School buildings are designed to serve both as places of education and as temporary shelters in the aftermath of major earthquakes, such the status of school buildings are very important. Therefore, how to correctly and quickly assess the seismic performance of an existing school building is an urgent issue that deserved to further investigate. Moreover, when the researchers using artificial intelligence to infer the seismic performance of the school building, it is also a disturbing topic worthy of discussion on how to determine the appropriate number of school cases and seismic factors. Because when the number is excessive, it will take lots of time and cost to build the seismic database and infer the seismic assessment model. If the number is insufficient, the inference result will be not satisfactory. In order to solve the above subjects, this paper used several research methods to optimize the seismic assessment model. Firstly, the sensitivity analysis was applied to test an optimal model under the consideration of number of school cases and seismic factors. Then used the grey theory to explore the relationships between the seismic factors and seismic performance of school buildings. Finally, adopted support vector machine (SVM) and gene expression programming (GEP) to deduce the optimal models, SVM were also validated by 10-Fold Cross-Validation. Results show that when researchers apply artificial intelligence theory, the number of factors should be at least five. If researchers want to get a better inference result, the number of factors can be more than ten. As for the number of testing cases, two times the number of factors should be taken, and more than three times is preferred. The seismic assessment models inferred by SVM and GEP, possess good performance. These results can be used by architects and general engineers, and the developed research methods can also be referenced for subsequent researches in academia.
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