A Bayesian Network Framework to Predict Compressive Strength of Recycled Aggregate Concrete

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.