# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 4 Discussions and Conclusions  
This paper uses the CNN method to locate the damaged rod of a complex steel truss based on the first order mode shapes and mode curvature differences. It shows the prediction accuracies for a variety of damage scenarios and reveals some internal working mechanisms of the network. Sections 3.1 and 3.2 show that the CNN method based on the first order mode shape has a higher accuracy when locating the single damaged rod in all damage scenarios, even if the damage degree is only 4%, the prediction accuracies of some rods reach 100%, while the method based on the mode curvature difference can achieve the same prediction accuracy in locating the rod with a high damage degree, but the prediction accuracy is poor when locating the rod with a low damage degree. Section 3.3 shows the features of the two kinds of input data, compared with the first order mode shape (unprocessed data), the curvature difference (processed data) has obvious numerical variations at the damage location, but it does not have a better accuracy as expected, the central difference method may result in the loss of some information, while the original data like the first order mode shape may contain more damage information and the damage detection accuracy based on it is higher. Furthermore, to extract the inconspicuous features from the data, the CNN needs a deep learning architecture to get more powerful features extraction ability, hence, the CNN, using the first order mode shape as the inputs, needs a deep learning architecture to achieve higher accuracy; while the CNN, using the mode curvature difference as the inputs, only needs a shallow learning architecture to achieve its maximum prediction accuracy. However, even for the method based on the first order mode shape, there are always some rods with poor prediction accuracy. The reason may be that, only the vertical displacements of the truss are used as the inputs, they may not contain enough damage information. By observing the features extracted from the mode shapes, it can be seen that the CNN can obtain the difference between the sample to be classified and the weighted average of the training samples in shallow layers, and then amplify the difference in the subsequent convolutional layer. Thus, it can be inferred that the partial work of the CNN to locate a damaged rod is to render the input data remarkable at the damage segment through updating the internal weights of the network,  
Figure 15: The features of various damage scenarios extracted from the first (top row) and the third (bottom row) layers. it appears like the result of calculating the difference between the sample to be classified and the weighted average of the training samples and then map it via a quadratic function. After the calculation of multiple convolutional layers, the mode shapes will have a distinguishable peak feature in the damaged segment like the curvature differences. This kind of data processing is very similar to the existing damage indexes based on the mode curvature differences, mode shape coefficient [21] and mode shape difference function [22], which were used traditionally to overcome the disadvantages of low-order mode shapes. As the Figure 9 shows, there are certainly many other features extracted by the CNN, but their specific meanings are to be explored. The work above can only identify the damage existing in which segment; however, the CNN should be able to locate the damage existing in which rod. Besides, this paper ignored the function of the large size convolution kernel in the feature extractions; therefore, what the CNN does inside still needs a further exploration. A neural network is a method simulating the biological neuron through mathematical algorithm model. Through the previous discussions, it can be found that it does something very similar to the human mind in the process of damage locating.