## 6 Conclusions  
To provide a reliable reference for rapid and accurate regional seismic damage evaluation considering SSI, based on a validated finite element model considering the nonlinearity of both the structure and soil, detailed parametric analyses considering the influence of multiple factors such as the pile length, pile diameter, number of spans, frequency of structure, seismic records, and soil shear wave velocity on SSI effects were conducted. Based on the presented works, some conclusions are provided as follows:  
1. For pile-supported RC-frames considering SSI, the maximum base shear generally increases with a larger pile length and the growth is within 10%; the maximum base shear and story drift, respectively, increases and decreases with the increase in the pile diameter, and the maximum change is up to 20% and 40%, respectively.
2. Both the span number and seismic intensity can considerably influence the seismic demands of pile-supported RC-frames in some cases and their influences on the structural seismic demands have no obvious laws. In general, the influence of SSI on the structural seismic demands decreases with a larger soil shear wave velocity.
3. SSI can reduce the maximum base shears of pile-supported RC-frames in most cases while it can also significantly increase the structural maximum base shear and story drift in some specific cases, especially for low-rise structures built on tough soil. The SSI effect on the maximum base shear does not always have a positive correlation with that on the maximum story drift.
4. The SSI effect on seismic demands predominantly depends on the structural frequency, spectral characteristic, and peak acceleration of the seismic record and the soil shear wave velocity while the influence of the pile diameter and span number of structure still cannot be neglected in some cases.
5. The influence of the seismic soil-structure interaction should be fully considered to reduce casualties and economic losses when designing low-rise structures built on tough soil and high-rise structures built on soft soil. Machine learning is expected to solve the seismic soil-structure interaction issue by establishing and training a data set that sufficiently considers all the influencing factors of soil-structure interaction.  
Figure 17: Comparisons of \(e_{sd}\) and \(e_{ls}\) under different peak accelerations.  
**Author Contributions:** Conceptualization, J.W. and J.Y.; Data curation, J.W.; Funding acquisition, J.W.; Investigation, J.W.; Methodology, J.W. and J.Y.; Resources, J.Y.; Software, J.W. and J.Y.; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON] and J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [Scientific Research Foundation of Graduate School of Southeast University] grant number [No. YBPY2125].  
**Data Availability Statement:** The data can be obtained through contacting the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge the Scientific Research Foundation of Graduate School of Southeast University (No. YBPY2125).  
**Conflicts of Interest:** The authors declare no conflict of interest.