# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 3 Experiment and Result Analysis
### Training Network Model  
The network model was constructed. The parameters of the network are shown in Table 3.  
The created network topology is a two-layer network, and the competition layer consists of eight vectors. The initial weight was set to 0.125. The neighborhood shape of each network node was set to a hexagon, and the neighborhood radius was set to 3. The competition layer was \(8\times 8=64\) neuron nodes. The training steps were set to 10, 50, 100, 200, 500, and 1000. Table 4 shows the classification effects under different training steps; the numbers in the table represent the classification numbers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Damage Type** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** \\ \hline Micro-honeycombs & 0.45219 & 1.23347 & 0.341173 & 0.659015 & 1.487459 & 16074.99305 0.6085123 & 2113537.15 \\ Micro-depressions & 0.735626 & 0.844082 & 0.245272 & 0.986124 & 1.270514 & 16039.67163 \(-\)0.23044 & 2113536.83 \\ Micro-voids & 0.966008 & 0.645785 & 0.155829 & 3.719458 & 1.034531 & 16113.92133 0.185468 & 2113536.14 \\ Micro-cracks & 0.561931 & 0.114353 & 0.047756 & \(-\)1.768837 & 1.1816 & 16103.60313 \(-\)0.487734 & 2113536.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Normal sample. Figure 11: Parameters with inferior screening results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & \multicolumn{8}{c}{**Value**} \\ \hline
**Input Layer** & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) \\ \cline{2-9}
**Node** & **P1** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** \\ \hline Weight & \multicolumn{8}{c}{0.125} \\ Neighborhood & \multicolumn{8}{c}{Hexagon, R = 3} \\ shape & \multicolumn{8}{c}{64} \\ Neurons & \multicolumn{8}{c}{Training steps} & \multicolumn{8}{c}{10, 50, 100, 200, 500,1000} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Network parameters. Table 4 shows that when the number of training steps equals 10, a micro-damage diagnosis model is initially established, and the micro-damage is roughly divided into two groups. With the increase of the number of training steps, when the number of training steps is 50 and 100, respectively, the diagnostic accuracy is further improved. The micro-injury is divided into three groups. When the number of training steps reaches 200, the micro-injury is effectively distinguished. However, when the number of training steps reaches 500 and 1000, the micro-injury classification results are the same as 200 steps, which has no practical significance. Therefore, 200 steps 200 were chosen as the optimal value for the micro-damage diagnosis model. To verify the reliability of parameter optimization, the topological structure of the winning neurons in Figure 12 was analyzed. The gray hexagon with the number in the figure corresponds to the classification label of the winning neurons. The unique neighborhood characteristics of SOM and the spatial topology of the input neurons are preserved. Figure 12 shows that the winning neuron numbers are 1, 16, 49, and 64, occupying four different topological spatial locations. The classification result of the topological result graph is completely consistent with the clustering result obtained under the optimal parameters of the network. By analyzing the topology, the network parameters were optimized to build a better SOM network model. It is proven that the setting of various parameters of the network can meet the working requirements of an underwater structure diagnosis system and effectively distinguish the types of micro-damage. Although the topological position of the winning neurons was obtained, it cannot determine the type of damage for each neuron. Therefore, the neuron distance map was obtained as shown in Figure 13. Small square hexagons represent neurons and straight lines represent linear connections between neurons. The distance between neurons can be derived from the Euclidean distance formula.