# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 4 Experiments and Results Analysis
### Neuron Topology Analysis for RPC Bending Fatigue Damage SOM Network Model  
In order to obtain the damage category information corresponding to each neuron, the clustering results of the network model were analyzed. Obtain the topological structure distance map of the structure damage identification network model, as shown in Figure 9. The small gray squares in the figure represent neurons and the straight lines between them represent straight-line connections between neurons. The distance between neurons is obtained by the Euclidean distance formula. The hexagons connect the neurons, with the color depth representing the distance between neurons. The colors are from dark to light, indicating that the distance between neurons is from far to near. It can be inferred that the neurons with a light color have high similarity, and the difference between them is low; while the neurons with a dark color have low similarity, and the difference between them is large. Based on the analysis of the traditional comparison method, among the 64 input neurons of the damage identification network model, the damage types of 61 neurons correspond to the damage types of the standard samples. For example, for neurons 36, 41, 42, 43, 44, 50, 51, 52, 57, 58 and 59, their damage types may correspond to the winning neuron number 49, which corresponds to the honeycomb damage type. However, neuron 37 is between the hole and sag damage states, and is far away from neurons 53 and 60, corresponding to other unknown damage types. In order to clearly present the damage type corresponding to each neuron, a corresponding relationship table between the damage types and sample classification numbers is constructed as shown in Table 3.  
By analyzing Table 2, it can be seen that the model can obtain the damage classification of almost all neurons, and the recognition accuracy rate is as high as 95.31%. However, \begin{table}
\begin{tabular}{c c} \hline \hline
**Damage Type** & **Sample Classification Number** \\ \hline Honeycomb & 36, 41, 42, 43, 44, 49, 50, 51, 52, 57, 58, 59 \\ Hole & **1**, 2, 3, 4, 9, 10, 11, 17, 18, 19, 20, 25, 26, 27, 33, 34, 35 \\ Sag & 5, 6, 7, 8, 11, 12, 13, 14, 15, **16**, 21, 22, 23, 24, 29 \\ Crack & 30, 31, 32, 38, 39, 40, 45, 46, 47, 48, 54, 55, 56, 61, 62, 63, **64** \\
**Unknown type** & **37**, **53**, **60** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correspondence between damage types and samples. Figure 9: Topological distance graph of neurons. there are still cases where unknown neurons cannot be associated with their type of injury. In order to further improve the recognition accuracy of the network model, the TOP-G algorithm is used to determine the type of unknown neuron damage. The analysis process is shown in Figure 10.  
Figure 10: Determining unknown neuron category based on TOP-G algorithm. First, determine the gray level of the topology map, \(L=161\), and the gray value range is [0, 161], then the gray level of the topology map image is \(g=2\)[8]; Then, at this grayscale, the topological distance color map is converted to a grayscale image; Create a sliding window, mark the polygon as \(L_{1}\)-\(L_{161}\), assign the gray value \(g_{1}\)-\(g_{161}\); Finally, the gray values are sorted from large to small, and the judgment is made according to the sorting result. Neurons 53 and 60 are connected by a neighborhood polygon number 147, with a grayscale value of 205, which can be seen as a class. The gray values of the eight neighborhood polygons are sorted by gray value, and the neighborhood polygon No.124 has the largest gray value, which connects the neuron number 45 and the neuron number 53. Therefore, it is determined that neurons 53 and 60 correspond to crack damage. Neuron 37 corresponds to six neighborhood polygons, and their gray levels are sorted. The gray value of the polygon in the neighborhood of No. 80 is the largest, which is connected to the neuron of No. 24, corresponding to sag damage. Therefore, it was judged that neuron No. 37 corresponds to sag damage.