## 8 Present Work Limitations of the and Future Research Recommendations  
Limitations of the current study are as follows: First is the dataset's size and diversity. Although this study utilized a database of 583 RCSW shear tests, the diversity in the dataset may not fully encompass all variability in material properties, geometric configurations, and loading conditions. This may limit the applicability of the developed model to a variety of shear walls not represented by the dataset. The effectiveness of ML models is highly dependent on feature selection. This study may not have considered all possible influences, such as environmental factors or material deterioration, which may affect shear strength capacity but were not included in the analysis. Second is the model interpretability.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**CoV**} \\ \cline{2-10}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline Stacking model & 2.34 & 128.2 & 5.51 & 1644.4 & 0.33 & 65.8 & 1 & 0.98 & 0.013 & 0.147 \\ [PERSON] et al. [16] & 24.17 & 92.30 & - & - & 14.49 & 48.79 & 1 & 0.98 & - & 0.168 \\ [PERSON] et al. [17] & 4.25 & 84.41 & - & - & 2.68 & 49.30 & 1 & 0.98 & - & - \\ [PERSON] et al. [22] & 2.7 & 91.1 & - & - & 1.22 & 58.63 & 1 & 0.98 & - & 0.081 \\ [PERSON] et al. [23] & - & - & - & - & - & - & - & 0.94 & - & 0.103 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Predictive performance metrics for stacked models.  
Although ensemble and deep ML models provide high predictive performance, they do not have the explanatory nature of traditional analytical models. The complexity of these models may hinder the understanding of the mechanisms behind shear wall behavior, which is critical for engineers. The final is the hyperparameter tuning. Although hyperparameter optimization is performed, the tuning process is still sensitive to the optimization algorithm and the choice of the defined search space. This may result in models that perform well on the training dataset but may not be robust enough in real-world applications.  
Recommendations for future research are as follows: First is to expand the scope of dataset collection. Future research should aim to collect a wider and more diverse dataset, including different types of shear walls, material properties, and loading conditions, which will enhance the generalization and robustness of the predictive model. Again, incorporating other influencing factors. Future research could explore the incorporation of other variables, including environmental factors and other physical factors that may influence shear wall behavior. Incorporating these factors will improve the accuracy, reliability, and applicability of the predictions. Second is the improved model interpretability. Methods need to be developed to improve the interpretability of advanced models (e.g., deep learning) while maintaining their predictive power. Techniques, such as SHAP (Shapley Additive Interpretation) or LIME (Locally Interpretable Model Irrelevant Interpretation), can be employed to provide insights into the model's decision-making process. In addition, hybrid models are applied. Hybrid models that combine traditional analytics and machine learning methods can be explored in the future to improve the accuracy and interpretability of predictive models. The final is to conduct a longitudinal study. It would be beneficial to examine the performance of shear walls over time. This could involve the development of predictive models that take into account the effects of material aging and changes in properties, thus providing a more comprehensive understanding of shear wall behavior under different conditions.