César Marques Salgado , Roos Sophia de Freitas Dam , William Luna Salgado , Claudio de Carvalho Conti , Julio Cezar Suita
{"title":"Prediction of chemical elements in cement using neutron activation analysis and artificial intelligence","authors":"César Marques Salgado , Roos Sophia de Freitas Dam , William Luna Salgado , Claudio de Carvalho Conti , Julio Cezar Suita","doi":"10.1016/j.radphyschem.2025.112699","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents a methodology for predicting the percentages of the key chemical elements (Calcium, Silicon, Aluminum, Iron and Oxygen) in Portland cement Class G samples. The approach integrates prompt gamma neutron activation analysis (PGNAA) and neutron activation analysis (NNA) and with artificial neural network (ANN) to enhance the characterization of these cement samples. A mathematical model was developed using the MCNP6 code to simulate both prompt and delayed gamma-ray emissions resulting from neutron activation in cement samples containing multiple elements. An <sup>241</sup>Am–Be neutron source was also simulated. To establish a relationship between the gamma radiation spectra from the neutron reactions and the elemental concentrations in the cement samples, a MultiLayer Perceptron (MLP) ANN was employed. This network, consisting of a hidden layer with three independent modules, was trained using the supervised error backpropagation algorithm. The results show exceptional accuracy in predicting the concentrations of the five main elements present in the cement, as well as its density, with an average relative error of less than 5 % for 97.92 % of the Validation test.</div></div>","PeriodicalId":20861,"journal":{"name":"Radiation Physics and Chemistry","volume":"232 ","pages":"Article 112699"},"PeriodicalIF":2.8000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiation Physics and Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0969806X25001914","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This study presents a methodology for predicting the percentages of the key chemical elements (Calcium, Silicon, Aluminum, Iron and Oxygen) in Portland cement Class G samples. The approach integrates prompt gamma neutron activation analysis (PGNAA) and neutron activation analysis (NNA) and with artificial neural network (ANN) to enhance the characterization of these cement samples. A mathematical model was developed using the MCNP6 code to simulate both prompt and delayed gamma-ray emissions resulting from neutron activation in cement samples containing multiple elements. An 241Am–Be neutron source was also simulated. To establish a relationship between the gamma radiation spectra from the neutron reactions and the elemental concentrations in the cement samples, a MultiLayer Perceptron (MLP) ANN was employed. This network, consisting of a hidden layer with three independent modules, was trained using the supervised error backpropagation algorithm. The results show exceptional accuracy in predicting the concentrations of the five main elements present in the cement, as well as its density, with an average relative error of less than 5 % for 97.92 % of the Validation test.
期刊介绍:
Radiation Physics and Chemistry is a multidisciplinary journal that provides a medium for publication of substantial and original papers, reviews, and short communications which focus on research and developments involving ionizing radiation in radiation physics, radiation chemistry and radiation processing.
The journal aims to publish papers with significance to an international audience, containing substantial novelty and scientific impact. The Editors reserve the rights to reject, with or without external review, papers that do not meet these criteria. This could include papers that are very similar to previous publications, only with changed target substrates, employed materials, analyzed sites and experimental methods, report results without presenting new insights and/or hypothesis testing, or do not focus on the radiation effects.