Abstract
In the present study, we have used feed-forward neural network (FFNN) for the prediction of compressive and flexural strength of steel slag mixed concrete for pavements. The compressive and flexural strength of the specimens were examined experimentally for the specimens consisting of 0, 10, 15, 20 and 25% Argon Oxygen Decarburization (AOD) steel slag as a partial replacement of cement in M40 concrete mix. The curing of specimens was done for 3, 7, 28, 90, 180 and 365 days and thus accounting for the total of 90 observations. Both the output parameters were dependent upon 8 input parameters. To evaluate the performance of the FFNN model, we have used Mean Squared Error (MSE) and Mean Average Error (MAE) as the performance indicators.
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Gupta, T., Sachdeva, S.N. (2021). A Novel Approach for Predicting the Compressive and Flexural Strength of Steel Slag Mixed Concrete Using Feed-Forward Neural Network. In: Pandey, V.C., Pandey, P.M., Garg, S.K. (eds) Advances in Electromechanical Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5463-6_41
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DOI: https://doi.org/10.1007/978-981-15-5463-6_41
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