer shields, and the buildup factor is calculated by empirical formula of doublelayer shield buildup factor.
The support vector regression method was proposed by Trontl et al. [18], which also transforms nlayer shields into (n − 1) layer through the iterative algorithm. Different from Suteau and Chiron’s iterative method, the equivalent shield layer in each iteration process is calculated by support vector regression. The input vector space of SVR for singlelayer and doublelayer shields is obtained by the Monte Carlo method and SAS3 (Shield Analysis Sequence No.3), and the input vectors of SVR are the equivalent buildup factor of N layer (1,2, ..., N) in front of the shield layer and the buildup factor of the shield in the nth layer, the homogenized effective atomic number of (1, 2, ..., N − 1) and the atomic number of the nth shield material, the equivalent shield thickness of the nlayer (1, 2, ..., N − 1) in front of the nth shield layer and the shield thickness of the nth layer, and the incident photon energy. Finally, the buildup factor for multilayer shields is calculated by LIBSVM. According to Trontl’s work, the relative average deviation (RAD) for the buildup factor of doublelayer shields calculated by the support vector method is 9.7%, and the maximum relative absolute deviation is 68.77%, and RADs for triplelayer and quadruplelayer shields were 24.3% and 29.7%, respectively [18, 22].
Through the introduction of several common calculation methods of buildup factor for multilayer shields, it is found that although these methods can obtain good results and solve the problem of buildup factor calculation to a certain extent, the input vector space of the iterative method and support vector machine are incident photon energy, atomic number of shield material, shield thickness, and buildup factor for each shield, and the accuracy of the buildup factor calculated in this way cannot satisfy the requirements of the correction pointkernel code. In this paper, some new parameters are proposed as input vectors by studying the photon transport in the shields. The new parameters can effectively improve the calculation accuracy of buildup factor by training these parameters based on DNN.
2.2. Deep Neural Network
The deep neural network (DNN) is a kind of machine learning, which can be understood as a neural network with many hidden layers, and it is also well known as deep feedforward network or multilayer perceptron. The structure of the deep neural network is divided into input layer, hidden layers, and output layer. The layers are fully connected, and any neuron in i layer must be connected with any neuron in i + 1 layer. The method based on DNN includes the following steps: the extraction of characteristics and features, procession of the features, internal training of samples, crossvalidation, external test of samples, and so on. The structure of DNN applied in the method is shown in Figure 1.
2.3. Input Vectors
The input vectors are given by a number of parameters defining the physical problem that is to be modelled using the DNN.
2.3.1. Cross Section
To make the DNN learn characteristics of parameters of buildup factor more richly, the transport process of photons in the shield is analyzed in this paper. When γray incidents on a shield, depending on shielding material (characterized by density and section of photoelectric effect, Compton scattering, and electronpositron effect) and photon energy, there exists a certain probability that the electromagnetic fields of photons interact with the electromagnetic fields of atoms, atomic electrons, nuclei, and charged particles. In the process of interaction, photons may be absorbed, and the whole energy of photons will be converted into other forms of energy. The scattering may occur and part of the photon energy will be absorbed, and its propagation direction will be changed, or the photon energy will remain unchanged, and only its propagation direction will be changed [23]. Any of the interaction process may result in secondary photons that have a probability of reaching the point of interest, thus increasing the flux, influence, or dose. In the process of interaction, the dominant interactions are photoelectric effect, Compton scattering, and electronpositron effect.
The photoelectric effect is the interaction of photons with bound electrons in matter, and the full energy of the photon is absorbed by a bound electron so that the kinetic energy of the electron is enough to get rid of the bondage of the atomic nucleus and emit from the inside of the atom, while the photon itself disappears. When the energy of the incident photon is high (above 1.022 MeV) and it passes by the nucleus, the incident photon may be transformed into a positron and a negative electron under the action of the nuclear Coulomb field. The reaction is called electronpositron effect. The incident photon collides with the material atom, and part of the photon energy is transferred to the external electron of the atom. The electron obtains kinetic energy and gets rid of the atomic bondage and becomes a recoil electron. The newly generated scattering photon deviates from the original direction, and its energy decreases.
When the energy of incident photon is relatively low, photoelectric effect dominates and then Compton scattering. While the energy of the incident photon is relatively high, the electron pair effect dominates. All matters obey this law. The probability of the three interactions between photons and materials is related to their cross section: cross section of photoelectric effect, Compton scattering, and electronpositron effect. According to the analysis, it can be deduced the three kinds of cross sections of interaction will directly affect the probability that the photons interact with the materials and affect the value of buildup factor. Therefore, using three kinds of crosssections as input vector parameters can well reflect the physical process of photon transport.
2.3.2. Energy of Incident Photons
The energy of incident photons also has a great influence on the calculation for buildup factor. When the energy of the incident photon is different, the corresponding three crosssections for the same material are different and the buildup factor is also different. For the same shield, the buildup factor decreases with the increase in energy. The main reason is that the scattering cross section decreases with the increase in photon energy, which leads to the decrease in photon scattering.
2.3.3. Number of Mean Free Path
The number of mean free path (MFP) is an important parameter affecting the buildup factor, which is the ratio of photon penetration distance to mean free path. In shielding design, the thicker the shield is, the greater the number of the free path of photons in the shielding is, and the times of scattering are also more.
2.3.4. Density of Shield Material
The density of shield material is also one of the parameters affecting the calculation of buildup factor. The density of material is related to cross section in some way. For example, the quotient of density and cross section of shield material is the mass attenuation coefficient, which is the share of particles reduced by interaction after the charged particles penetrate through the unit mass thickness. The mass energy decreased coefficient and mass energy absorption coefficient are in the same way. In addition, the density can also be used as a representative of some material to distinguish the material through which photons penetrate for engineering application.
In conclusion, the input vector parameters of DNN include incident photon energy, material density of each layer, shield free path number of each layer, Compton scattering cross section of each layer, electronpositron effect cross section of each layer, and photoelectric effect cross section of each layer. The input vectors and output of DNN are shown in Table 1.

2.4. MLBUF Calculation
According to the determined input vectors of DNN, the appropriate structure of DNN used to calculate the buildup factors is constructed. The DNN trains the sample data of buildup factor. By continuously adjusting the relevant parameters of DNN learning, the error of training set and verification set can meet the requirement of calculation accuracy of buildup factor. To ensure that the DNN learning does not appear overfitting and underfitting, the error of buildup factor of calculation training set and verification set is reduced as far as possible. The main flow chart of the buildup factor calculation method based on DNN is shown in Figure 2.
2.4.1. Learning Sample Data Generation
The Monte Carlo method has been used to generate the reference data points. According to the determined parameters to be learned, N groups of different shield models are established. Then, MCNP input files with different energy, different shield thickness, and different shield material combinations are generated in batch. The samples are calculated by MCNP program, and then the dose considered the scattering photons calculated by MCNP, and D_{1} unconsidered scattering photons are extracted in batches, and the corresponding buildup factors are calculated. After preprocessing the learning sample data, the learning sample data are divided into input items and corresponding output items.
2.4.2. Determine the Structure of DNN
The topological structure of the DNN is determined according to the number of input parameters and the number of output parameters. Considering the complexity of the practical shielding problem, the structure of DNN has the following guiding principles:(a)For complicated engineering problems, the hidden layers of the neural network should adopt doublelayer neurons as much as possible.(b)In the singlelayer hidden layer neural network, the structure of the neuron number of the whole neural network is recommended as follows:(c)In the doublelayer hidden layer neural network, the structure of the neuron number of the whole neural network is recommended as follows:
According to the characteristics of buildup factor for multilayer shield calculation parameters, this paper adopts the following parameters of DNN to train the DNN:(1)The deep neural network is composed of input layer, three hidden layers, and output layer(2)The number of neurons in each layer is auto, 50, 80, 50, and auto(3)Relu is selected as the activation function in the input layer and hidden layers, and linear is used as the activation function in the output layer(4)SGD + momentum is adopted as the activation function as a training method, and the minibatch parameters are set to 512(5)The ratio of training set, verification set, and test set is 9 : 1 : 1
2.4.3. Training and Prediction
The DNN is used to train the samples data. By adjusting the training parameters of the DNN, the relative average deviation of the training set and validation set is less than the setting accuracy or the iteration finished, and the DNN ends the training.
After the DNN finished the training, by inputting the input vectors (incident photon energy, material density of each layer, number of mean free path of each layer, Compton scattering cross section of each layer, electronpositron effect cross section of each layer, and photoelectric effect cross section of each layer), the buildup factor can be predicted quickly and accurately.
3. Results and Discussion
3.1. The Results
In this paper, the slab model and a pointcollimated source are used as the calculation model of buildup factor, and the buildup factor for singlelayer, doublelayer, triplelayer, and quadruplelayer slab model is calculated, respectively. The geometry models of the four are shown in Figure 3, and the variation range of shield models is shown in Table 2.

By MCNP modelling, the problem of calculating the buildup factor is transformed into solving the ratio of the dose penetrated shields and the dose without considering the scattering. A number of MCNP input files are generated by random uniform sampling in the variation range of the shield layer. In this paper, the number of MCNP input files for singlelayer, doublelayer, triplelayer, and quadruplelayer slab model is 3900, 7800, 15600, and 31200, respectively. The buildup factor of each shield combination (each layer comprised of different material or a combination of materials) under different shields, different number of mean free path, and different incident photon energy is calculated.
The sample data are preprocessed for the DNN training. Then, the DNN trained the sample data, and the training iteration time of training is set to 100000. The DNN stops training until the validation set error converges to the ideal level. The mean absolute percentage error (MAPE) of training set and validation set is used as appraisal to the prediction, and the MAPE charts of the training process for singlelayer, doublelayer, triplelayer, and quadruplelayer slab model are shown in Figure 4. When the validation set error converges to the ideal level, the DNN stopped training, and the trained DNN was used to predict the buildup factor.
(a)
(b)
(c)
(d)
3.2. The Regression Analysis and the Relative Average Deviation
The quality of the prediction was tested on all data (including training set, validation set, and test set), using relative average deviation (RAD) as a quality measure:where is the predicted value corresponding to the target value .
The regression analysis on all data of training set, validation set, and test set has been done in this paper, and the results of singlelayer shields, doublelayer shields, triplelayer shields, and quadruplelayer shields are shown in Figure 5. According to the regression analysis chart, the DNN can well fit the parameters of the buildup factor, and there is no overfitting or underfitting. The relative deviation between the predicted value and the actual value calculated by MCNP is small and consistent.
(a)
(b)
(c)
(d)
For analysing the relative error between the predicted value and the target value, this paper has drawn the distribution of relative error of singlelayer shields, doublelayer shields, triplelayer shields, and quadruplelayer shields. As it is shown in Figure 6, the relative errors between all predicted values and actual values are mostly between −10% and 10%. It can be educed that the deep neural network is suitable for calculating the buildup factor. The RAD of different media of shielding is calculated, and the partial RAD results are shown in Table 3.
(a)
(b)
(c)
(d)

According to the results of comparison between predicted value and actual value, the relative average deviation observed for singlelayer shields is 2.05%, with the maximum of 19.31% detected for a 0.141 mfp thick aluminum shield at 10 MeV incident γray energy( and ). Only 0.57% of the predicted data points have the relative absolute deviation higher than 10%.
The RAD observed for doublelayer shields is 2.87%. Maximum relative absolute deviation of 31.37% was observed for a shield composed of 0.199 mfp of iron and 0.026 mfp of aluminum, at 0.8 MeV γray energy (and). 2.05% of the predicted data points have the relative absolute deviation higher than 10%.
The RAD observed for triplelayer shields is 3.13%. Maximum relative absolute deviation of 32.50% was detected for a shield made of 0.078 mfp thick layer of iron with 0.712 mfp thick layer of aluminum followed by 0.061 mfp thick layer of iron at 0.6 MeV incident γray energy ( and ). Only 3.25% of predicted data points have the relative absolute deviation higher than 10%.
The RAD for quadruplelayer shields is 3.06%. Maximum relative absolute deviation of 32.04% was detected for a shield comprised of 2.259 mfp of aluminum, 1.689 mfp of iron, 2.137 mfp of aluminum, and 0.034 mfp of lead at 0.5 MeV γray energy ( and ). Only 2.85% of predicted data point have the relative absolute deviation higher than 10%.
For validating the method, we reestablish an infinite homogeneous medium of the doublelayer shielding model with a pointisotropic source and use the constructed DNN to train the MCNP sample data. This paper takes the MCNP value as reference value and compares the calculated results of DNN and PyMLBUF with MCNP results at 3 MeV, respectively. The results of DNN at 3 MeV are calculated by the trained DNN, and the results of PyMLBUF are calculated by the PyMLBUF online platform. The calculation results of MCNP, DNN, and PyMLBUF and the deviation of DNN results and PyMLBUF results compared with the MCNP results are all shown in Table 4. According to Table 4, the RAD is 4.83% and the maximum relative absolute deviation is 18.39% for DNN and the RAD is 6.97% and maximum relative absolute deviation is 18.60% for PyMLBUF.

3.3. The Comparison of Actual Target and Predicted Target
In this paper, the data (including training set, validation set, and test set) are been predicted by the trained DNN, and the partial predicted results of buildup factor for singlelayer shields and stratified shields are showed in Figures 7 and 8. It can be seen that the curves of the predicted target and the actual target are almost identical, and the relative deviation is very small. The DNN constructed in this paper can fit the parameters of the buildup factor very well and calculate the corresponding buildup factor value successfully.
(a)
(b)
(c)
(d)
(a)
(b)
(c)
(d)
3.4. Application
The method has been preliminarily used in our 3D radiation field calculation software called VMERAS as a sub module. This paper uses 4 simplified nuclear fuel waste barrels as the calculation model to calculate its 3D radiation field, where #1 barrel is regarded as the source and the other barrels as shielding. Figure 9 is the CAD model display of the calculation model, and Figure 10 is the visualization of the calculated radiation field in VMERAS. Figure 11 is the comparison of partial calculated dose rate between MCNP and VMERAS. It can been clearly seen that the deviation between VMERAS and MCNP is very small, and it proved that the novel and highprecision method for calculating MLBUF has the ability to apply into the practical engineering.
4. Conclusion
In this paper, a novel and highprecision method for calculating the γray buildup factor for multilayer shields is introduced. Comparing to the previous approaches, the biggest characteristics of the method are deep neural network and new parameters of the buildup factor calculation. Through the deep neural network constructed in this paper training the new parameters, the MLBUF can be calculated without decoupling the complex physical relationship between input and output. Once the deep neural network finishes the training, it can be used to predict a number of buildup factors in a short time. By illustrating the preliminary application case, it is proved that the novel and highprecision method has the ability to apply into the 3D radiation field calculation program and it has a broad application prospect.
From the above discussion about the predicted results, it can be seen that the accuracy of the predicted buildup factor is very high and even the maximum deviation does not seem far from acceptable. The method can meet the accuracy requirement of pointkernel code correction in the calculation of 3D radiation field and satisfy the requirement of calculation speed. Therefore, the method for calculating buildup factors is feasible in engineering practice.
Data Availability
The data used to support the conclusions of this paper are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work was financially supported by the National Exemplary Base for International Science and Technology (grant no. 2018SYS04).
References
 I. M. Prokhorets, “Pointkernel method for radiation fields simulation problems of atomic science & technology,” Voprosy Atomnoj Nauki I Techniki = Pytannja Atomnoï Nauky I Techniky = Problems of Atomic Science and Technology, vol. 48, no. 5, pp. 106–109, 2007. View at: Google Scholar
 V. R. Cain and C. G. Qad, “A combinational geometry version of QADP5A,a point kernel code for neutron and gammaray shielding calculations,” Tech. Rep., Radiation Shielding Information Center, Oak Ridge, TN, USA, 1977, Report ORNI–CCC307. View at: Google Scholar
 Grove Software Inc, MIROSHIELD® User’s Manual, Lynchburg, VA, USA, 2009.
 C. Dupont and J. C. Nimai, “MERCURE4: a threedimensional Monte Carlo program for the integration of straightline attenuation point kernels,” Tech. Rep., International Atomic Energy Agency (IAEA), Vienna, Austria, 1980, Report OLS SLSN82116. View at: Google Scholar
 Y. Guo, Y. Song, C. Lu, M. Fu, and Z. Zhang, “Monte calropoint kernel method for calculating the radiation field in decommissioning nuclear facilities,” Nuclear Science and Engineering, vol. 38, no. 6, pp. 1002–1007, 2018, in Chinese. View at: Google Scholar
 G. R. White, “The penetration and diffusion of Co60 Gammarays in water using spherical geometry,” Physical Review, vol. 80, no. 2, pp. 154–156, 1950. View at: Publisher Site  Google Scholar
 Y. Harima, S.i. Tanaka, Y. Sakamoto, and H. Hirayama, “Development of new gammaray buildup factor and application to shielding calculations,” Journal of Nuclear Science and Technology, vol. 28, no. 1, pp. 74–84, 1991. View at: Publisher Site  Google Scholar
 J. J. Taylor, Application of Gamma Ray BuildUp Data to Shield Design, Westinghouse Electric Company, Pittsburgh, PA, USA, 1954.
 ANSI/ANS6.4.3, Gamma Ray Attenuation Coefficient and Buildup Factors for Engineering Materials, La Grange Park in Illinois, American Nuclear Society, La Grange Park, IL, USA, 1991.
 Y. Harima, Y. Sakamoto, S. Tanaka, and M. Kawai, “Validity of the geometricprogression formula in approximating gammaray buildup factors,” Nuclear Science and Engineering, vol. 94, no. 1, pp. 24–35, 1986. View at: Publisher Site  Google Scholar
 M. H. Kalos, “Nuclear Development Associates (NDA),” 1956. View at: Google Scholar
 L. A. Bowman, D. K. Trubey, Bowman, and Trubey, “Oak ridge national laboratory, x–822,” 1958. View at: Google Scholar
 D. L. Broder, Y. P. Kayurin, and A. A. Kutuzov, “Transmission of gamma radiation through heterogeneous media,” The Soviet Journal of Atomic Energy, vol. 12, no. 1, pp. 26–31, 1962. View at: Publisher Site  Google Scholar
 G. d. P. Burke and H. L. Beck, “Calculated and measured dose buildup factors for gamma rays penetrating multilayered slabs,” Nuclear Science and Engineering, vol. 53, no. 1, pp. 109–112, 1974. View at: Publisher Site  Google Scholar
 U.T. Lin and S.H. Jiang, “A dedicated empirical formula for γray buildup factors for a point isotropic source in stratified shields,” Radiation Physics and Chemistry, vol. 48, no. 4, pp. 389–401, 1996. View at: Publisher Site  Google Scholar
 K. Mann and S. S. Mann, “Development of an onlineplatform for gammaray shielding calculations and investigations,” Annals of Nuclear Energy, vol. 150, Article ID 107845, pp. 1–22, 2021. View at: Google Scholar
 C. Suteau and M. Chiron, “An iterative method for calculating gammaray buildup factors in multilayer shields,” Radiation Protection Dosimetry, vol. 116, no. 14, pp. 489–492, 2005. View at: Publisher Site  Google Scholar
 K. Trontl, T. Šmuc, and D. Pevec, “Support vector regression model for the estimation of γray buildup factors for multilayer shields,” Annals of Nuclear Energy, vol. 34, no. 12, pp. 939–952, 2007. View at: Publisher Site  Google Scholar
 G. E. Hinton and R. Salakhutdinov, “Reducing the dimensionality of data with neural network,” Science, vol. 313, no. 5789, pp. 504–507, 2006. View at: Publisher Site  Google Scholar
 L. Ma, F. JuefeiXu, F. Zhang et al., “Deep gauge: multigranularity testing criteria for deep learning systems, automated software engineering,” pp. 120–131, 2018, https://arxiv.org/abs/1803.07519. View at: Google Scholar
 J. Fang, Introduction of Radiation Protection, China Atomic Energy Press, Beijing, China, 1991, in Chinese.
 P. Duckic, K. Trontl, and M. Matijevic, “Active Learning for Support Vector Regression in Radiation Shielding design,” in Proceedings of the International Conference on High Performance Computing & Simulation, pp. 311–317, IEEE, Barcelona, Spain, July 2015. View at: Google Scholar
 Z. Tian, Radiation Dosimetry, China Atomic Energy Press, Beijing, China, 1992, in Chinese.
Copyright
Copyright © 2021 Chao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.