## 5 Conclusions  
This study aimed to develop an accurate and reliable M5P-tree-based model to predict the punching shear capacity of RC flat slabs without shear reinforcement. The motivation behind the development of this model was to address the limitations of existing methods and provide a more comprehensive tool for structural engineers. The key findings of the study and the limitations of the research are summarized below. * The M5P algorithm outperformed existing models and design codes, providing more accurate predictions for the punching shear strength. This improved accuracy could lead to more efficient designs and increased safety in RC structures. * The effective depth of the slab (\(d\)) was identified as the most significant factor affecting the punching shear strength, which is consistent with previous studies and engineering experience. * The M5P model demonstrated a high level of accuracy, with a low correlation between input design variables and model error. This is an essential characteristic for an ideal \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Excluded** \\ **Variables** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Input** \\ **Variables** \\ \end{tabular} } & \multicolumn{4}{c}{**Training Set**} & \multicolumn{4}{c}{**Testing Set**} \\ \cline{3-8}  & & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline None & \((b)(d)(f_{c})(f_{y})(\rho)(\lambda)\) & 0.9857 & 77.1034 & 47.9516 & 0.9806 & 71.9993 & 48.3483 \\ \((b)\) & \((d)(f_{c})(f_{y})(\rho)(\lambda)\) & \((-4.59\%)\) & \((+50.9\%)\) & \((+33.6\%)\) & \((-5.95\%)\) & \((+50.85\%)\) & \((+41.6\%)\) \\ \((d)\) & \((b)(f_{c})(f_{y})(\rho)(\lambda)\) & 0.6676 & 271.8977 & 125.1944 & 0.5988 & 228.9639 & 136.6226 \\ \((\lambda)\) & \((-32.28\%)\) & \((+252.7\%)\) & \((+161.1\%)\) & \((-38.91\%)\) & \((+218.0\%)\) & \((+182.5\%)\) \\ \((f_{c})\) & \((b)(d)(f_{y})(\rho)(\lambda)\) & \((-6.9171\) & 140.4382 & 72.2165 & 0.9383 & 93.0885 & 60.4217 \\ \((f_{y})\) & \((b)(d)(f_{c})(\rho)(\lambda)\) & \((-6.96\%)\) & \((+82.2\%)\) & \((+50.6\%)\) & \((+43.1\%)\) & \((+29.3\%)\) & \((+25.0\%)\) \\ \((f_{y})\) & \((b)(d)(f_{c})(\rho)(\lambda)\) & 0.9673 & 82.8514 & 48.3227 & 0.9592 & 75.5153 & 49.3749 \\ \((\rho)\) & \((b)(d)(f_{c})(f_{y})(\lambda)\) & \((-1.87\%)\) & (+7.45\%) & (+0.77\%) & \((-2.18\%)\) & \((+4.88\%)\) & \((+2.12\%)\) \\ \((\rho)\) & \((b)(d)(f_{c})(f_{y})(\lambda)\) & 0.9017 & 152.1536 & 78.1118 & 0.9052 & 113.9448 & 70.9103 \\ \((\lambda)\) & \((b)(d)(f_{c})(f_{y})(\rho)\) & \((-8.52\%)\) & (+97.4\%) & (+62.9\%) & \((-7.69\%)\) & \((+58.3\%)\) & \((+46.7\%)\) \\ \((\lambda)\) & \((b)(d)(f_{c})(f_{y})(\rho)\) & 0.9698 & 79.5789 & 48.8820 & 0.9456 & 84.1065 & 55.4452 \\ \((\lambda)\) & \((b)(d)(f_{c})(f_{y})(\rho)\) & \((-1.61\%)\) & (+3.22\%) & (+1.94\%) & \((-3.57\%)\) & \((+16.8\%)\) & \((+14.7\%)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effect of input variables on the performance of the proposed M5P model. predictive model, as it ensures that the predictions are not significantly influenced by irrelevant factors. * The sensitivity analysis indicated that the effective depth of the slab (\(d\)) had the most significant impact on the model performance, while the yield strength of reinforcement (\(f_{y}\)) had the least impact. This information can be used to prioritize design considerations and improve the overall efficiency of the design process. As limitations of this research, the following ones can be stated:
* The dataset used for the development and validation of the M5P model was limited in size and scope, which may affect the generalizability of the results. Future research could benefit from larger and more diverse datasets, including slabs with different reinforcement configurations and materials. * This study focused on the prediction of punching shear capacity without considering other failure modes or serviceability requirements.