In recent years, the use of recycled aggregate concrete (RAC) has become a major concern when promoting sustainable development in construction. However, the design of concrete mixes and the prediction of their compressive strength becomes difficult due to the heterogeneity of recycled aggregates (RA). Artificial-intelligence (AI) approaches for the prediction of RAC compressive strength ${(f}_c)$ need a sizable database to have the ability to generalize models. Additionally, not all AI methods may update input values in the model to improve the performance of the algorithms or to identify some model parameters. To overcome these challenges, this study proposes a new method based on Bayesian Networks (BNs) to predict the $f_c$ of RAC, as well as to identify some parameters of the RAC formulation to achieve a given $f_c$ target. The BN approach utilizes the available data from three input variables: water-to-cement ratio, aggregate-to-cement ratio, and RA replacement ratio to calculate the prior and posterior probability of $f_c$. The outcomes demonstrate how BNs may be used to forecast both forward and backward, related to the $f_c$ of RAC, and the parameters of the concrete formulation.

The modelling of chloride transport in concrete under an electrical field requires taking into account the electrode processes. These processes are very rarely introduced into the literature, despite their impact on chloride migration and the electroneutrality of the pore solution of the material. This paper aims to propose a multi-ion model for chloride migration that takes into consideration the electrode processes. The model is applied to simulate the standard chloride migration test. The generation of OH⁻ in the cathode and H⁺ in the anode allows for the monitoring of the electroneutrality. The model considers all of the ions in the pore solution. Ion fluxes are calculated using the Nernst–Planck equation. The Langmuir model is used to simulate the chloride isotherms. The thermodynamic equilibrium in the material is considered, which reflects the ion–solid interactions during the migration. Measurements of water porosity and the chemical composition of the pore solution are essential to provide input data and the initial and boundary conditions. The numerical results of the ion profiles in the material studied confirm the electroneutrality at any point within the material, in contrast with models that do not take the electrode processes into account. The proposed model allows for the more accurate simulation of the chloride migration test and electrochemical chloride extraction in reinforced concrete structures subjected to NaCl as part of maintenance and repair strategies.

A multispecies model for chloride migration in cementitious materials was proposed. The model is applied to simulate chloride migration in the concrete submitted to the chloride migration test, considering the electrode processes. These processes are very rarely integrated into the literature, despite their impact on the chloride migration and the electroneutrality in the pore solution of the material. The generation of OH⁻ in the cathode and H⁺ in the anode allows for the monitoring of the electroneutrality. The modelling considers all the ions of the pore solution. Ion fluxes are calculated using Nernst–Planck equation. The Langmuir model is used to simulate the chloride isotherms. The thermodynamic equilibrium in the material is considered, which reflects the liquid–solid interactions during the migration. The ion profiles with and without considering the electrode processes are presented.