###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. Number** & **Object of Study** &
\begin{tabular}{c} **Predictable** \\ **Characteristics** \\ \end{tabular} & **Prediction Method** \\ \hline \multirow{4}{*}{[19]} & Concrete with the addition of & Compressive strength, tensile & Gene expression \\  & metakaolin as a partial & strength, flexural & programming, artificial neural \\  & replacement for cement & tensile strength & network, M5P model tree \\  & & & algorithm, random forest \\ \hline
[20] & Heavy concrete & Compressive strength & M5P model tree algorithm \\ \hline
[21; 22] & Heavy concrete with & Elastic modulus, & Model tree algorithm M5, \\  & secondary aggregate & compressive strength & artificial neural network \\ \hline
[23] & Concrete containing rice husk ash and & & \\  & reclaimed asphalt pavement as a partial & Compressive strength & Artificial neural network \\  & replacement for Portland cement and & & \\  & primary aggregates, respectively & & \\ \hline
[24] & Concrete with partial or complete & & \\  & replacement of natural aggregate with & Compressive strength & Artificial neural network \\  & waste rubber & & \\ \hline
[25] & Self-compacting concrete with & & Artificial neural \\  & recycled aggregate & Compressive strength & network algorithm: \\  & recycled aggregate & Compressive strength & Bayesian regularization, \\  & & & scaled conjugate gradient \\  & & & back-propagation \\ \hline
[2; 26] & Self-compacting concrete with fly ash & Compressive strength &
\begin{tabular}{c} Nonlinear dependency model, \\ multiregression model, \\ artificial neural network \\ \end{tabular} \\ \hline
[27] & Self-compacting concrete with & & & Ensemble methods: random \\  & recycled aggregates & Compressive strength &
\begin{tabular}{c} Ensemble methods: random \\ forest, k-nearest neighbors, \\ extremely randomized trees, \\ extreme gradient boosting, \\ gradient boosting, light \\ gradient boosting machine \\ \end{tabular} \\ \hline
[28] & Double-wall tubular columns with & & & Random forest regression, \\  & metal and nonmetal & Axial compressive strength & XGBoost regression, AdaBoost \\  & composite materials & & regression, lasso regression, \\  & & & ridge regression, \\  & & ANN regression \\ \hline
[29] & Geopolymer concrete & \
\begin{tabular}{c} Compressive strength, \\ flexural tensile strength \\ \end{tabular} &
\begin{tabular}{c} Artificial neural network \\ based on GDX (adaptive LR \\ with gradient descent) \\ \end{tabular} \\ \hline
[30] & Fresh concrete mix & \
\begin{tabular}{c} Plastic viscosity, \\ yield strength \\ \end{tabular} &
\begin{tabular}{c} Artificial neural network, \\ random forest \\ \end{tabular} \\ \hline
[31] & Round bounded concrete columns & Compressive strength & Multiphysics programming of \\  & & genetic expressions \\ \hline
[32] & Reinforced concrete beams with collars & Shear strength & Artificial neural network \\ \hline
[33] & Self-compacting geopolymer concrete & \
\begin{tabular}{c} Plastic viscosity, \\ compressive strength \\ \end{tabular} &
\begin{tabular}{c} Hybrid artificial neural \\ network combined with bat. \\ \end{tabular} \\ \hline
[34] & Ash concrete from rice husks & Compressive strength &
\begin{tabular}{c} Artificial neural network, \\ artificial neurofuzzy inference \\ system \\ \end{tabular} \\ \hline
[35] & Environmentally friendly concrete & \
\begin{tabular}{c} Flexural tensile strength \\ \end{tabular} &
\begin{tabular}{c} Hybrid artificial neural \\ network combined with \\ response surface methodology \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In the production of building materials, researchers generate a large amount of data containing important information about the mechanical properties of the resulting material. Data such as the volumetric content of various components, together with the description of the process and results of experiments, often have an unstructured and complex form (in the form of texts in natural language, tables, graphs) [45].