## 4 Discussion of Prediction Results  
### Prediction Performance of Different Scenarios  
The overall confusion matrix of the test set is shown in Figure 8. The overall test accuracy is 79.7%, and the prediction accuracies of all damage states are around 80%. Meanwhile, more than 90% of the predicted damage states are within one level difference to the actual damage states. Note that ground motions in the training set come from pre-2003 earthquakes in the PEER database, while ground motions in the validation and test sets come from post-2003 earthquakes in the K-Net database. This arrangement ensures that the ground motions adopted to test the accuracy of the model are completely different from those used to train the model. Therefore, the test accuracy of 79.7% indicates that the proposed method can reasonably predict seismic damage of building even for earthquakes that do not exist in the training set. Moreover, the computation efficiency of the MI-CNN model is significantly better than the nonlinear time history analysis of the MDOF shear model. For example, the computation time of 278,000 samples in the test set is 31.38 s for the MI-CNN model, which is 340 times faster than the nonlinear time history analyses of MDOF shear models (170 min) on a laptop platform (AMD Ryzen 5 3600, RAM 32 G, GPU GTX1660S). To further demonstrate the prediction performance of the MI-CNN model, samples in the test set with different PGAs, building fundamental periods, and for  
Figure 8: Confusion matrix of the test set (prediction accuracy = 79.7%). The sensitivity analysis of results on fundamental period is shown in Figure 9a, and the prediction accuracy of most samples is around 80%. Nevertheless, the prediction accuracy for Labels 3 and 4 with PGA of 1 m/s\({}^{2}\) is missing, and this is because the PGA of 1 m/s\({}^{2}\) is relatively small, and no sample reaches the severe and complete damage states when PGA is 1 m/s\({}^{2}\). Moreover, the prediction accuracies for Labels 3 and 4 in the PGA range of 1-3 m/s\({}^{2}\) are still relatively poor; to further investigate the reason for this, the distribution of the training set together with the prediction accuracies for samples with PGA of 1-3 m/s\({}^{2}\) are illustrated in Figure 9b. As is evident in Figure 9b, the prediction accuracies are relatively low for Labels 3 and 4 (64.3% and 59.1%). This is because the proportion of training set samples in Labels 3 and 4 are very small (2.3% and 0.3%), which can jeopardize the prediction accuracy for this scenario. It is worth noting that this skewed distribution of the training set in the PGA range of 1-3 m/s\({}^{2}\) is not a mistake, this is because there are some very unique ground motions with a very strong destructive power to mid-rise buildings, even with a very small PGA. The authors think that this is a limitation of the proposed model, and further studies are required to consider this exceptional scenario. The confusion matrices for cases with different PGAs are presented in Figure 10. The prediction accuracies of all PGA bands are above 77%. It is worth noting that, according to Figure 1a, when the ground motion intensity measure is large, the building seismic response can be very dispersed. Nevertheless, as shown in Figure 10d, the prediction accuracy for buildings with a large PGA can still yield good accuracy (77.5%), which indicates that the MI-CNN model can effectively predict the seismic response of buildings in very strong nonlinearity. Figure 9: Influence of PGA on prediction accuracy: (**a**) prediction accuracies for samples with different PGAs; (**b**) distribution and prediction accuracy for samples with PGA of 1-3 m/s\({}^{2}\). The sensitivity analysis on the fundamental period is demonstrated in Figure 11a.