Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

Figure 1

The three steps of this study for RAC mixing. (a) Each factor and S/a value of NAs and RAs were used to construct the NN model using how they satisfied the concrete performance criteria (e.g., slump, air content, admixture, and compressive strength), and how they matched the selected factor to receive the factor's optimum value. (b) Each factor’s value obtained in “a” was applied to the NN input and output variables. The neuron that received the input value displayed the output value using a connection weight. This time and connection weight were calculated using a sigmoid transfer function. (c) The GA was applied to optimize each NN parameter (e.g., momentum, learning rate of the NN, and number of neurons in the hidden layers).