## 6 Conclusions  
URM buildings are a valuable part of cultural heritage and residential architecture worldwide, despite their limited ability to bear lateral loads. Further, these buildings are prevalent in regions highly prone to earthquakes. This study examines the effectiveness of various ML algorithms in identifying the seismic response and damage classification of a benchmark URM building. A comprehensive database was established by performing numerical analysis on a benchmark URM building, due to the absence of experimental data in the literature suitable for training ML models. This study presents a novel application of a diverse range of ML algorithms for the classification of seismic damage, which may improve the seismic risk assessment of non-engineered, unreinforced masonry buildings. Eight ML algorithms were used to predict the seismic response of the URM building. The results showed that the GPR model exhibited better performance, with smaller \(R^{2}\) values and larger RMSE values in both training and test sets. Additionally, the comparison of the actual and predicted responses from the GPR model showed better fit than other regression models. The percentage variation in the \(R^{2}\) and RMSE values of the training and test sets (0.20% and 3.50%, respectively) for the GPR model were also found to be smaller than those of the other prediction models. The lowest RMSE value of the training set was found to be associated with the RF model, but the RMSE value of the test set was higher (44.88% higher), indicating the overfitting of the model. Hence, the results indicated that the overall performance of the non-parametric models such as GPR, RF, and NN was better than that of the linear models, indicating the nonlinear relationship between the predictor and response variables. In the case of damage classification, eight ML algorithms were explored to identify the suitability of the models for the classification of the damage in a URM building. The results showed that the RF model had a higher accuracy rate, at around 92.90%, and a misclassification rate of 0.07. The precision and recall values associated with DS4 in the RF model were found to be equal to 96.6%, which indicates the accurate classification of completely damaged building cases, which is crucial in the seismic damage assessment of buildings. Followed by the RF model, the performance of the NN and AB models was found to be comparatively better than that of the other classification models used in the study. Additionally, the F1 score of these models was also higher, suggesting that the models performed better in distinguishing different damage states accurately. Finally, the relative-importance assessment of the input parameters in RF model showed that the IDR value was the most important predictor, with a relative importance of about 26.84%, followed by PGA (16.81%) and shear strength of masonry (11.5%). Figure 13: Relative importance of input parameters affecting the Random Forest classification model. The results of the study highlight the effectiveness of various ML models in the prediction of seismic response and classifying damage in URM buildings. The ML models, in conjunction with the optimized parameters outlined in the study, can be effectively employed to address similar challenges related to the prediction of seismic response in URM buildings worldwide. Moreover, these results will significantly influence the post-earthquake assessment of URM buildings and aid researchers in designing their experimental and numerical studies on URM buildings. The important limitation in this demonstrated application of ML models for the damage prediction of a URM building is the requirement of the use of similar features for the input and output variables. Furthermore, accurate prediction of response and damage classification necessitate the use of precise values for masonry material properties and detailed descriptions of damage states.