Experimental and DBN-Based neural network extraction of radiation attenuation coefficient of dry mixture shotcrete produced using different additives. (November 2021)

• Record Type: Journal Article
• Authors: Çakıroğlu, Melda Alkan; Kaplan, Ali Nadi; Süzen, Ahmet Ali
• Is Part Of: Radiation physics and chemistry. Volume 188(2021)
• Publication Date: 2021-11
• Subjects: DBN -- Deep learning -- Dry mixture shotcrete -- Neural network -- Radiation attenuation coefficient; Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2021.109636
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: In this study, the radiation attenuation coefficients (μm ) of different proportions of additives were produced in dry mixture shotcrete both by experimental processes and by deep neural network based on DBN. Fly ash, silica fume, and polypropylene fiber were used as additives of dry mix shotcrete. In the first part of the two-part study, μm values were obtained from seven samples produced and a data set was created along with the input parameters of the experiment. In the second part, a model was developed for predicting the value of μm with input parameters using the DBN deep neural network Algorithm. Experimental data obtained in accordance with both applications and data generated by the Deep Belief Network (DBN) model were analyzed. As a result, the DBN model prediction μm values with an accuracy performance of 87.86%. Highlights: Modeling of DBN-based deep neural network for generating radiation attenuation coefficient. Investigation of the effect of additives used in dry mix shotcrete on radiation attenuation coefficient. Verification of experimental data with a predict model.