Experimental and DBN-Based neural network extraction of radiation attenuation coefficient of dry mixture shotcrete produced using different additives
| Autor: | Ahmet Ali Süzen, Ali Nadi Kaplan, Melda Alkan Çakıroğlu |
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| Rok vydání: | 2021 |
| Předmět: |
Radiation
Materials science Silica fume Artificial neural network 010308 nuclear & particles physics Model prediction Extraction (chemistry) 01 natural sciences Shotcrete 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep belief network 0302 clinical medicine Fly ash 0103 physical sciences Biological system Radiation attenuation |
| Zdroj: | Radiation Physics and Chemistry. 188:109636 |
| ISSN: | 0969-806X |
| DOI: | 10.1016/j.radphyschem.2021.109636 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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