## 4 Discussion  
The health of buildings is significant for the safety of human life. Damage detection by structural damage response is a relatively popular method in SHM. With the development of machine learning and deep learning, more and more methods are being applied to this field. Machine learning requires fewer training samples and low computational cost but requires manually designed complex feature extraction methods. Deep learning can automatically extract feature, but has a high computational cost and requires more training samples. We propose a two-stage structural damage response method based on 1D-CNN and SVM by analyzing the vibration damage response of an eight-layer steel framework. The 1D-CNN is used in the first stage to detect the damage location, and the SVM is used in the second stage to detect the damage severity, which fully combines the advantages of 1D-CNN and SVM to achieve better damage detection with less computational cost and simpler feature extraction methods and is meaningful for the application of SHM. In this paper, we compare our proposed method with several other methods. From Table 3, it can be found that, in general, the two-stage damage detection methods work better than the single-stage damage detection method. Among the single-stage damage detection methods, 1D-CNN is much better than SVM. On the one hand, it is because 1D-CNN has an extremely strong feature extraction ability, and on the other hand, it is because we have not designed a feature extraction method for SVM that combines expertise, but directly uses wavelet packet decomposition as the feature extraction method, resulting in a large difference between the two effects. In [PERSON] et al. [11], the authors achieved extremely high accuracy by using variance, regression coefficients, and cross-correlation function amplitude as features, followed by SVM for classification. This suggests that better results can be achieved with SVM if the designed feature extraction method is good enough, but this requires strong expertise, and the designed feature extraction method may not be applicable to other structures. In contrast, 1D-CNN only uses a simpler structure, which is good at automatically extracting features and achieving a high accuracy rate and is applicable to a variety of structures. Among the two-stage methods, the proposed method in this paper is slightly better than 1D-CNN and 1D-CNN. It can be seen from Table 4 that the two methods are close to each other in detecting the damage location because the first stage of both methods is same. The main difference is in the second stage of damage severity detection. To further probe the reason, we compared the confusion matrix of 1D-CNN with 1D-CNN and SVM. From Figure 5(a), we can find that the samples incorrectly identified by 1D-CNN were all samples with the same damage location and different damage severity. On the one hand, this is because the change of damage response caused by the change of damage severity was small, and on the other hand, it is because the number of samples with different damage  
Figure 7: Visualization results of the test samples before and after classification. severity at the same damage location was small, which was not enough for 1D-CNN to learn sufficient information. While SVM uses the wavelet packet decomposition of the damage response as the feature vector, which is a classification problem with high-dimensional small samples, and is well suited to be solved by using SVM, 1D-CNN and SVM have indeed achieved better results. There are not many studies on multi-level damage detection. [PERSON] et al. [32] proposed a multilevel damage classification method based on Lamb wave and transfer learning. They divided the damage detection into three levels, which detected the existence, location, and size of damage. In future work, we will consider adding a stage to detect the existence of damage. Their method used 1D-CNN for damage detection at all three levels, and although their method achieved high accuracy, it also takes more time to train.