### Artificial Intelligence Applied to Concrete Production
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Output Variable** & **Input Variables** & **Methods** & **Source** \\ \hline \multirow{4}{*}{Compressive Strength} & Cement, water content, fine aggregate, normal weight coarse aggregate, lightweight coarse aggregate, and water-cement ratio. & Gaussian progress regression, support vector machine & [28] \\ \hline \multirow{4}{*}{Tensile and Flexural Strength} & Water, cement, silica fume, fly ash, coarse aggregate, fine aggregate, viscosity modifying agent, fibers, fiber properties, print speed, and nozzle area. & Gaussian Process Regression, Decision Tree Regression, Support Vector Machine, and XGBoost Regression. & [29] \\ \hline \multirow{4}{*}{Compressive, Splitting Tensile, and Flexural Strengths} & Cement, metakaolin, water-to-binder ratio, fine aggregate, coarse aggregate, superplasticizer, and age. & Gene expression programming, artificial neural network, M5P model tree algorithm, and random forest. & [30] \\ \hline \multirow{4}{*}{Flexural Strength} & Water-to-cement ratio, sand-to-aggregate ratio, superplasticizer, silica fume, steel fiber volume. & Gradient boosting, random forest, and extreme gradient boosting. & [31] \\ \hline \multirow{4}{*}{Compressive Strength} & Water-to-cement ratio, coarse aggregate, sand, fine aggregate & multiple linear regression, artificial neural network, and adaptive neuro-fuzzy inference system. & [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of AI techniques found in the literature for prediction of concrete slump. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Output Variable** & **Input Variables** & **Methods** & **Source** \\ \hline \multirow{4}{*}{Slump and Compressive Strength} & Water-cement ratio, cement content, compressive strength of cement, fine aggregate, fineness module, chemical admixture, type of aggregate (limestone or basalt). & Regression tree, random forest, support vector machines, artificial neural network, partial least square, bagging, fuzzy logic. & [33] \\ \hline \multirow{4}{*}{Slump} & Water, cement, fine aggregate, coarse aggregate, fly ash, water-reducing admixture, superplasticizing admixture. & Regularized logistic regression, linear discriminant analysis, K-nearest neighbors, support vector machines, classification and regression tree, random forest, extreme gradient boosting. & [34] \\ \hline \multirow{4}{*}{Slump} & Cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, flow. & Neural networks. & [35] \\ \hline \multirow{4}{*}{Slump} & Cement, water-to-binder ratio, fly ash, sand, coarse aggregate, superplasticizer. & Nonlinear regression, multi-linear regression, and artificial neural networks. & [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)Algorithms based on artificial intelligence techniques have proven to be relevant in exploring the properties of complex systems when there are sufficient data for training. This study presents a solution to forecast the compressive strength, flexural strength, and slump of concrete based on the quantities of the materials in the mix. For compressive strength, the best model identified was a single-hidden-layer multi-layer perceptron regressor with 134 units, achieving a mean absolute percentage error (MAPE) of 11.4% measured through cross-validation. For flexural strength, a single-hidden-layer multi-layer perceptron regressor with 178 units achieved a MAPE of 14%. Regarding slump, the best-performing model was a single-hidden-layer multi-layer perceptron regressor with 193 units, which achieved a MAPE of 7%. MAPE is calculated as shown in Equation (2), representing the average percentage error of the prediction relative to the actual value.