## 5 Experimental Results and Discussion for Three-Story Building Structure
### Compared with Other Methods  
To verify the advantages of our proposed method, classical ML algorithms including support vector machine (SVM) [34], FFT-SVM, random forest (RF) [35], K-nearest neighbor (KNN) [36], and eXtreme gradient boosting (XGBoost) [37] are selected to evaluate structural damage degree and improved accuracy in structural damage detection. The accuracy of KNN, RF, and XGBoost is 67.64%, 70.24%, and 75.78%, respectively, representing a low ability for recognizing structural damage detection. For these algorithms, such as SVM, KNN, RF, and XGBoost, the time-sequence data of acceleration are input datasets. For FFT-DCNN and FFT-SVM algorithms, the time sequence is transformed into frequency information by the FFT method, and then frequency information is fed into DCNN or SVM algorithms. The relative setting of these algorithms are as follows:  
SVM: A Gaussian RBF function is used as the SVR kernel function, and a grid search is used to determine the penalty parameters \(c\) and kernel parameters \(g\). The search range \(c\) and \(g\) are [\(10^{-4}\), \(10^{4}\)] and [\(2^{-4}\), \(2^{4}\)] by the grid searching method, respectively. Tolerance for stopping criterion is set to 1e-3, and it is enough to satisfy the error criterion. KNN: The number of neighbors is set to 5. The value of function weights is set to uniform, representing that all points in each neighborhood are weighted equally. The power parameters. The Minkowski metric is set to 1, and it is equivalent to using Manhattan distance. The leaf size that affects the construction and query speed is set to 30. RF: The number of trees in the forest is 100, and the maximum depth is set 3. The min samples split is set to 2, which denotes the minimum number of samples required to split an internal node. Min samples leaf represents that training samples in each of the left and right branches, and the values of Min samples leaf are set to 5. The value of Max features is set to 'auto', representing that the number of features to be considered when looking for the best split. XGBoost: The maximum depth of a tree is set to 6, and the minimum child weight depth of a tree is 1. To making the update step more conservative, the max delta step is set to 0.1. L2 regularization term on weights is set 1, which can reduce the overfitting problem. The learning rate is set to 0.3. The fraction columns of random samples for each tree are set to 1. Table 5 shows the comparison of damage detection ability between the algorithms mentioned above and the proposed method. It can be seen from Table 5 that the proposed method has higher accuracy in 93.38% than classical ML methods. In addition, the FFT-SVM improves accuracy by 4.56% than SVM when FFT is added to SVM. It shows that the preprocessing method, for example, FFT method, can reduce the effect of fault or noisy data and improved accuracy in structural damage detection. To observing the classification result of every defined pattern for test data based on different algorithms, evaluation criteria including Precision, Recall, and F\({}_{1}\)-score are utilized in Figures 9-11. Recall represents the number of positive class predictions made out of all positive examples in the dataset. It can be seen from Figure 9 that the obtained Recall scores of 10%, 20%, and 30% damaged conditions are approximately over 0.8 using the FFT-DCNN algorithm, with the exception of 40% damaged conditions, for which the scores fall approximately 0.5. In addition, using the precision evaluating on test data, similar results are achieved for FFT-DCNN algorithm. For 10%, 20%, 30%, and 40% damaged \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline FFT-DCNN & 93.29 \\ FFT-SVM & 90.15 \\ SVM & 85.59 \\ KNN & 67.64 \\ RF & 70.27 \\ XGBoost & 75.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison result between the proposed method and ML algorithms.