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2013 | 123 | 2 | 171-172
Article title

Prediction Primary Radiation Shielding Wall Thickness with Artificial Neural Networks

Content
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Languages of publication
EN
Abstracts
EN
In this study, wall thickness for using in primary radiation shielding was determined in different energy ranges using tenth value layer by artificial neural networks. Radiation energy values, tenth value layers and negative logarithm of transmission factor (n) were selected as input parameters and wall shielding thickness values selected as output parameters. Consequently, developed artificial neural networks model outputs were compared with experimental results and it was seen that the results were harmonious.
Keywords
EN
Year
Volume
123
Issue
2
Pages
171-172
Physical description
Dates
published
2013-02
References
  • [1] National Council on Radiation Protection and Measurements, Structural Shielding Design and Evaluation for Megavoltage X- and Gamma-Ray Radiotherapy Facilities, Report 151, J. Radiol. Prot. 26, 349 (2006)
  • [2] D. Sayala, US Radiology 1, 87 (2008)
  • [3] J.P. Biggs, in: Radiation Shielding for Megavoltage Photon Therapy Machines 52nd Annual Meeting, AAPM, Philadelphia 2010, p. 5
  • [4] E. Öztemel, Artificial Neural Networks, Papatya Publishing, Istanbul 2003
  • [5] Ö. Terzi, S. Önal, Afr. J. Agricult. Res. 7, 1317 (2012)
Document Type
Publication order reference
YADDA identifier
bwmeta1.element.bwnjournal-article-appv123n2002kz
Identifiers
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