## 4 Experimental Verification
### Damage Detection Results  
The experimental data was used to verify the trained neural network. This study utilized stiffness reductions to generate the training data, the different stiffness reduction cases were not completely consistent with practical situations. Therefore, the case involving a residual stiffness of 70% simulated a healthy condition, and the case involving a residual stiffness of 35% simulated a damage condition. Overall, the system provided conservative estimates of HI values. Because the experimental data only had healthy condition and destruction condition, an evaluated HI of \(>\)0.6 was considered to indicate a healthy condition, and an evaluated HI of \(<\)0.4 was considered to indicate a damage condition. The experimental results obtained for the four damage cases, namely single-floor, two-floor, three-floor, and multistory damage, are described as follows. * Damage on the second floor: The evaluated HI for the second floor was clearly lower than those for the other floors (Figure 18). Similar to the numerical simulation, larger vibration was observed near the second floor. The evaluated HI on the damaged second floor was 0.257, which was less than the target value (0.4). Therefore, the system successfully detected the damaged floor and identified the other floors to be in a healthy condition. (Table 14)
* Damage on the fourth floor: The damaged floor could not be clearly observed (Table 15). For the healthy condition, the HI values obtained for the third, sixth, and seventh floors appeared to be close to the target value. Although Errors were observed between the evaluated HI values for the first, second, and fifth floors and the target value, these evaluated values were higher than 0.6; therefore, all healthy floors were identified successfully. (Figure 22) The evaluated HI value for the fourth floor was higher than 0.4, indicating that the proposed system failed to detect the damage, but the HI value for the fourth floor was the lowest among the evaluated values. Figure 17: (**a**) The training state; (**b**) Regression distribution of the training, test. * Damage on the fifth and sixth floors: The evaluated HI values for the damaged floors approached the target values (Figure 19 and Table 16). For the healthy condition, the first floor to the fourth floor were identified to be healthy, but the seventh floor was not. This can be attributed to the calculation of RCMCSE; because of this calculation, the damage on the sixth floor may have affected the evaluated HI of the seventh floor. * Damage from the first floor to the third floor: In this case of multistory (three floors) damage, the system demonstrated efficient recognition ability for healthy conditions. The evaluated HI values for the fourth-seventh floors were higher than the target value of 0.6 (Figure 20 and Table 17). The second and third floors were identified to be damaged. The evaluated HI value for the third floor was higher than 0.4, indicating that the proposed system failed to detect damage on this floor. * Damage from the fourth floor to the seventh floor: The system successfully detected the healthy and damaged conditions (Figure 21 and Table 18). Although errors were observed for the values obtained for the third and fourth floors, these values approached the target value. Figure 19: Health index of damage on the fifth floor and the sixth floor. Figure 20: Health index of damage from the first floor to the third floor. Figure 19: Health index of damage on the fifth floor and the sixth floor. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Damaged Floor** & **Health Index** & **Evaluated Health Index** & **Error (\%)** \\ \hline
**1F** & & 1 & 1 & 0\% \\ \hline
**2F** & & 1 & 1 & 0\% \\ \hline
**3F** & & 1 & 1 & 0\% \\ \hline
**4F** & & 1 & 1 & 0\% \\ \hline
**5F** & ✓ & 0 & 0 & 0\% \\ \hline
**6F** & ✓ & 0 & 0 & 0\% \\ \hline
**7F** & & 1 & 0.467 & 53.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Diagnosis result of damage on the fifth floor and the sixth floor.