Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
816188 | Alexandria Engineering Journal | 2014 | 16 Pages |
Abstract
This work was divided into two phases. Phase one included the validation of neural network to predict mortar and concrete properties due to sulfate attack. These properties were expansion, weight loss, and compressive strength loss. Assessment of concrete compressive strength up to 200Â years due to sulfate attack was considered in phase two. The neural network model showed high validity on predicting compressive strength, expansion and weight loss due to sulfate attack. Design charts were constructed to predict concrete compressive strength loss. The inputs of these charts were cement content, water cement ratio, C3A content, and sulfate concentration. These charts can be used easily to predict the compressive strength loss after any certain age and sulfate concentration for different concrete compositions.
Keywords
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Physical Sciences and Engineering
Engineering
Engineering (General)
Authors
Ahmed M. Diab, Hafez E. Elyamany, Abd Elmoaty M. Abd Elmoaty, Ali H. Shalan,