# An Intelligent Warning Method for Diagnosing Underwater Structural Damage
## 3 Experiment and Result Analysis
### Training Network Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Micro-Honeycombs**} & \multirow{2}{*}{**Micro-voids**} & \multirow{2}{*}{**Micro-depressions Micro-cracks**} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Clustering** \\ **Result** \\ \end{tabular} }} \\
**Training Steps** & & & & & \\ \hline
10 & 55 & 37 & 37 & 55 & 50\% \\
50 & 43 & 37 & 37 & 55 & 75\% \\
100 & 43 & 1 & 37 & 37 & 75\% \\
200 & 49 & 1 & 16 & 64 & 100\% \\
500 & 49 & 1 & 16 & 64 & 100\% \\
1000 & 49 & 1 & 16 & 64 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results for different steps. Figure 12: Winning neuron topology. The long hexagon of the connected neurons and the depth of the color represents the distance between the neurons. In Figure 13, the connected region of the dark hexagon divides the whole neuron into four sub-regions, corresponding to different damage types. The neurons of the light-colored hexagons connected in the sub-area have the same type of damage. Neurons 53 and 60 are surrounded by dark hexagons, corresponding to unknown damage types. Neuron 28 is far from the four types of damage and is classified as micro-voids defects with close proximity. Alternatively, it can be classified as other unknown defects different from 53 and 60 or 28. Therefore, the corresponding situation of all neuronal damage is obtained. In order to determine the efficiency of the algorithm, test results were analyzed under different test conditions. Table 5 shows the classification of damage. Table 5 describes the types of damage that the damage model can identify and the recognition rate for identifying the four types of damage. The data in Table 5 show that the proposed algorithm can not only detect the expected type of defect but also diagnose other unknown defects in the underwater concrete. It can be inferred that when the concrete structure is healthy, Figure 11 will consist of hexagons of a uniform color. Its classification accuracy, \(R\), can be calculated by the formula: \(R=\frac{N_{all}-N_{all}}{N_{all}}\times 100\%\). While there are only four kinds of damage in the structure, each damage type can be accurately identified. The algorithm can also accurately extract unknown defects. Therefore, the algorithm can obtain good detection results in the case of correct detection, false positive, and false  
Figure 13: Distance between the neurons. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Damage Type** & **Sample Classification Number** & **Classification Accuracy** \\ \hline Micro-honeycombs & 36, 41, 42, 43, 44, 49, 50, 51, 52, 57, 58, 59 & 80\% \\  & 1, 2, 3, 4, 9, 10, 17, 18, 19, 20, 25, 26, 27, & 93.33\% \\  & 33, 34, 35 & 100\% \\ Micro-depressions & 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 21, 22, 23, & 100\% \\  & 24, 29 & 86.66\% \\  & 56, 61, 62, 63, 64 & - \\  & 53, 60 & - \\ Unknown situation & 28 & - \\  & 37 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification of damage. This paper used the research methods of comparative analysis and graphs analysis. We can draw the conclusion that each classification predictive sample can correspond to the damage state. All prediction samples were correctly classified and the overall classification accuracy rate was 100%. It showed that the intelligent early warning method achieved satisfactory results and can be used as an intelligent and accurate diagnosis algorithm for solving underwater structural damage under complex constraints.