## 6 Discussion and Conclusions  
In the present study, we proposed a ML approach of the structural damage prediction after a single or multiple seismic events on an RC frame in terms of DI\({}_{\text{G,PA}}\) and DI\({}_{\text{DC}}\) damage indices using as input features the IMs of the second shock and the established damage after the first one. The ability of ten MLAs to model the problem of structural damage prediction of an RC frame under single or multiple ground motions was thoroughly investigated. For this purpose, multiple error metrics were adopted in order to assess the predictive capacity of the examined MLAs. Then, it took place thorough comparison between the now known MLAs. Moreover, the generalization of the most efficient algorithms was evaluated. The investigation relied entirely on evaluative methods of sensitivity analysis, variability and error analysis. The comparative study indicates that the structural damage under single and multiple seismic shocks can be efficiently described in terms of DI\({}_{\text{G,PA}}\). Moreover, adopting the extra trees regressor, a higher prediction performance is gained. This algorithm facilitates the learning of specialized functions for extracting useful representations in complex learning dependencies and utilizes random decision trees to learn without causing uncertainty issues. Moreover, overfitting is avoided, while the algorithm utilizes significantly reduced computing training costs and time, producing improved training stability, high generalization performance and remarkable determination accuracy. In addition, the algorithm leads to much better predictive results and high generalization ability with reduced bias and variance. Therefore, a robust forecasting model capable of responding to the highly complex problem of structural damage prediction is deduced. It should also be emphasized that this methodology deals with the noisy scattered residuals points with great accuracy. Based on the results, this algorithm provides a generalized model that predicts the final damage of the examined RC frame given the initial damage and the seismic excitation characteristics. Conclusively, as an outcome of this research, the authors developed a user-friendly web application that incorporates the results of this study. Future work should focus on examining an expanded dataset that incorporates a wide range of structural features such as design code, height level, elevation and plan regularity in order to generate a universal model of structural predictions under single and multiple shocks. Conceptualization, P.C.L., I.E.K. and K.D. ; methodology, P.C.L., I.E.K. and K.D. ; software, P.C.L., I.E.K. and K.D. ; validation, P.C.L., I.E.K. and K.D. ; investigation, P.C.L., I.E.K. and K.D. ; data curation, P.C.L. ; writing--original draft preparation, P.C.L., I.E.K. and K.D. ; writing--review and editing, P.C.L., I.E.K. and K.D. ; visualization, P.C.L. ; supervision, L.L., L.K.V. ; project administration, P.C.L., I.E.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Acknowledgments:** The first author needs to gratefully thank his parents for their significant support during his studies. **Conflicts of Interest:** The authors declare no conflict of interest.