Comparison between Neural Network and Fuzzy Logic on Assessment of Long Term Concrete Compressive Strength and Expansion Due To Sulfate Attack
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 9
Abstract
This study is divided into two phases. Phase I includes assessment of the validation of neural network and fuzzy logic in predicting mortar and concrete properties due to sulfate attack. These properties include expansion, weight loss, and compressive strength loss. The neural network and fuzzy logic models showed high validity on predicting compressive strength, expansion and weight loss for deteriorated concretes and mortars subjected to sodium and magnesium sulfate attack. The main objective of this study is presented in phase II. Phase II aims to present a new application of neural network to assess concrete compressive strength up to 200 years subjected to any concentration of sodium or magnesium sulfate. Cement content, water cement ratio, C3A content, and sulfate concentration were the inputs of the neural network model. Design charts were established using the output results of neural network models. These charts can be used easily to predict the compressive strength loss after any certain age and sulfate concentration for different concrete compositions.
Authors and Affiliations
Ahmed M. Diab, Hafez E. Elyamany, Abd Elmoaty M. Abd Elmoaty, Ali H. Shalan
Keywords
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- EP ID EP21250
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