# Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network
## 4 Discussion and Conclusions  
In this paper, the applicability of CNNs in SDD was investigated. The training samples of the CNN were obtained using FEA; the trained CNN has an ideal detection effect on the testing samples obtained from the numerical model; the detection effectiveness was also validated by the samples obtained from the vibration experiments. The detection accuracy of the proposed method was above 90%. In Section 3.1, the detection of a single damage, double damages, triple damages and mixed damage scenarios was investigated using the CNN trained with the samples obtained from numerical simulations of the steel frame. It was found that the network converges after only 100 to 200 iterations and the training accuracy reaches 94%. In Section 3.2, the vibration signals obtained from vibration experiments of the steel frame were used as the inputs of the CNN to validate the applicability of the CNN trained using FEA data. The detection results were ideal, and the detection accuracy was over 90%. Generally, it is unrealistic to train a CNN through real structural measurements, as it is difficult to obtain adequate damage samples for the engineering structures in service. To simulate structural damages by loosening joint bolts, increasing the structural mass, and other methods in the experiments [27] provides some hints for the creation of structural damage scenarios, but these man-made \"damages\" were time-consuming and laborious and limited to certain types. On the other hand, the FEA can simulate a variety of damage scenarios and generate a large number of training samples. The results shown that these samples  
Figure 15: Acceleration time-history curve. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Amount**} & \multicolumn{8}{c}{**Prediction Damage Location**} \\ \cline{2-11}  & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Total** & **\%** \\ \hline \multirow{5}{*}{Actual damage} & 0 & 742 & 4 & 33 & 0 & 12 & 0 & 0 & 0 & 791 & 93.8 \\  & 1 & 41 & 687 & 9 & 0 & 0 & 54 & 0 & 0 & 791 & 86.9 \\  & 2 & 56 & 17 & 691 & 7 & 20 & 0 & 0 & 0 & 791 & 87.3 \\ \cline{1-1}  & 3 & 15 & 0 & 19 & 702 & 45 & 10 & 0 & 0 & 791 & 88.7 \\ \cline{1-1}  & 4 & 27 & 0 & 7 & 78 & 679 & 0 & 0 & 0 & 791 & 85.8 \\ \cline{1-1}  & 5 & 9 & 69 & 2 & 8 & 0 & 693 & 10 & 0 & 791 & 87.6 \\ \cline{1-1}  & 6 & 0 & 12 & 0 & 0 & 0 & 38 & 741 & 0 & 791 & 93.6 \\ \cline{1-1}  & 7 & 0 & 0 & 0 & 0 & 0 & 5 & 21 & 765 & 791 & 96.7 \\ \cline{1-1}  & All & 890 & 789 & 761 & 795 & 756 & 800 & 772 & 765 & 6328 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test results of damage classification in dataset E.  
can predict the actual structural damages, which makes up for the disadvantages of the experimental method in generating damage scenarios. In this paper, data normalization was used to make the data from numerical simulations and vibration experiments comparable. After dimensionless normalization, these two kinds of data were distributed in the same range without changing the inherent damage features of the original signals. From the above discussions, the following conclusions can be drawn:  
1. The CNN can accurately map the vibration signals (acceleration) to the structural damage state; it has an ideal detection effect for a single damage and multiple damages. 2. The effective combination of numerical simulations and vibration experiments makes the CNN-based damage detection method more applicable in engineering practice. The use of FEA can generate a large number of CNN training samples. There are still some shortcomings in this paper: 1. The structural model is a steel frame used in a laboratory and the damage scenarios are man-made; 2. It is noticed that the results of this method are based on numerical simulations and experimental measurements.