The Raw Building Materials’ Research Quantitative Characteristics Radioactivity in Volgograd Region

Abstract:

The paper attempts to structure the raw materials for radioactivity using Kohonen self-organizing maps. The analysis was carried out on the data on the specific activities of natural radionuclides in the construction raw materials in the Volgograd region, as well as on the natural radionuclides specific effective activities calculated values. Unlike the traditional methods of analysis, the used methodology for assessing radioactivity is based on quantitative characteristics. As a result of the network training on data on the raw materials specific activities, a cluster map with segmentation by the natural radionuclides effective specific activity has been obtained. Based on the results obtained, the conclusions about the possibility and feasibility of using the algorithm used for the classification and analysis of data on the building materials radioactivity have been made.

• Periodical: Materials Science Forum (Volume 974)
• Pages: 342-349
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.974.342
• Online since: December 2019
• Authors: Ilya P. Mikhnev, Svetlana V. Mikhneva, Natalia A. Salnikova*, Lev B. Lempert
• Keywords: Building Materials Radioactivity, Effective Specific Activity, Gamma Spectrometry Analysis, Natural Radionuclides, Neural Networks, Radionuclide Spectrometry, Self-Organizing Map

References

[1] Valery A. Kamaev, Ilya P. Mikhnev, and Natalia A. Salnikova, Natural Radionuclides as a Source of Background Irradiation Affecting People Inside Buildings, Procedia Engineering. 150 (2016) 1663-1672.

DOI: 10.1016/j.proeng.2016.07.148

[2] I.P. Mikhnev, N.A. Salnikova, and M.B. Lempert, Modern Condition of Dose Loads from Construction Materials and Main Sources of Ionizing Impact on the Population of the Volgograd Region, Materials and Technologies in Construction and Architecture, Materials Science Forum. 931 (2018) 1007-1012.

DOI: 10.4028/www.scientific.net/msf.931.1007

[3] Alla Kravets, Olga Poplavskaya, Lev Lempert, Natalia Salnikova, and Irina Medintseva, The Development of Medical Diagnostics Module for Psychotherapeutic Practice, In: Communications in Computer and Information Science (CIT&DS 2017), Volgograd. 754 (2017) 872-883.

DOI: 10.1007/978-3-319-65551-2_63

[4] I.P. Mikhnev, N.A. Salnikova, and M.В. Lempert, Research of Activity of Natural Radionuclides in Construction Raw Materials of the Volgograd Region, Solid State Phenomena. 265 (2017) 27-32.

DOI: 10.4028/www.scientific.net/ssp.265.27

[5] Alla Kravets, Nikita Shumeiko, Natalia Shcherbakova, Boris Lempert, and Natalia Salnikova, Smart Queue, Approach for New Technical Solutions Discovery in Patent Applications, In: Communications in Computer and Information Science (CIT&DS 2017), Volgograd, Russia. 754 (2017) 37-47.

DOI: 10.1007/978-3-319-65551-2_3

[6] Ilya P. Mikhnev, Natalia A. Salnikova, Mikhail В. Lempert, and Kirill Yu. Dmitrenko, The Biological Effects of Natural Radionuclides from the Construction Materials on the Population of the Volgograd Region, In: 8th International Conference on Information, Intelligence, Systems and Applications (IISA 2017). (2017) 1-6.

DOI: 10.1109/iisa.2017.8316428

[7] A.G. Kravets, S.S. Vasiliev, and D.V. Shabanov, Research of the LDA algorithm results for patents texts processing, In: 9th International Conference on Information, Intelligence, Systems and Applications (IISA 2018). (2019) 1-6.

DOI: 10.1109/iisa.2018.8633649

[8] Ilya P. Mikhnev, Svetlana V. Mikhneva, and Natalia A. Salnikova, Studies of radon activity in civil engineering and environmental objects, International Journal of Engineering and Technology. l (7) (2.23) (2018) 162-166.

DOI: 10.14419/ijet.v7i2.23.11907

[9] Valery A. Kamaev, Natalia A. Salnikova, Suleiman A. Akhmedov, and Anatoliy M. Likhter, The Formalized Representation of the Structures of Complex Technical Devices Using Context-Free Plex Grammars, In: Communications in Computer and Information Science (CIT&DS 2015).,Volgograd, Russia. 535 (2015) 268-277.

DOI: 10.1007/978-3-319-23766-4_22

[10] A.G. Kravets, M.A. Kanavina, and N.A. Salnikova, Development of an Integrated Method of Placement of Solar and Wind Energy Objects in the Lower Volga, In: International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). (2017) 1-5.

DOI: 10.1109/icieam.2017.8076263

[11] A.G. Kravets, A.D. Kravets, and A.A. Korotkov, Intelligent multi-agent systems generation, In: World Applied Sciences Journal. 24 (24) (2013) 98-104.

[12] I.P. Mikhnev, N.A. Salnikova, and S.V. Mikhneva, Effect of Thermal Treatment of Building Materials on Natural Radionuclides Effective Specific Activity, Materials Science Forum. 945 (2019) 30-35.

DOI: 10.4028/www.scientific.net/msf.945.30

[13] Deductor: Advanced analytics without programming. Information on http://basegroup.ru/deductor/description, last accessed 2019/05/20.

[14] BaseGroupLabs. Data Analysis Technologies. Information on http://basegroup.ru/, last accessed 2019/05/20.

[15] Ilya Mikhnev, Natalia Salnikova, and Svetlana Mikhneva, New Industrial Technologies and Innovations for the Production of Nanostructured Materials, Advances in Social Science, Education and Humanities Research. 240 (2019) 83-89.

DOI: 10.2991/sicni-18.2019.18

[16] A.G. Kravets, A. Gurtyakov, and A. Kravets, Corporate intellectual capital management: Learning environment method, Proceedings of the IADIS International Conference ICT, Society and Human Beings, Proceedings of the IADIS International Conference e-Commerce. (2013) 3-10.

[17] A.V. Golubev, M.V. Shcherbakov, N.L. Scherbakova, and V.A. Kamaev, Automatic multi-steps forecasting method for multi seasonal time series based on symbolic aggregate approximation and grid search approaches, In: Journal of Fundamental and Applied Sciences. 8 (3S) (2016) 2429-2441.

[18] I.P. Mikhnev, N.A. Salnikova, and S.V. Mikhneva, Digital technologies for searching and processing unstructured Information in modern higher education,, Advances in Economics, Business and Management Research. 81 (2019) 620-625.

DOI: 10.2991/mtde-19.2019.124

[19] A. Korotkov, A.G. Kravets, Y.F. Voronin, and A.D. Kravets, Simulation of the initial stages of software development, In: International Journal of Applied Engineering Research. 9 (22) (2014) 16957-16964.

[20] D.M. Korobkin, S.A. Fomenkov, and A.G. Kravets, Methods for extracting the descriptions of sci-tech effects and morphological features of technical systems from patents, In: 9th International Conference on Information, Intelligence, Systems and Applications (IISA 2018). (2019) 1-6.

DOI: 10.1109/iisa.2018.8633624

[21] P.Y. Simard, D. Steinkraus, and J.C. Platt, Best practices for convolutional neural networks applied to visual document analysis, IEEE Conference Publications. (2003) 958-963.

DOI: 10.1109/icdar.2003.1227801