###### Abstract  
Deep learning has been applied to structural damage detection and achieved great success in recent years, such as the popular structural damage detection methods based on structural vibration response and convolutional neural networks (CNN). However, due to the limited number of vibration response samples that can be acquired in practice for damage detection, the CNN-based models may not be fully trained; thus, their performance for identifying different damage severity as well as the damage locations may be reduced. To solve this issue, in this paper, we follow the strategy of \"divide-and-conquer\" and propose a two-stage structural damage detection method. Specifically, in the first stage, a 1D-CNN model is constructed to extract the damage features automatically and identify the damage locations. In the second stage, a support vector machine (SVM) model and wavelet packet decomposition technique are combined to further quantify the damage. Experiments are conducted on an eight-level steel frame structure, and the accuracy of the experimental results is greater than 99%, which demonstrates the superiority of the proposed method compared to the state-of-the-art approaches. structural damage detection; convolutional neural networks; support vector machine;  
machine learning algorithms have been applied to this field, with support vector machine (SVM) being one of the most classic. [PERSON] et al. [11] proposed a method based on vibration statistical indicators and SVM, employing variance, regression coefficients, and cross-correlation function amplitude as feature vectors for SVM, and achieved good results on an eight-level steel frame structure. An enhanced Hilbert-Huang transform (HHT) and SVM-based structural damage detection technique is proposed by [PERSON] et al. [12]. The structural vibration function's Hilbert spectral energy is obtained by decomposing the vibration signal using improved empirical modal decomposition. Then, the structural damage feature vector is constructed, and the damage location and severity are detected using SVM. The method's efficacy is evaluated on an experimental model of an offshore platform. However, feature extractors based on traditional machine learning techniques require significant domain expertise, whereas deep-learning-based methods can automatically extract and select features from data, thus avoiding the need for the manual design of feature extraction methods and reducing workload [13]. In the field of structural health monitoring based on vibration response, one-dimensional convolutional neural networks (1D-CNN) are very popular deep learning methods; 1D-CNN takes time series directly as input and conducts one-dimensional convolution on the time axis. [PERSON] et al. [14] used 1D-CNN to detect damage in a steel beam numerical model. According to their results, 1D-CNN based on acceleration signals could detect 94.1% of the damage. [PERSON] et al. [15] used the time-frequency graph of the damaged signal after HHT transform and the marginal spectrum of the signal as the input of CNN and optimized the parameters of CNN with particle swarm optimization (PSO) to improve the model performance. [PERSON] et al. [16] proposed an improved denoising auto-encoder-based neural network and optimized it by using gray relation analysis. It is capable of automatically extracting high-level features from the original signal by multi-layer extraction and can achieve high accuracy in noisy environments. In addition, numerous works have demonstrated that 1D-CNN outperforms traditional machine learning methods in structure damage detection. In applications of SHM, different users have different requirements. For example, a house owner only needs to know the location of damage to his house to contact the maintenance staff, while the maintenance staff needs to know more details about the damage, including the location and severity of the damage to make better repairs. Therefore, it makes sense to design a multi-stage structural damage detection method to save computational costs.