The buildup factor calculations of concrete with different proportions of CRT based on a BP neural network by MCNP. Issue 6 (3rd June 2021)

• Record Type: Journal Article
• Authors: Gao, Han; Li, Xiang; Li, Zhanpeng; Wang, Yidi; Gao, Yunan; Tang, Wei; Chen, Long; Yan, Congchong; Tu, Yu; Sun, Liang
• Is Part Of: Journal of nuclear science and technology. Volume 58:Issue 6(2021)
• Page Start: 667
• Page End: 675
• Publication Date: 2021-06-03
• Subjects: CRT concrete -- MCNP -- buildup factor -- BP neural network -- Monte Carlo; Nuclear engineering -- Periodicals; Nuclear physics -- Periodicals; Nuclear energy -- Periodicals; 621.4805
• DOI: 10.1080/00223131.2020.1856733
• Languages: English
• ISSNs: 0022-3131

Abstract

ABSTRACT: Cathode ray tubes (CRTs) are glass materials that contain harmful heavy metal elements such as lead . CRTs have been widely used in electronic products sets for decades. At present, these discarded CRTs endanger the environment. Because the high-density heavy metal elements in CRTs have a good shielding effect for X/γ rays, these raw materials have become a good choice to produce radiation-proof concrete. In the selection and use of shielding materials, the buildup factor must be considered. Monte Carlo code MCNP is used to calculate the exposure buildup factor (EBF) of concrete with different proportions of CRT fragments in the photon energy range of 0.015–15 MeV for shielding thicknesses of up to 20 mean free paths (mfp). Back-propagation (BP) neural network is proposed to predict the EBF, and the prediction effect is evaluated, reliability of this method is verified; the average error is 3.4%, and the maximum error is 9.1%. Using this method, the EBF of concrete with an arbitrary proportion of CRTs, any energy level and any shielding thickness can be quickly obtained in the ranges of the parameters of the neural network training set (0.015–15 MeV and 0–20 mfp in this paper).