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.

In this work, an approach is presented to assess the reinforcement depassivation probability of reinforced concrete structures under corrosion induced by carbonation or chloride diffusion. The model consists of coupling mathematical formulations of CO₂ and Cl⁻ diffusion with Monte Carlo simulation (MCS). Random events were generated using MCS to create several design life and environmental scenarios. A case study was performed by simulating five Brazilian environmental conditions and distinct mixes of concrete. The effect of input parameters on the reinforcement concrete depassivation probability was evaluated. The results point out that the depassivation probability due to carbonation is more significant in urban centers, and the compressive strength of concrete has the main influence on the depassivation probability. Results also showed that the depassivation probability due to chloride ingress is influenced by, in order of importance, the chloride content on the surface (61.4%), concrete cover (20.3%), compressive strength (7.1%), relative humidity (6.1%), and temperature (5.1%). In addition, an increase in the compressive strength of concrete, from 30 to 50 MPa, can reduce depassivation probability by up to 70%, resulting in a concrete structure that attends the durability limit state. Thus, by incorporating probabilistic approaches, this model can be a valuable tool in the civil construction industry for studying the improvement of durability, reliability, and safety of reinforced concrete structures.