# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 1 Introduction  
Structural damage identification is an essential approach to prevent a sudden collapse of structures and avoid casualties and heavy economic losses [1; 2; 3]. A series of damage detection methods have been proposed [4; 5; 6; 7; 8], and health monitoring systems have been developed and installed in a number of large-scale engineering structures. It has been found that feature extraction is very crucial in a structural health monitoring system (SHM) [9; 10]. Usually, we can use the change of features extracted from the pre- and post-damage of structure to diagnose structural damage. However, conventional feature extraction methods are powerless for extracting from non-linear signals. In recent years, several new theories, such as wavelet transform [11] and fractal dimension (FD) [12], have been used to identify features of damage. In particular, FD analysis has attracted more and more attention in the field of structural damage detection. FD is powerful to describe the non-linear behavior of vibration signals quantitatively, and thus it is suitable for the field of fault diagnosis and damage detection [12; 13; 14; 15; 16]. Previous studies have found that if the structure has damage, the parameters that determine the state of structure will change, and the FD will also change. Thus, FD can be used as the feature, which can reflect structural damage and state, and diagnose the structural damageaccording to the change of FD. This is the theoretical basis of applying FD for structural damage detection. It has also been found that fractal method does not depend on the mathematical system model, and also can comprehensively extract the state information of system. In addition, FD is simple and intuitive. Thus, FD is an important and ideal index for structural damage identification. Recently, FD has become a research hotspot for SHM, in view of its flexibility and extensive applicability. Although fractal geometry has been applied to structural damage detection and diagnosis widely, which has wonderful adaptability and agility, and has a nice application foreground, it also has some problems. For example, considering that structural damage detection belongs to dynamic indirect problem, it is difficult for high-nonlinear complex cases to detect damage by use of a dynamics mathematic model. Furthermore, in real practice, a single damage index is generally impossible to reflect all types of damage for real structures, and it may also have some uncertainties of modeling and observation errors in practical measurement [13; 14]. Although statistical theory and approaches can reduce the effect of observation error and uncertain factors, it is essential and urgent to develop a comprehensive damage detection method to solve the above-mentioned problems. Since neural network (NN) possess powerful abilities, such as nonlinear processing, self-organizing, self-learning and self-adaption, it had obtained good results in the structural damage detection by integrating NN with fractal theory [17; 18; 19; 20]. Most of these existing studies focused on the application of FD in extracting features, fault diagnosis and damage identification. However, it is still a concerning issue how to make full use of the large volume of nonlinear characteristic data and integrate FD for damage detection and state assessment [17; 18; 19]. In recent years, as a complex machine learning algorithm, Deep Learning(DL) [17; 21; 22] has been widely applied in the field of artificial intelligence; especially Convolutional Neural Network (CNN) has been of wide concern and application, such as in image and sound recognition, machine vision and data mining. However, as a machine learning algorithm emerging in recent years, the application of CNN in engineering structural damage diagnosis is still in the initial stage. Network training is time-consuming and also has a lot of work that needs to be further carried out, such as the processing of the variety of structural damage, massive data and the application in practical engineering.