## 7 Summary and Conclusions  
Non-structural components (NSCs) play a crucial role in ensuring the continued operation of buildings following an earthquake, thereby emphasizing the significance of constructing seismically resilient structures. The goal of the current study was to evaluate how a soft story's position affects the floor responses of a building structure. The analysis considers the soft story at different levels of a structure. Eleven ground motions were considered for the time history analysis, and they were spectrally adjusted to be in accordance with the design spectrum for the hard soil and seismic zone V as per Indian standards. This study presents an innovative predictive model for dynamic amplification factors using state-of-the-art machine learning (ML) methods. Techniques such as the artificial neural network (ANN) and random forest (RF) were utilized to create accurate CDAF spectra, resulting in improved predictive accuracy. A total of five input parameters (the tuning ratio (\(T_{s}/T_{1}\)), the damping ratio of the component (\(\xi_{s}\)), the floor level, the soft-story level, and the excitation direction) are included in the database. A comprehensive exploration of hyperparameters was carried out to evaluate the effectiveness of each ML algorithm and determine the optimal performer. The application of the SHAP methodology is explored to elucidate the anticipated outcome and prioritize the input variables along with their interrelationships for the component dynamic amplification factor (CDAF), utilizing the devised RF model. To analyze the impact of different input variables on the forecasted CDAFs through an ANN model, [PERSON]'s algorithm was employed. The following concise conclusions may be made based on the examination of the building models:  
Figure 18: SHAP feature global importance plot. * In structures without vertical stiffness irregularity, the lower floor levels exert substantial seismic demands on the attached short-period NSCs. As the height of the soft story increases, the spectra at different levels start to converge. * Component acceleration experiences the highest amplification in middle-soft-story structures. The average amplification factor values are 2.72 for the bottom soft story, 3.74 for the top soft story, and 4.24 for middle-soft-story buildings, associated with the fundamental period of the component. * The damping ratio (\(\xi_{s}\)) strongly influences the amplification factors at the vibration periods of the buildings. However, it is worth mentioning that the impact of \(\xi_{s}\) becomes insignificant for both very long and extremely short NSC periods. * The CDAF deviates from the criteria of ASCE 7, FEMA P\(-\)750, and Eurocode 8, either underestimating or overestimating the values, particularly at NSC periods closer to the time periods of the buildings being studied. * At the soft story, the code-based definitions currently underestimate the amplification factors. Therefore, it is necessary to revise the existing code formulation to incorporate the impacts of vertical stiffness irregularity in the analysis. * In this study, machine learning (ML) techniques, specifically artificial neural network (ANN) and random forest (RF), are employed to develop prediction models for CDAF spectra. Among these techniques, RF is demonstrated to be a more effective and powerful tool compared to the ANN. * The sensitivity analysis (SHAP and [PERSON]'s algorithm) demonstrated that the component dynamic amplification factor (CDAF) is significantly influenced by the tuning ratio (\(T_{s}/T_{1}\)), the damping ratio (\(\xi_{s}\)), and the position of the non-structural component, i.e., floor level. The excitation direction and the soft-story level has relatively lesser influence. * This study's findings are limited to simple reinforced concrete moment-resisting bare frame structures, analyzed at a specific hazard level. To make the study more comprehensive, irregular structural models should be explored.