## 1 Introduction  
Structural damage identification has been an active research focus that has been receiving increased attention over the years in the civil engineering field. Structural accidents have once again demonstrated the necessity of developing valid techniques for early-stage structural damage identification [1; 2]. In general, structural damage identification can be divided into four progressive levels: judgment of damage occurrence, determination of damage location, discrimination of damage severity, and assessment of remaining structural life [34]. Typical damage identification methods found in the literature are usually developed by processing structural dynamic responses. Such methods can be largely categorized into two groups: vibration-based damage detection methods [5; 6; 7] and wave-propagation-based damage inspection methods [8; 9; 10]. An active branch of vibration-based damage identification is damage pattern recognition based on artificial intelligence methods [11; 12; 13; 14; 15]. The most popular artificial intelligence method used in structural damage diagnosis is artificial neural networks (ANNs), providing the advantages of self-adaptiveness, generalization, and real-time processing [16; 17; 18]. Representative studies of ANN-based damage pattern recognition are described as follows. [PERSON] et al. [19; 20] applied ANNs to evaluate the location and severity of damage in monotonic structures. [PERSON] et al. [21] proposed an ANN-based method using measured modal properties to detect damage in steel frames, testifying to the effectiveness of discriminating positions and degrees of multiple instances of damage. [PERSON] et al. [22] used ANNs to interrogate damage in the side shells of a ship structure, illustrating the capacity of ANNs to discover such damage. [PERSON] et al. [23] proposed a hybrid neuro-genetic algorithm to automate the design of a neural network for different types of structures. The neural network was trained considering frequency and strain as the input parameters, location and the amount of damage as the output parameters. Its performance was demonstrated using two test problems and the results were quite encouraging. [PERSON] and [PERSON] [24] summarized the existing studies of using measured frequent response functions (FRFs) as inputs to ANNs for structural damage identification, with the conclusion that FRFs held fair advantages over modal parameters in damage characterization. In recent years, the wavelet signal processing technique, referring to the wavelet transform (WT) [25; 26; 27; 28] and the wavelet packet transform (WPT) [29; 30; 31; 32; 33; 34; 35], has been incorporated into ANN-based damage pattern recognition, and that technique has become an active research focus of vibration-based structural damage identification. In this aspect, representative studies are as follows. [PERSON] et al. [36] developed a method integrating ANNs and WT to locate and quantify damage in polyvinyl chloride sandwich plates, with satisfactory results obtained. [PERSON] et al. [37] integrated a wavelet real-time filtering algorithm, adaptive neuro-fuzzy inference system (ANFIS), and an interval modeling technique to process structural response signals and excitation data. The method they proposed could rapidly identify unknown damage and small damage. [PERSON] et al. [38] combined WPT and ANNs to establish a two-step damage detection method that could locate and quantify damage with great robustness. [PERSON] et al. [39] proposed a synergetic approach of damage detection by linking ANNs and WT, in which the wavelet energy was used as a damage feature to discriminate different damage patterns. [PERSON] et al. [40] used probabilistic neural networks aided by the WPT to detect structural damage under ambient vibration. [PERSON] et al. [41] extracted features from the moments of the probability density functions of the wavelet coefficients obtained through WPT, and then developed a classification algorithm on the basis of a probabilistic neural network. [PERSON] et al.