## 1 Introduction
Numerical simulations and shake table experiments on three-story shear-type buildings showed that their method was effective in assessing structural damage and identifying the damage severity even under non-stationary non-Gaussian excitation conditions. Based on the previous studies, [PERSON] et al. [17; 18] applied the statistical moment-based method to the damage identification study of beam-type structures. They improved the existing statistical moment theory and proposed a damage index based on fourth-order statistical moments of the strain response. The results of numerical simulations and simply supported beam experiments [19] proved the effectiveness and practicality of their method. [PERSON] et al. [20] pointed out that the identification method based on statistical characteristics of the dynamic response can simplify the calculation. They used the cross-correlation function amplitude vector (CorV) of the measured dynamic response to evaluate the structural damage. The reliability of the method was demonstrated by experiments on the fastener loss of an aircraft panel model. The above studies show that the method based on the statistical characteristics of the dynamic response possesses great potential in revealing damage and is a reliable method for dynamic damage identification. Most of the dynamic-based damage identification methods tend to locate the damage by comparing the baseline data from intact structures. In actual engineering structures, some structures that have been in service for a long time, such as complete baseline data, are difficult or even impossible to obtain. As a result, more and more scholars have developed baseline-free damage identification. Baseline-free dynamic damage identification methods are mainly divided into weak baseline-free methods and strong baseline-free methods. The weak baseline-free method mainly adopts the curve (surface) fitting technique to obtain the baseline data (mode shapes and their derivatives, static deflection, etc. ), and it is possible to compare the baseline data to locate the damage. [PERSON] et al. [21] carried out the baseline-free structural damage identification in composite plates using the two-dimensional gapped smoothing method. [PERSON] et al. [22] used multi-segment function-fitting calculations to establish the deflection influence line (DIL) of a bridge without damage, and the damage location was detected by comparing the DIL before and after the damage. The strong baseline-free method, on the other hand, directly uses technical means such as wavelet transform, fractal dimension, and second-order central difference to process the damage data and characterize the occurrence of damage according to the abrupt peaks [23; 24; 25]. The above baseline-free methods achieved satisfactory results; however, there was a relatively complex data processing procedure when applying these methods. For example, when extracting mode shapes, a complex modal processing procedure must be undergone, which undoubtedly increases the computational cost. Moreover, known excitation information is usually required to construct the frequency response function of the structure. In addition, some valid information is lost when converting the time domain response to the frequency domain. In order to improve the computational efficiency and accuracy of damage identification, this paper proposes an identification method of structural multiple damages based on the CWT-SSSM feature by combining the statistical characteristics of the dynamic response and WT. The proposed method first selects the strain response, which is more sensitive to local damage for analysis, then utilizes CWT to highlight the damage singularity information in each order SSSM curve, and finally combines data fusion techniques and the three-sigma rule to construct the damage index of this paper. The proposed method is computationally simple, without the need for known excitation and baseline information, it enables the rapid localization of structural damage using only the output response.