Prediction of concrete compressive strength due to long term sulfate attack using neural network

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 200 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, C(3)A 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. (C) 2014 Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.