## 3 Experimental Results
### Equipment and Procedures for Data Collection  
In order to demonstrate the performances of load identification under real conditions and collect data for network training, a self-build GARTEUR aircraft model [40] is set up. The self-build GARTEUR model is similar with that in [25], which comprises six 2024-T3 aluminum beams with rectangular cross-sections, namely the fuselage, wings, vertical tail, horizontal tail and wingtip ballast plates. The elastic modulus, the Poisson's ratio and the density of the model is given in Table 2. As shown in Figure 3, the model has a wingspan of 2 m, a fuselage length of 1.5 m, and a total weight of 43.34 kg. Throughout all experiments, the deformation of the self-build GARTEUR model remains within the elastic deformation range, making the system behave as an elastic system that adheres to [PERSON]'s law, which is also similar to that in [25]. A modal experiment is conducted using PolyMAX [41]. For the GARTEUR model, the modal data is between 6.062 Hz (mode 1) to 140.024 Hz (mode 14). Figure 3: The equipment used for data collections. A self-build GARTEUR model is utilized for the structures. The force sensor that attached on the exciter would provide the ground truth values for the loads, while the accelerometers on attached on the GARTEUR model would measure the response signals. All signals are synchronized and captured by the LMS SCADAS III from Siemens (Leuven, Belgium). In order to collect the synchronized \"load-response\" data on the above-mentioned GARTEUR model, the vibration exciter, force sensor and the accelerometers are attached on the GARTEUR model. To evaluate the proposed approach under different response position configurations, in the experiments, two configurations of the accelerometers position are designed, namely the symmetrical distribution and the asymmetrical distribution. Figure 4 shows the sketch of the positions of the exciter and the accelerometers under the two configurations. For the symmetrical distribution, the vibration exciter with an attached force sensor is placed at the triangle point on Figure 4a. In the meanwhile, the acceleration response data are captured at six different positions, which are shown as the circle points on Figure 4a. As for the asymmetrical case, the exciter is placed at a different position, which is also shown as a triangle point on Figure 4b. To verify the identification performance under different numbers of measurement points, in this scenario, four accelerometers are used, which are placed on the circle points on Figure 4b. The Siemens LMS SCADAS III, which is from the Siemens LMS, Leuven, Belgium, is utilized for the temporal synchronization of the load and response signals. The sampling frequency is set to 2048 Hz, which is more than 10 times of the frequency of the 14 th mode of the structure. For four sinusoidal loads, 4 s of response signals are captured. For random loads on symmetrically distributed sensors, the duration of the signals is 16 s, while that of the random loads on asymmetrically distributed sensors is 8 s. For each signal, a proportion of 70% data is used for NN training, and 30% of the data are used for quantitative evaluation. It should be noted that the proposed method is a supervised learning-based method, so the training data are important for the algorithm performance. When applying the approach to unknown structures without the training data prior, there could be a domain gap that would affect the algorithm's performance. Such problems can be further addressed using unsupervised learning techniques like auto-encoder [42] or generative adversarial network [43]. The two works in [42,43] are both based on vibration data. The use of unsupervised learning techniques would reduce the dependence on training data for the algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elastic Modulus** & **Poisson’s Ratio** & **Density** \\ \hline
73 GPa & 0.33 & 2780 kg/m3 \\ \hline \hline \end{tabular}
\end{table}