June 22, 2024
Abbas Rezaei

Abbas Rezaei

Academic rank: Assistant professor
Education: Ph.D in Electrical engineering
Phone: 083-38305001
Faculty: Faculty ofٍٍ Electrical Engineering


Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis
Type Article
Researchers Ehsan Eftekhari zadeh، S.A.H. Feghhi، Gholam Hossein Roshani، Abbas Rezaei


Due to variation of neutron energy spectrum in the target sample during the activation process and to peak overlapping caused by the Compton effect with gamma radiations emitted from activated elements, which results in background changes and consequently complex gamma spectrum during the measurement process, quantitative analysis will ultimately be problematic. Since there is no simple analytical correlation between peaks’ counts with elements’ concentrations, an artificial neural network for analyzing spectra can be a helpful tool. This work describes a study on the application of a neural network to determine the percentages of cement elements (mainly Ca, Si, Al, and Fe) using the neutron capture delayed gamma-ray spectra of the substance emitted by the activated nuclei as patterns which were simulated via the Monte Carlo N-particle transport code, version 2.7. The Radial Basis Function (RBF) network is developed with four specific peaks related to Ca, Si, Al and Fe, which were extracted as inputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model, several structures have been constructed and tested. The comparison between simulated and predicted values using the proposed RBF model shows that there is a good agreement between them.