Abstract
A new formulation to estimate the elastic modulus of concrete containing recycled coarse aggregate is proposed in this work using artificial neural networks (ANN) and nonlinear regression. Up to six predictors variables were used to training 243 ANN. The models were generated based on results obtained from experimental campaigns. Feedforward neural network and Levenberg–Marquardt back propagation algorithm were used for training the ANN. The best ANN was found with the architecture 6-4-2-1 (input -1st hidden layer -2nd hidden layer -output), attaining a root-mean-square error of 2.4 GPa associated with a coefficient of determination of 0.91. Once the ANN model was established, 46,656 concrete samples were created. These were employed to formulate the model using nonlinear regression. The developed model showed a highly efficient performance to predict the elastic modulus. Lastly, considering the parametric study conducted, the results pointed out that the approach can be applied to predict the concrete elastic modulus and can indicate better mix proportions for concretes containing natural and/or recycled coarse aggregates, enabling its use as a simulation tool in the development of engineering projects focused on durability and sustainability.
Highlights
Civil construction is one of the sectors that most contribute to cities’ development and economic growth
To select the topology that best maps the elastic modulus of concrete containing natural and recycled coarse aggregates, the results presented in Figure 8 and Table 2 were analyzed, where the performance parameters obtained in the training and validation stages were compared
We evaluated the possibility of applying machine learning coupled with made with natural and recycled coarse aggregate
Summary
Civil construction is one of the sectors that most contribute to cities’ development and economic growth. Nowadetermination of concrete elastic modulus consists of a destructive technique in which madays, there are alternative non-destructive techniques to estimate the elastic modulus, terial samples are subjected to loading tests to generate the stress–strain curve. More sophisticated models were developed using the theory of of a more complex way, on solving equations that represent the mechanical behavior elasticity, concrete rheology, mechanics of composite solids [25,26,27,28,29]. These models composite materials [21,22,23,24].and.
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