Prediction Primary Radiation Shielding Wall Thickness with Artificial Neural Networks

• Authors: A. Akkaş , C. Başyiğit , M. Necip Kurtarici
• 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

28.41.Qb; 84.35.+i

Discipline

• 84.35.+i: Neural networks(for optical neural networks, see 42.79.Ta; see also 07.05.Mh Neural networks, fuzzy logic, artificial intelligence in computers in experimental physics; 87.18.Sn in biological complexity)
• 28.41.Qb: Structural and shielding materials

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2013
• Volume: 123
• Issue: 2
• Pages: 171-172

Dates

published

2013-02

Contributors

author

A. Akkaş

• Suleyman Demirel University, Teknoloji Fak İmalat Müh., Isparta, Turkey

author

C. Başyiğit

• Suleyman Demirel University, Teknik Egt. Fak. Yapı Egt. Bol., Isparta, Turkey

author

M. Necip Kurtarici

• Suleyman Demirel University, Teknik Egt. Fak. Yapı Egt. Bol., Isparta, Turkey

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)

Identifiers

DOI

10.12693/APhysPolA.123.171