# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
###### Abstract  
In order to improve the identification accuracy of damage detection and evaluation based on the vibration response, this paper presents a structural damage identification method based on the fractal dimension, data fusion and a revised counter-propagation network (RCPN). Firstly, the fractal dimensions of the original signal response are extracted through data preprocessing. Secondly, the first-time fusion of data (i.e., the feature-level fusion) is carried out, after which these data are used as the input for the RCPN, to identify and decide the initial damage. Finally, the second-time data fusion (i.e., based on the decision results of the feature-level fusion) is carried out, leading to decision-level fusion. In order to verify the validity of the proposed method, a four-storey benchmark structure of ASCE is used for damage identification and comparison, using a single RCPN decision and the data fusion damage identification method, respectively. The results show that the proposed method is more accurate and reliable than the results of single RCPN decision and feature-level fusion decision, and has good noise resistance and robustness. structural damage identification; fractal; revised counter-propagation neural network; data fusion +
Footnote †: journal: Medical Imaging and Biomedicine  
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Footnote 1: School of Civil Engineering, Liaoning Petrochemical University, Fushun 113001, China; [EMAIL_ADDRESS]  
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Footnote 1: School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China  
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Footnote 1: School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China  
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Footnote 1: School of Civil Engineering, Liaoning Petrochemical University, Fushun 113001, China; [EMAIL_ADDRESS]  
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Footnote 1: School of Civil Engineering, Shenyang Jianzhu University, Shenyang 110168, China  
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A real and pressing issue is that a civil engineering structure has the characteristics of complexity and non-linearity as a system, yet it is difficult to describe these characteristics accurately [16]. However, these characteristics can be described by fractal theory in statistical analysis to some extent. As we know, when the structure is damaged, some of its characteristic parameters will change accordingly. Here, it is worth noting that the fractal dimension extracted from the vibration signal will also change accordingly. This is the theoretical basis for the application of fractal theory to structural damage identification. The fractal method does not require mathematical model comparing some other methods, can extract structural information as a whole, and the solution of the fractal dimension (FD) is simple and intuitive. Based on the above-mentioned advantages, in recent years, the fractal dimension has become a hot issue in the field of structural health monitoring [17; 18; 19]. With the rapid development of sensor technology and computing power, many intelligent algorithms, such as Bayesian methods [19], genetic algorithms (GAs) [20; 21], K-nearest neighbor (kNN) [22; 23], support vector machine (svm) [24; 25], and artificial neural networks (ANNs) [26; 27; 28; 29; 30], had been used in structural damage detection. In recent years, benefiting from the rapid development of artificial intelligence theory and computer technology, the data-driven structural damage identification method has become a hot issue, while the concept of deep learning has shown a vital role in engineering applications [30; 31]. [PERSON] et al. [32] proposed a windowed one-dimensional convolutional neural network (CNN) for multiclass damage identification. [PERSON] et al. [33] proposed a method for structural damage identification under non-stationary excitation based on threshold free combined recursive distance matrix and multi-label CNN. [PERSON] et al. [34] used accelerometer data combined with deep CNN to identify structural damage.