\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors** & **Inputs** & **Methods** & **Database** & **References** \\ \hline [PERSON] et al. (1999) & \begin{tabular}{c} _SPT-N_ values and geometrical properties \\ \end{tabular} & Neural Network & 25 & [25] \\ \hline [PERSON] (2011) & Dynamic stress-wave data & \begin{tabular}{c} Support Vector Machines and \\ Generalized Regression Neural Network \\ \end{tabular} & 105 & [26] \\ \hline [PERSON] et al. (2015) & \begin{tabular}{c} Flap number, basic properties of \\ the surrounding soil, pile \\ geometry, and pile-soil friction \\ angle \\ \end{tabular} & \begin{tabular}{c} Artificial Neural Network, Genetic \\ Programming and Linear \\ Regression \\ \end{tabular} & 100 & [27] \\ \hline [PERSON] et al. (2017) & \begin{tabular}{c} Soil length to socket length ratio, \\ total length to diameter ratio, \\ uniaxial compressive strength, and \\ standard penetration test \\ \end{tabular} & hybrid _PSO–ANN_ & 132 & [1] \\ \hline [PERSON] and [PERSON] (2018) & \begin{tabular}{c} Internal friction angle of soil \\ located in shaft and tip, pile length, \\ effective vertical stress at pile toe and pile area \\ \end{tabular} & _ICA-ANN_ & 59 & [31] \\ \hline [PERSON] et al. (2021) & \begin{tabular}{c} Pile length, pile cross-sectional \\ area, hammer weight, pile set, and drop height \\ \end{tabular} & _ANFIS_, _GP_, and _SA–GP_ & 50 & [2] \\ \hline [PERSON] et al. (2019) & \begin{tabular}{c} Internal friction angle of soil \\ located in shaft and tip, effective \\ vertical stress at pile toe, pile area, and pile length \\ \end{tabular} & _ICA-ANN_ and _ANFIS_ & 59 & [29] \\ \hline [PERSON] et al. (2020) & \begin{tabular}{c} Shear resistance angle at the shaft of the pile, soil shear resistance \\ angle at the tip of the pile, length \\ of pile, cross-sectional area of the pile, and effective stress at the tip \\ of the pile \\ \end{tabular} & _Decision tree_, _k-nearest neighbor_, _Multilayer Perceptron Artificial_ _Neural Network_, _Random Forest_, _Support Vector Regressor_, and _Extreme Gradient Boosting_ \\ \hline [PERSON] et al. (2021) & \begin{tabular}{c} CPT and pile loading test results \\ \end{tabular} & _ANFIS_ and _ANFIS_–_GMDH_–PSO_ & 72 & [30] \\ \hline [PERSON] et al. (2020) & \begin{tabular}{c} Pile diameter, pile length, relative \\ density, embedment ratio, and \\ both the pile end resistance and \\ base resistance \\ \end{tabular} & _GA-ANFIS_ and _PSO-ANFIS_ & 20 & [28] \\ \hline [PERSON] et al. (2020) & \
\begin{tabular}{c} Laboratory and in situ testing \\ results \\ \end{tabular} & _ANFIS_, _ANN_, and _GA-ANN_ & 43 & [33] \\ \hline [PERSON] et al. (2021) &
\begin{tabular}{c} Pile area, pile length, flap number, \\ average cohesion and friction angle, average soil-specific weight, \\ and average pile-soil friction angle \\ \end{tabular} & _MLP-GWO_ and _ANFIS_–GWO_ & 100 & [34] \\ \hline \end{tabular}
\end{table} 
Table 1: Proposed machine-learning models in the literature to estimate the pile-bearing capacity. be assured. Moreover, the majority of published papers have proposed machine-learning models in the form of mathematical equations, which are hard to duplicate in future studies. Admittedly, this practice has very little value for other researchers and civil engineers in the field. Conveniently, to overcome these limitations, investigators have presented their optimal models in the form of a programmed interface or a simple script by a well-known programming language such as Python or Matlab for generating the proposed model. This will make it available to anyone interested in the problem of modeling regardless of their proficiency level. The current study contributes to providing a new alternative model for predicting the pile-bearing capacity based on 12 advanced machine-learning methods, which are applied for the first time for this aim.