Hybrid and optimized neural network models to estimate the elastic modulus of recycled aggregate concrete

In the present study, several hybrids and coupled forms of machine learning algorithms were developed to provide accurate elastic modulus of recycled aggregate concrete's (ERAC) estimation, called multilayer perceptron neural networks (MLPNN). For this gain, a comprehensive dataset was collected from literature containing 400 rows of samples. Networks with different hidden layer numbers were used to develop MLPNN (from one layer to three layers). The accuracy of MLPNN deeply related to neuron numbers of each hidden layer, wherein the present paper, three optimization algorithms; the arithmetic optimization algorithm (AOA), equilibrium optimizer (EO), and flow direction algorithm (FDA); are merged with MLPNN to obtain the ideal value of neuron numbers. Considering all models, all developed models have the acceptable ability for predicting the ERAC by the coefficient of determination of at least 0.9306 for the learning stage and 0.9411 for the examining stage. All in all, the MLPNN model with two numbers of hidden layers with a structure of 17-14-1 optimized with AOA can be proposed as the most appropriate model. The created MLPNN models may optimize material selection and quality control in building processes, reducing dependence on natural aggregates.