## 5 Experimental Results and Discussion for Three-Story Building Structure
### Compared with Other Methods
conditions, the Precision is approximately over 0.7 with a high recognition performance, which is shown in Figure 10. It can be seen from Figure 11 that the exception of 40% damaged conditions achieves a low score, and other patterns (such as 10%, 20%, and 30% damaged conditions) obtain a high score using FFT-DCNN algorithm. Figure 10: Precision of the test data using different algorithms. Figure 9: Recall of the test data using different algorithms. In order to reveal the classification of different algorithms under four damaged conditions of a three-story building structure, Figure 12 shows the confusion matrix of the proposed approach and compared algorithms. More specifically, in the FFT-DCNN method, the accuracy of 10% damaged condition and 40% damaged condition keep a high value over 95%. For 20% damaged condition, 37 samples are misclassified as 30% damaged condition, and 11 samples are misclassified as 10% damaged condition. The Precision keeps a high value of over 86%. The Precision is 86.7% for 30% damaged condition. Finally, the total accuracy of test data for FFT-DCNN is 93.29%, which illustrates the excellent ability of the proposed method for recognizing structural damage conditions compared with other methods, including FFT-SVM (90.15%), SVM (85.59%), KNN (67.64%), RF (70.27%), and XGBoost (75.78%). In addition, for the FFT-SVM algorithm, the numbers of classification accuracy in 0-3 damaged conditions are 1003, 239, 468, and 541, respectively, in Figure 12b. The numbers of classification accuracy in 0-3 damaged conditions are 991, 310, 495, and 575, respectively, in Figure 12c. It indicates that accuracy can be improved when the acceleration data are transformed into frequency information. More importantly, the frequency of the three-story frame generates obvious changes compared to methods based on time sequence when the structure suffers different damaged degrees. Our proposed method utilizes frequency information of three-story frame to recognize structural damage with high performance. All experimental algorithms are performed in the same Windows server, and the server is configured as: GPU is GeForce RTX 3080 Ti, RAM is 32 GB, and AMD Ryzen 9 5950X 16-Core Processor. It can be seen from Figures 13 and 14 and Table 6 that the proposed methods cost more time than ML algorithms during training and testing datasets. It is mainly because FFT-CNN continually updates the parameter of the network using a number of datasets. In addition, the training process finishes when the network's loss curve begins to converge, which can cost a large amount of time in Figure 13. With the increase in computing ability, the consuming time of algorithms will be reduced in the future. Thus, the accuracy of structural damage detection should receive the primary attention, which can affect public safety. Figure 11: F\({}_{1}\)-scored of the test data using different algorithms. Figure 12: Confusion matrix between actual conditions and predicted conditions. Moreover, the test time is an important evaluation metric to judge whether the algorithm can be utilized in actual engineering. It can be seen that the proposed method takes a short \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Training Time (s)** & **Test Time (s)** \\ \hline FFT-DCNN & 118.152 & 0.168 \\ FFT-SVM & 1.384 & 1.207 \\ SVM & 3.391 & 2.038 \\ KNN & 0.056 & 0.368 \\ RF & 5.253 & 0.408 \\ XGBoost & 6.754 & 0.079 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of cost time between the proposed method and ML algorithms. Figure 14: Comparison of test time between the proposed method and ML algorithms. Figure 13: Comparison of training time between the proposed method and ML algorithms. time on test datasets, compared with FFT-SVM, SVM, KNN, and RF. Thus, the proposed method can be designed for recognizing structural damage in actual civil engineering.